Find benchmark fit, not benchmark fame.

A-OKVQA (Augmented OK-VQA) is a benchmark designed to evaluate commonsense reasoning and external world knowledge in visual question answering. It extends beyond basic VQA tasks that rely solely on image content, requiring models to leverage a broad spectru…

AA-LCR (Artificial Analysis Long Context Retrieval) is a benchmark for evaluating long-context retrieval and reasoning capabilities of language models. It requires models to find and synthesize information across multiple documents.

ACEBench evaluates whether large language models can use tools in realistic settings: picking the right API, filling its arguments, pushing back on requests that cannot be satisfied, and driving multi-step agent tasks against a simulated environment. Data i…

AGIEval is a human-centric benchmark designed to evaluate foundation models in the context of human cognition and problem-solving. It uses official, standard, and authoritative admission and qualification exams intended for general human test-takers, such a…

AI2D (AI2 Diagrams) is a benchmark dataset for evaluating AI systems' ability to understand and reason about scientific diagrams. It contains over 5,000 diverse diagrams from science textbooks covering topics like the water cycle, food webs, and biological…

AIME 2024 (American Invitational Mathematics Examination 2024) is a benchmark based on problems from the prestigious AIME competition. These problems represent some of the most challenging high school mathematics problems, requiring creative problem-solving…

AIME 2025 (American Invitational Mathematics Examination 2025) is a benchmark based on problems from the prestigious AIME competition, one of the most challenging high school mathematics contests in the United States. It tests advanced mathematical reasonin…

AIME 2026 (American Invitational Mathematics Examination 2026) is a benchmark based on problems from the prestigious AIME competition, one of the most challenging high school mathematics contests in the United States. It tests advanced mathematical reasonin…

AIR-Bench Chat is the generative half of AIR-Bench (Audio InstRuction Benchmark, ACL 2024 main conference) — the first instruction-following benchmark for large audio-language models (LALMs), covering human speech, natural sounds and music. It contains roug…

AIR-Bench Foundation is the discriminative half of AIR-Bench (Audio InstRuction Benchmark, ACL 2024 main conference) — the first instruction-following benchmark for large audio-language models (LALMs), covering human speech, natural sounds and music. The Fo…

AlpacaEval 2.0 is an evaluation framework for instruction-following language models that uses an LLM judge to compare model outputs against a strong baseline. It provides win-rate metrics reflecting human preferences.

AMC (American Mathematics Competitions) is a benchmark based on problems from the AMC 10/12 competitions from 2022-2024. These multiple-choice problems test mathematical problem-solving skills at the high school level and serve as qualifiers for the AIME co…

The AnatEM corpus is an extensive resource for anatomical entity recognition, created by extending and combining previous corpora. It includes over 13,000 annotations across 1,212 biomedical documents, focusing on identifying anatomical structures from subc…

ARC (AI2 Reasoning Challenge) is a benchmark designed to evaluate science question answering capabilities of AI models. It consists of multiple-choice science questions from grade 3 to grade 9, divided into an Easy set and a Challenge set based on difficulty.

ARC-AGI-2 (Abstraction and Reasoning Corpus for Artificial General Intelligence 2) is a benchmark designed to measure an AI system's ability to efficiently acquire new skills on-the-fly, using only a handful of demonstrations. It evaluates abstract reasonin…

ARC-Challenge-Indic is a translation of the AI2 Reasoning Challenge (ARC-Challenge) science question-answering benchmark into 10 Indic languages, plus the original English set, for evaluating multilingual scientific reasoning.

ArenaHard is a challenging benchmark that evaluates language models through competitive pairwise comparison. Models are judged against a GPT-4 baseline on difficult tasks requiring reasoning, understanding, and generation capabilities.

ArXiv-Math is a benchmark of 103 research-level mathematics problems extracted from arXiv preprints. These problems represent cutting-edge mathematical research and test the ability of language models to reason about advanced mathematical concepts at the fr…

ArxivRollBench is a rolling benchmark built from recent arXiv papers. It evaluates whether large language models can reason over fresh scientific text through three task formats: sequencing, cloze, and next-fragment prediction.

ArxivRollBench is a rolling benchmark built from recent arXiv papers. It evaluates whether large language models can reason over fresh scientific text through three task formats: sequencing, cloze, and next-fragment prediction.

AutomationBench evaluates agents on realistic business workflows across sales, marketing, operations, support, finance, and HR. EvalScope runs the public tasks, simulated SaaS services, and assertion-based scoring provided by Zapier's official Python package.

BabyVision is a visual perception benchmark that evaluates the fundamental visual abilities of multimodal large language models through tasks inspired by infant and early childhood visual development. It focuses on fine-grained discrimination, spatial perce…

BBH (BIG-Bench Hard) is a subset of 23 challenging tasks from the BIG-Bench benchmark that are specifically selected because language models initially struggled with them. These tasks require complex reasoning abilities that benefit from Chain-of-Thought (C…

The BC2GM (BioCreative II Gene Mention) dataset is a widely used corpus for gene mention recognition, consisting of 20,000 sentences from MEDLINE abstracts where gene and protein names have been manually annotated by domain experts.

The BC4CHEMD (BioCreative IV CHEMDNER) dataset is a corpus of 10,000 PubMed abstracts with 84,355 chemical entity mentions manually annotated by experts for chemical named entity recognition.

The BC5CDR corpus is a manually annotated resource of 1,500 PubMed articles developed for the BioCreative V challenge, containing over 4,400 chemical mentions, 5,800 disease mentions, and 3,100 chemical-disease interactions.

BFCL (Berkeley Function Calling Leaderboard) v3 is the first comprehensive and executable function call evaluation benchmark for assessing LLMs' ability to invoke functions. It evaluates various forms of function calls, diverse scenarios, and executability.

BFCL-v4 (Berkeley Function-Calling Leaderboard V4) is a comprehensive benchmark for evaluating agentic function-calling capabilities of LLMs. It tests web search, memory operations, and format sensitivity as building blocks for agentic applications.

BhashaBench-Multi (Ayurveda) is a domain-specific multiple-choice benchmark evaluating LLM knowledge of Ayurvedic medicine across 22 Indic languages. Each question originates in English and is machine translated (with LLM-judged translation quality scores)…

BhashaBench-Multi (Finance) is a domain-specific multiple-choice benchmark evaluating LLM knowledge of finance across 22 Indic languages. Each question originates in English and is machine translated (with LLM-judged translation quality scores) into the tar…

BhashaBench-Multi (Krishi) is a domain-specific multiple-choice benchmark evaluating LLM knowledge of agriculture (Krishi) across 22 Indic languages. Each question originates in English and is machine translated (with LLM-judged translation quality scores)…

BhashaBench-Multi (Legal) is a domain-specific multiple-choice benchmark evaluating LLM knowledge of Indian law across 22 Indic languages. Each question originates in English and is machine translated (with LLM-judged translation quality scores) into the ta…

BhashaBench-Ayur is the predecessor of BhashaBench-Multi's ayur domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of Ayurvedic medicine, covering English and Hindi.

BhashaBench-Finance is the predecessor of BhashaBench-Multi's finance domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of finance, covering English and Hindi.

BhashaBench-Krishi is the predecessor of BhashaBench-Multi's krishi domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of agriculture (Krishi), covering English and Hindi.

BhashaBench-Legal is the predecessor of BhashaBench-Multi's legal domain: a domain-specific multiple-choice benchmark evaluating LLM knowledge of Indian law, covering English and Hindi.

BigCodeBench is an easy-to-use benchmark for solving practical and challenging tasks via code. It evaluates the true programming capabilities of large language models (LLMs) in a more realistic setting with diverse function calls from 139 popular libraries…

BigCodeBench-Hard is a curated subset of BigCodeBench containing 148 tasks that are more aligned with real-world programming tasks. These tasks require more complex reasoning and multi-step problem solving.

BiomixQA is a curated biomedical question-answering dataset designed to evaluate AI models on biomedical knowledge and reasoning. It has been utilized to validate the Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) framework across different L…

BLINK is a benchmark designed to evaluate the core visual perception abilities of Multimodal Large Language Models (MLLMs). It transforms 14 classic computer vision tasks into 3,807 multiple-choice questions with single or multiple images and visual prompts.

BroadTwitterCorpus is a dataset of tweets collected over stratified times, places, and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to pr…

BrowseComp is an OpenAI benchmark for evaluating browsing and search agents. It contains 1,266 hard-to-find, fact-seeking questions with short, verifiable answers. EvalScope loads the mirrored dataset from ModelScope (evalscope/browsecomp).

CCBench (Chinese Culture Bench) is an extension of MMBench specifically designed to evaluate multimodal models' understanding of Chinese traditional culture. It covers various aspects of Chinese cultural heritage through visual question answering.

CC-OCR V2 is a challenging OCR benchmark tailored to real-world enterprise document processing. It deliberately over-samples the hard and corner cases that prior OCR benchmarks under-represent, such as photographed and scanned tables, handwritten formulas,…

C-Eval is a comprehensive Chinese evaluation benchmark designed to assess the knowledge and reasoning abilities of language models in Chinese. It covers 52 subjects ranging from STEM to humanities and social sciences, with questions from middle school to pr…

ChartQA is a benchmark designed to evaluate question-answering capabilities over charts and data visualizations. It tests both visual reasoning and logical understanding of various chart types including bar charts, line graphs, and pie charts.

CharXiv is a comprehensive chart understanding benchmark from NeurIPS 2024 that evaluates multimodal large language models on realistic scientific charts from arXiv papers. It tests both low-level chart element perception (descriptive) and high-level reason…

Chinese SimpleQA is a Chinese question-answering dataset designed to evaluate the performance of language models on simple factual questions. It tests the model's ability to understand and generate correct answers in Chinese across various knowledge domains.

CL-bench represents a step towards building LMs with this fundamental capability (Context Learning), making them more intelligent and advancing their deployment in real-world scenarios. This benchmark is specifically designed to evaluate a model's ability t…

Claw-Eval evaluates assistant agents on realistic personal-assistant workflows that require tool use, file and fixture access, multimodal inputs, and simulated user interactions. EvalScope runs the pinned official Claw-Eval Python runner, Docker sandbox, an…

CMATH is a Chinese elementary school mathematics benchmark containing 1,698 problems across grades 1-6. It evaluates the mathematical reasoning capabilities of language models on Chinese-language math word problems at increasing difficulty levels.

C-MMLU (Chinese Massive Multitask Language Understanding) is a comprehensive Chinese evaluation benchmark covering 67 subjects across STEM, humanities, social sciences, and China-specific topics. It evaluates models' knowledge and reasoning in Chinese conte…

CMMU (Chinese Massive Multi-discipline Multimodal Understanding) includes manually collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines in Chinese. It is the Chinese counterpart to MMMU.

CMMU is a novel Chinese multi-modal benchmark designed to evaluate domain-specific knowledge across seven foundational subjects: math, biology, physics, chemistry, geography, politics, and history. It tests multimodal understanding in Chinese educational co…

CoinFlip is a symbolic reasoning benchmark that tests LLMs' ability to track binary state changes through sequences of actions. Each problem involves determining a coin's final state (heads/tails) after various flipping operations.

Common Voice 15 is a massively multilingual speech corpus collected by Mozilla, covering 114 languages with thousands of hours of validated speech data from volunteers worldwide.

CommonsenseQA is a benchmark for evaluating AI models' ability to answer questions that require commonsense reasoning about the world. Questions are designed to require background knowledge not explicitly stated in the question.

Competition-MATH is a comprehensive benchmark of 12,500 challenging competition mathematics problems collected from AMC, AIME, and other prestigious math competitions. It is designed to evaluate the advanced mathematical reasoning capabilities of language m…

CoNLL-2003 is a classic Named Entity Recognition (NER) benchmark introduced at the Conference on Computational Natural Language Learning 2003. It contains news articles annotated with four entity types.

The CoNLL++ dataset is a corrected and cleaner version of the test set from the widely-used CoNLL2003 NER benchmark. It provides improved annotation quality for evaluating named entity recognition systems on news text.

Copious corpus is a gold standard corpus for biodiversity entity recognition, consisting of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities covering taxonomic and ecological information.

CountQA probes object counting, a basic perceptual skill that multimodal models are largely unevaluated on. Its images were hand-captured in everyday environments and deliberately feature high object density, clutter and occlusion, so counting cannot be sol…

CrossNER is a fully-labeled collection of named entity recognition (NER) data spanning over five diverse domains: AI, Literature, Music, Politics, and Science. It enables cross-domain NER evaluation and domain adaptation research.

Data-Collection is a flexible framework for mixing multiple evaluation datasets into a unified evaluation suite. It enables comprehensive model assessment using carefully selected samples from various benchmarks.

DeepSWE is a coding-agent benchmark for evaluating repository-level software engineering tasks. EvalScope integrates it through Pier and runs each benchmark sample as one Pier Python API job.

DeepSearchQA is a Google DeepMind benchmark for evaluating deep research agents on difficult multi-step information-seeking tasks across the open web. It contains 900 prompts spanning 17 domains and is designed to measure exhaustive answer-set generation ra…

DocMath-Eval is a comprehensive benchmark focused on numerical reasoning within specialized domains. It requires models to comprehend long and specialized documents and perform numerical reasoning to answer questions.

DocVQA (Document Visual Question Answering) is a benchmark designed to evaluate AI systems' ability to answer questions based on document images such as scanned pages, forms, invoices, and reports. It requires understanding complex document layouts, structu…

Drivelology Binary Classification evaluates models' ability to identify "drivelology" - a unique linguistic phenomenon characterized as "nonsense with depth." These are utterances that are syntactically coherent yet pragmatically paradoxical, emotionally lo…

Drivelology Multi-label Classification evaluates models' ability to categorize "drivelology" text into rhetorical technique categories: inversion, wordplay, switchbait, paradox, and misdirection. Each text may belong to multiple categories.

Drivelology Narrative Selection evaluates models' ability to understand the underlying narrative of "drivelology" text - linguistic utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive.

Drivelology Narrative Writing evaluates models' ability to generate detailed descriptions illustrating the implicit narrative of "drivelology" text - linguistic utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or…

DROP (Discrete Reasoning Over Paragraphs) is a challenging reading comprehension benchmark that requires models to perform discrete reasoning operations over text passages. Unlike simple extractive QA, DROP questions require numerical reasoning, counting, a…

EmbSpatial-Bench is a benchmark for evaluating embodied spatial understanding of large vision-language models (LVLMs). The benchmark is automatically derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective: close, far,…

EQ-Bench is a benchmark for evaluating language models on emotional intelligence tasks. It assesses the ability to predict likely emotional responses of characters in dialogues by rating the intensity of possible emotional reactions.

ERQA (Embodied Reasoning QA) is a benchmark for evaluating spatial reasoning and embodied understanding capabilities of multimodal large language models. It tests models' ability to reason about trajectories, actions, spatial relationships, and task plannin…

EvalMuse is a text-to-image benchmark that evaluates the quality and semantic alignment of generated images using fine-grained analysis with the FGA-BLIP2Score metric.

The FinNER dataset is a corpus of financial agreements from public U.S. Security and Exchange Commission (SEC) filings, annotated with Person, Organization, Location, and Miscellaneous entities to support information extraction for credit risk assessment.

FLEURS (Few-shot Learning Evaluation of Universal Representations of Speech) is a massively multilingual benchmark covering 102 languages for evaluating automatic speech recognition (ASR), spoken language understanding, and speech translation.

FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems. It evaluates factuality, retrieval accuracy, and reasoning abilities in long-context scenarios.

GAIA (General AI Assistants) is a benchmark of 450+ questions targeting next-generation LLMs with tool use, web browsing and multi-step reasoning. Each question has an unambiguous short answer and is bucketed into one of three difficulty levels.

GDPval evaluates whether models can complete realistic economically valuable work tasks and produce requested deliverable files. This adapter targets OpenAI's public 220-task gold subset mirrored on ModelScope as openai-mirror/gdpval.

GEdit-Bench (Grounded Edit Benchmark) is an image editing benchmark grounded in real-world usage scenarios. It provides comprehensive evaluation of image editing models across diverse editing tasks with LLM-based judging.

GenAI-Bench is a comprehensive text-to-image benchmark featuring 1600 prompts designed to evaluate image generation models across diverse categories and complexity levels.

GeneralArena is a custom benchmark designed to evaluate the performance of large language models in a competitive setting, where models are pitted against each other in custom tasks to determine their relative strengths and weaknesses.

General-FunctionCalling is a customizable benchmark for evaluating function calling (tool use) capabilities of language models. It tests both the decision to call tools and the accuracy of generated function calls.

General-MCQ is a customizable multiple-choice question answering benchmark for evaluating language models. It supports flexible data formats and variable number of answer choices.

General-QA is a customizable question answering benchmark for evaluating language models on open-ended text generation tasks. It supports flexible data formats and configurable evaluation metrics.

General Text-to-Image is a customizable benchmark adapter for evaluating text-to-image generation models with user-provided prompts and images.

General-VMCQ is a customizable visual multiple-choice question answering benchmark for multimodal models. It uses MMMU-style format with image/video/audio placeholders in text, supporting flexible media inputs.

General-VQA is a customizable visual question answering benchmark for evaluating multimodal models. It supports OpenAI-compatible message format with flexible image/video/audio input (local paths, URLs, or base64).

GeniaNER is a large-scale biomedical NER dataset consisting of 2,000 MEDLINE abstracts with over 400,000 words and almost 100,000 annotations for biological terms. It is one of the most comprehensive resources for biomedical entity recognition.

GPQA (Graduate-Level Google-Proof Q&A) Diamond is a challenging benchmark of 198 multiple-choice questions written by domain experts in biology, physics, and chemistry. The questions are designed to be extremely difficult, requiring PhD-level expertise to a…

GSM8K (Grade School Math 8K) is a high-quality dataset of 8.5K linguistically diverse grade school math word problems created by human problem writers. The dataset is specifically designed to evaluate and improve the multi-step mathematical reasoning capabi…

GSM8K-Indic translates the GSM8K grade-school math word problems into 10 Indic languages, each available in native script and a romanized (Latin transliteration) variant, plus the original English.

GSM8K-V is a purely visual multi-image mathematical reasoning benchmark that systematically transforms each GSM8K math word problem into its visual counterpart. It enables clean within-item comparison across modalities for multimodal math evaluation.

HallusionBench is an advanced diagnostic benchmark designed to evaluate image-context reasoning and detect hallucination tendencies in Large Vision-Language Models (LVLMs). It specifically tests models' susceptibility to language hallucination and visual il…

HaluEval is a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. It provides a comprehensive benchmark for assessing model reliability and factual accuracy.

HarveyNER is a dataset with fine-grained locations annotated in tweets, collected during Hurricane Harvey. It presents unique challenges with complex and long location mentions in informal crisis-related descriptions.

HealthBench is a comprehensive benchmark designed to measure AI capabilities for health-related tasks. Built in partnership with 262 physicians from 60 countries, it includes 5,000 realistic health conversations with custom physician-created rubrics.

HellaSwag is a benchmark for evaluating commonsense natural language inference, specifically testing a model's ability to complete sentences describing everyday situations. The dataset uses adversarial filtering to create challenging distractors that are gr…

HellaSwag-Hindi is a Hindi translation of the HellaSwag commonsense sentence-completion benchmark's full validation set. The context stem stays in English; the 4 candidate continuations are translated into Hindi, so the model must connect an English scenari…

HiPhO is the first benchmark dedicated to high school physics Olympiads with human-aligned evaluation. It compiles 13 recent Olympiad exams (2024-2025) spanning international and regional competitions, with mixed modalities that range from text-only problem…

Humanity's Last Exam (HLE) is a comprehensive language model benchmark consisting of 2,500 questions across a broad range of subjects. Created jointly by the Center for AI Safety and Scale AI, it represents one of the most challenging academic benchmarks av…

HMMT February 2025 (MathArena) is a challenging evaluation benchmark derived from the Harvard-MIT Mathematics Tournament (HMMT) February 2025 competition, one of the most prestigious and difficult high school math contests globally.

HMMT February 2026 is a challenging evaluation benchmark derived from the Harvard-MIT Mathematics Tournament (HMMT) February 2026 competition, one of the most prestigious and difficult high school math contests globally.

HMMT November 2025 (MathArena) is a challenging evaluation benchmark derived from the Harvard-MIT Mathematics Tournament (HMMT) November 2025 competition, one of the most prestigious and difficult high school math contests globally. It is a different contes…

HPD-v2 (Human Preference Dataset v2) is a text-to-image benchmark that evaluates generated images based on human preferences. It uses the HPSv2.1 score metric trained on large-scale human preference data.

HumanEval is a benchmark for evaluating the code generation capabilities of language models. It consists of 164 hand-written Python programming problems with function signatures, docstrings, and comprehensive test cases.

HumanEval Plus is a rigorous extension of OpenAI's HumanEval benchmark, designed to address high false-positive rates in code generation evaluation. It augments the original test cases with tens of thousands of automatically generated inputs to expose edge-…

IFBench is a benchmark designed to evaluate how reliably AI models follow novel, challenging, and diverse verifiable instructions, with a strong focus on out-of-domain generalization. Developed by AllenAI, it addresses overfitting and data contamination iss…

IFEval (Instruction-Following Eval) is a benchmark for evaluating how well language models follow explicit, verifiable instructions. It contains prompts with specific formatting, content, or structural requirements that can be objectively verified.

IMO-AnswerBench is a benchmark of 400 challenging problems sourced from the International Mathematical Olympiad (IMO) Shortlists. It covers four major mathematical domains and is designed to evaluate advanced mathematical reasoning capabilities of language…

BoolQ-Indic is a translation of the BoolQ yes/no reading-comprehension benchmark into 10 Indic languages plus English, for evaluating multilingual passage understanding.

IndicParam is a graduate-level benchmark evaluating LLM understanding of low- and extremely low-resource Indic languages. All 13,207 multiple-choice questions are sourced from official UGC-NET language question papers and answer keys, presented in each lang…

InfoVQA (Infographic Visual Question Answering) is a benchmark designed to evaluate AI models' ability to answer questions based on information-dense images such as charts, graphs, diagrams, maps, and infographics. It focuses on understanding complex visual…

IQuiz is a Chinese benchmark for evaluating AI models on intelligence quotient (IQ) and emotional quotient (EQ) questions. It tests logical reasoning, pattern recognition, and social-emotional understanding through multiple-choice questions.

The JNLPBA dataset is a widely-used resource for bio-entity recognition, consisting of 2,404 MEDLINE abstracts from the GENIA corpus annotated for five key molecular biology entity types. It is a standard benchmark for biomedical NER.

The JNLPBA-Rare dataset is a specialized subset of the JNLPBA test set created to evaluate zero-shot performance on its least frequent entity types: RNA and cell line. It tests model ability to recognize rare biomedical entities.

JobBench evaluates agentic systems on realistic professional work tasks that require reading reference files, producing deliverables, and reconciling multi-source information. This adapter uses the ModelScope dataset evalscope/job-bench.

K2-Vendor-Verifier checks whether a third-party deployment of Kimi-K2 faithfully reproduces the official Moonshot AI API's tool-calling behavior. It replays the official evaluation prompt set against a vendor endpoint and compares finishreason and tool-call…

Kimi-Vendor-Verifier is a pre-flight compliance check for Kimi K2 / K2-Thinking deployments. It sends synthetic probe requests to verify that the vendor API correctly rejects non-default values of immutable decoding parameters (temperature, topp, presencepe…

KINA (Knowledge Index of Noah's Ark) is a high-density multidisciplinary knowledge benchmark for evaluating whether large language models can solve expert-level questions across 261 fine-grained disciplines. It is the first benchmark to incorporate discipli…

LibriSpeech is a large-scale corpus of approximately 1,000 hours of read English speech derived from audiobooks. It is one of the most widely used benchmarks for evaluating automatic speech recognition (ASR) systems.

LiveCodeBench is a contamination-free benchmark for evaluating code generation models on real-world competitive programming problems. It continuously collects new problems from coding platforms to ensure models haven't seen the test data during training.

LoCoMo evaluates very long-term conversational memory in two-person multi-session dialogues. This adapter supports the official question-answering task from locomo10.json.

LogiQA is a benchmark for evaluating logical reasoning abilities, sourced from expert-written questions originally designed for testing human logical reasoning skills in standardized examinations.

LogicVista evaluates the fundamental logical reasoning abilities of multimodal large language models in visual contexts. Every item is a multiple-choice question whose answer options are drawn inside the image (diagrams, puzzles, sequences, charts), so a mo…

LongBench v2 is a challenging benchmark for evaluating long-context understanding of large language models. It covers a wide variety of real-world tasks that require reading and comprehending long documents (ranging from a few thousand to over 2 million tok…

LongMemEval evaluates long-term interactive memory in chat assistants. Each question is answered from a timestamped multi-session user-assistant history.

MaritimeBench is a benchmark for evaluating AI models on maritime-related multiple-choice questions in Chinese. It consists of specialized questions related to maritime knowledge, navigation, marine engineering, and seafaring operations.

Maritime-OCR-Bench is a comprehensive evaluation benchmark for assessing multimodal large model capabilities on OCR-related tasks. The current released set contains 1,888 manually curated samples across five task types.

MATH-500 is a curated subset of 500 problems from the MATH benchmark, designed to evaluate the mathematical reasoning capabilities of language models. It covers five difficulty levels across various mathematical topics including algebra, geometry, number th…

MathQA is a large-scale dataset for mathematical word problem solving, gathered by annotating the AQuA-RAT dataset with fully-specified operational programs using a new representation language. It contains diverse math problems requiring multi-step reasoning.

MathVerse is an all-around visual math benchmark designed for equitable and in-depth evaluation of Multimodal Large Language Models (MLLMs). It contains 2,612 high-quality, multi-subject math problems with diagrams, transformed into 15K test samples across…

MATH-Vision (MATH-V) is a meticulously curated dataset of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. It evaluates mathematical reasoning abilities in multimodal settings.

MathVista is a comprehensive benchmark for mathematical reasoning in visual contexts. It combines newly created datasets with existing benchmarks to evaluate models on diverse visual mathematical reasoning tasks across multiple domains.

MBPP (Mostly Basic Python Problems) is a benchmark consisting of approximately 1,000 crowd-sourced Python programming problems designed for entry-level programmers. It evaluates a model's ability to understand problem descriptions and generate correct Pytho…

MBPP Plus is a fortified version of the MBPP benchmark, created to improve evaluation reliability for basic Python programming synthesis. It addresses quality issues in the original dataset and significantly increases test coverage for each problem.

MCP-Atlas is a Scale AI benchmark for evaluating tool-use competency with real Model Context Protocol (MCP) servers. It contains public tasks with prompts, allowed tool lists, ground-truth tool trajectories, and expert claims used for LLM-as-judge coverage…

MeasureBench is a comprehensive benchmark for evaluating the ability of vision-language models (VLMs) to read values from measuring instruments. It covers both real-world photographs and synthetically generated images of 26 instrument types across 4 design…

MedMCQA is a large-scale multiple-choice question answering dataset designed to address real-world medical entrance exam questions. It contains over 194K questions covering diverse medical topics from Indian medical entrance examinations (AIIMS, NEET-PG).

MedXpertQA is an expert-level medical multiple-choice benchmark designed to evaluate advanced medical knowledge and reasoning. It contains separate text-only and multimodal tracks built from challenging medical examination questions and reviewed by licensed…

MGSM (Multilingual Grade School Math) is a benchmark designed to evaluate multilingual mathematical reasoning capabilities of language models. It extends GSM8K to 11 typologically diverse languages, testing whether models can perform chain-of-thought reason…

MIA-Bench is a multimodal instruction-following benchmark designed to evaluate vision-language models on their ability to follow complex, compositional instructions grounded in images. Each sample contains an image paired with a multi-component instruction,…

MicroVQA is an expert-curated benchmark for multimodal reasoning in microscopy-based scientific research. It evaluates AI models' ability to understand and reason about microscopy images across various scientific domains.

MILU (Multi-task Indic Language Understanding Benchmark) is a comprehensive evaluation dataset for assessing LLM performance across 11 Indic languages. It spans 8 domains and 41 subjects, combining translated general-knowledge questions with culturally spec…

Minerva-Math is a benchmark designed to evaluate advanced mathematical and quantitative reasoning capabilities of language models. It consists of 272 challenging problems sourced primarily from MIT OpenCourseWare courses, covering university and graduate-le…

MiniMax-Vendor-Verifier is a multi-validator deployment-correctness check for MiniMax M2 / M2.5 / M2.7 vendors. Each prompt row carries an optional checktype tag that routes it through specific validators, plus an always-on erroronlyreasoning detector for t…

MiniWoB evaluates whether a multimodal agent can complete short browser tasks such as clicking buttons, filling forms, scrolling and dragging items.

The MIT-Movie-Trivia dataset, originally created for slot filling in movie domain dialogues, has been modified for NER by merging and filtering slot types. It tests recognition of movie-related entities in conversational queries.

The MIT-Restaurant dataset is a collection of restaurant review text specifically curated for training and testing NLP models for Named Entity Recognition. It contains sentences from real reviews with annotations in BIO format.

MMBench is a systematically designed benchmark for evaluating vision-language models across 20 fine-grained ability dimensions. It uses a novel CircularEval strategy and provides both English and Chinese versions for cross-lingual evaluation.

MMStar is an elite vision-indispensable multimodal benchmark designed to ensure genuine visual dependency in evaluation. Each sample is carefully curated to require actual visual understanding, minimizing data leakage and testing advanced multimodal capabil…

MMAU (Massive Multitask Audio Understanding) is a comprehensive benchmark for evaluating audio understanding capabilities of multimodal large language models across diverse audio tasks.

MMLU (Massive Multitask Language Understanding) is a comprehensive evaluation benchmark designed to measure knowledge acquired during pretraining. It covers 57 subjects across STEM, humanities, social sciences, and other domains, ranging from elementary to…

MMLU-Pro is an enhanced version of MMLU with increased difficulty and reasoning requirements. It features 10 answer choices instead of 4 and includes more challenging questions requiring deeper reasoning across 14 diverse domains.

MMLU-Redux is an improved version of the MMLU benchmark with corrected answers. It addresses known errors in the original MMLU dataset by fixing incorrect ground truth labels, missing correct options, and ambiguous questions.

MMMLU (Multilingual Massive Multitask Language Understanding) is a multilingual extension of the MMLU benchmark. It evaluates the multilingual knowledge and reasoning capabilities of language models across 14 languages, covering 57 subjects from the origina…

MMMU (Massive Multi-discipline Multimodal Understanding) is a comprehensive benchmark designed to evaluate multimodal models on expert-level tasks requiring college-level subject knowledge and deliberate reasoning. It covers 30 subjects across 6 core discip…

MMMU-PRO is an enhanced multimodal benchmark designed to rigorously assess the genuine understanding capabilities of advanced AI models across multiple modalities. It builds upon the original MMMU benchmark with key improvements that make evaluation more ch…

MRI-MCQA is a specialized benchmark composed of multiple-choice questions related to Magnetic Resonance Imaging (MRI). It evaluates AI models' understanding of MRI physics, protocols, image acquisition, and clinical applications.

MSR-VTT is a large-scale open-domain video captioning benchmark for evaluating video-to-text generation. The native adapter groups records by videoid, so multiple annotation rows for one video become one sample with multiple reference captions.

MSVD is a classic video captioning benchmark with short web videos annotated by many human captions. The native adapter treats each video as one evaluation sample and uses all available captions as references.

Multi-IF is a benchmark designed to evaluate LLM capabilities in multi-turn instruction following within a multilingual environment. It tests the ability to follow complex instructions across multiple conversation turns in different languages.

MultiNERD is a large-scale, multilingual, and multi-genre dataset for fine-grained Named Entity Recognition, automatically generated from Wikipedia and Wikinews. It covers 10 languages and 15 distinct entity categories.

MultiPL-E HumanEval is a multilingual code generation benchmark derived from OpenAI's HumanEval. It extends the original HumanEval to 18 programming languages, enabling cross-lingual evaluation of code generation capabilities.

MultiPL-E MBPP is a multilingual code generation benchmark derived from MBPP (Mostly Basic Python Programming). It extends the original MBPP to 18 programming languages, enabling cross-lingual evaluation of code generation capabilities.

MusicTrivia is a curated multiple-choice benchmark for evaluating AI models on music knowledge. It covers both classical and modern music topics including composers, musical periods, instruments, and popular artists.

MuSR (Multistep Soft Reasoning) is a benchmark for evaluating complex reasoning abilities through narrative-based problems. It includes murder mysteries, object placements, and team allocation scenarios requiring multi-step inference.

MVBench is a public multimodal video understanding benchmark covering temporal perception, attribute/state reasoning, symbolic ordering, and high-level cognition. This native adapter uses the ModelScope PKU-Alignment/MVBench mirror by default, which provide…

The NCBI disease corpus is a manually annotated resource of PubMed abstracts designed for disease name recognition and normalization. It provides a gold standard for evaluating disease named entity recognition systems.

Needle in a Haystack is a benchmark focused on evaluating information retrieval capabilities in long-context scenarios. It tests a model's ability to find specific information (needles) within large documents (haystacks).

OCRBench is a comprehensive evaluation benchmark designed to assess the OCR (Optical Character Recognition) capabilities of Large Multimodal Models. It covers five key OCR-related tasks with 1,000 manually verified question-answer pairs.

OCRBench v2 is a large-scale bilingual text-centric benchmark with the most comprehensive set of OCR tasks (4x more than OCRBench v1), covering 31 diverse scenarios including street scenes, receipts, formulas, diagrams, and more.

OfficeQA is a grounded reasoning benchmark by Databricks, built for evaluating model/agent performance on end-to-end grounded reasoning tasks over U.S. Treasury Bulletin documents (1939-2025).

olmOCR-Bench evaluates end-to-end document transcription: a model reads one rendered PDF page and returns the full Markdown transcription of that page, which is then checked against human-written unit tests instead of a single reference answer.

OlympiadBench is an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from mathematics and physics competitions, including the Chinese college entrance exam (CEE). It provides rigorous evaluation of advanced scientific reason…

OmniBench is a pioneering universal multimodal benchmark designed to rigorously evaluate MLLMs' capability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously.

This adapter preserves EvalScope's original 981-page OmniDocBench TSV integration for compatibility with existing evaluations.

OmniDocBench v1.6 evaluates end-to-end document parsing for text, formulas, tables, layout, and reading order. This adapter is intentionally restricted to the official v1.6 data and scoring contract.

$OneMillion-Bench ($1M-Bench) evaluates how well language models and agents complete economically valuable, expert-level professional work. The public release contains 400 bilingual tasks written and reviewed by domain experts across finance, healthcare, in…

OntoNotes Release 5.0 is a large, multilingual corpus containing text in English, Chinese, and Arabic across various genres. It is richly annotated with multiple layers of linguistic information including syntax, predicate-argument structure, word sense, na…

MRCR (Memory-Recall with Contextual Retrieval) is OpenAI's benchmark for evaluating retrieval and recall capabilities in long-context scenarios. It tests whether models can correctly extract and use specific information (needles) embedded in long prompts.

PerceptionBench is a benchmark from Moonshot AI that evaluates the atomic visual perception capabilities of multimodal large language models. It is built bottom-up: the earliest failure points of frontier MLLMs on 42 existing benchmarks were diagnosed to de…

PerspectiveGap evaluates whether a model can compose orchestration prompts for multi-agent systems while routing only the context each sub-agent needs.

PerspectiveGap evaluates whether a model can compose orchestration prompts for multi-agent systems while routing only the context each sub-agent needs.

PhyX is the first large-scale benchmark for physical reasoning in realistic, visually grounded scenarios. This is its multiple-choice variant: each university-level physics problem is presented with a figure and four answer options, and the model has to nam…

PhyX is the first large-scale benchmark for physical reasoning in realistic, visually grounded scenarios. This is its open-ended variant: no options are shown, so the model has to derive the answer of a university-level physics problem from the figure and s…

PIQA (Physical Interaction QA) is a benchmark for evaluating AI models' understanding of physical commonsense - how objects interact in the physical world and what happens when we manipulate them.

PLawBench is a rubric-based benchmark that evaluates large language models on real-world Chinese legal practice. It mirrors the workflow of a practising lawyer across three hierarchical levels: eliciting facts during a public legal consultation, analysing a…

PMC-VQA is a large-scale medical visual question answering benchmark built from figures of biomedical papers in the PubMed Central Open Access subset. This integration evaluates the manually verified testclean split, the 2,000-question subset the authors re…

PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 difficulty levels with 9,000 high-quality problem samples. It ensures difficulty comprehensiveness, language diversity, and high-quality translation for discriminative m…

POPE (Polling-based Object Probing Evaluation) is a benchmark specifically designed to evaluate object hallucination in Large Vision-Language Models (LVLMs). It tests models' ability to accurately identify objects present in images through yes/no questions.

PRBench (Professional Reasoning Benchmark) evaluates open-ended reasoning on realistic, high-stakes Finance and Legal problems. Its expert-authored conversations and fine-grained rubrics measure whether a response is accurate, useful, auditable, and appropr…

ProcessBench is a benchmark for evaluating AI models on mathematical reasoning process verification. It tests the ability to identify errors in step-by-step mathematical solutions across various difficulty levels from GSM8K to OmniMath.

PubMedQA is a biomedical question answering dataset designed to evaluate models' ability to reason over biomedical research texts. It contains questions derived from PubMed abstracts with yes/no/maybe answers.

QASC (Question Answering via Sentence Composition) is a question-answering dataset with a focus on multi-hop sentence composition. It consists of 9,980 8-way multiple-choice questions about grade school science, requiring models to combine multiple facts to…

RACE (ReAding Comprehension from Examinations) is a large-scale reading comprehension benchmark collected from Chinese middle school and high school English examinations. It tests comprehensive reading comprehension abilities.

RealWorldQA is a benchmark contributed by XAI designed to evaluate multimodal AI models' understanding of real-world spatial and physical environments. It uses authentic images from everyday scenarios to test practical visual comprehension.

Ref-Adv-s is the public 1,142-case subset of Ref-Adv, a referring expression comprehension benchmark designed to test whether multimodal large language models can distinguish a target from hard same-category visual distractors instead of relying on groundin…

RefCOCO is a dataset for training and evaluating models on Referring Expression Comprehension (REC). It contains images, object bounding boxes, and free-form natural-language expressions that uniquely describe target objects within MSCOCO images.

ResearchRubrics evaluates Deep Research agents on realistic, open-ended research tasks. Each task pairs a user prompt with expert-written, fine-grained rubrics covering explicit and implicit requirements, information synthesis, references, communication qua…

Sanskriti is a multiple-choice trivia benchmark testing knowledge of Indian states' culture, history, and geography, sourced from state-specific attributes (art, cuisine, festivals, etc.) with Wikipedia-backed answers. From the SANSKRITI paper (arXiv:2506.1…

SciCode is a challenging benchmark designed to evaluate language model capabilities in generating code for solving realistic scientific research problems. It covers 16 subdomains from 5 major domains: Physics, Math, Material Science, Biology, and Chemistry.

ScienceQA is a multimodal benchmark consisting of multiple-choice science questions derived from elementary and high school curricula. It covers diverse subjects including natural science, social science, and language science, with questions accompanied by…

SciQ is a crowdsourced science exam question dataset covering Physics, Chemistry, Biology, and other scientific domains. Most questions include supporting evidence paragraphs.

ScreenSpot-Pro is a GUI grounding benchmark built from authentic high-resolution screenshots of professional desktop software. Given a natural-language instruction, a model must locate the target UI element on the screen, which stresses fine-grained localiz…

SEED-Bench-2-Plus is a large-scale benchmark designed to evaluate Multimodal Large Language Models (MLLMs) on text-rich visual understanding tasks. It contains 2.3K multiple-choice questions with precise human annotations across real-world scenarios.

Seed-TTS-Eval is an objective benchmark for zero-shot text-to-speech and voice conversion evaluation. It uses out-of-domain English and Mandarin samples from Common Voice and DiDiSpeech-2, and the official evaluation focuses on intelligibility and speaker c…

SimpleQA is a benchmark by OpenAI designed to evaluate language models' ability to answer short, fact-seeking questions accurately. It focuses on measuring factual accuracy with clear grading criteria for correct, incorrect, and not-attempted answers.

SimpleVQA is the first comprehensive multimodal benchmark to evaluate the factuality ability of MLLMs to answer natural language short questions. It features high-quality, challenging queries with static and timeless reference answers.

SIQA (Social Interaction QA) is a benchmark for evaluating social commonsense intelligence - understanding people's actions and their social implications. Unlike benchmarks focusing on physical knowledge, SIQA tests reasoning about human behavior.

SkillsBench evaluates whether coding agents can discover and apply task-bundled Agent Skills. Each task contains an instruction, an optional skill directory, a Docker environment, an oracle solution, and a verifier. EvalScope builds the task Docker image, r…

SLAKE is a bilingual (English / Chinese) radiology visual question answering benchmark built by physicians on CT, MRI and X-Ray images. Questions cover both purely visual properties of the scan and medical knowledge that has to be recalled on top of what th…

SuperGPQA is a large-scale multiple-choice question answering dataset designed to evaluate model generalization across diverse fields. It contains 26,000+ questions from 50+ fields, with each question featuring 10 answer options.

SURDS benchmarks fine-grained spatial understanding and reasoning by vision-language models in realistic driving scenes. It is derived from the six-camera nuScenes dataset and evaluates object-centric and relational spatial skills without supplying depth ma…

SWE-bench Lite is a focused subset of SWE-bench containing 300 Issue-Pull Request pairs from 11 popular Python repositories. It provides a more accessible entry point for evaluating automated software engineering capabilities.

SWE-bench Lite Agentic is the agentic-mode evaluation of SWE-bench Lite, a focused subset of SWE-bench containing 300 Issue-Pull Request pairs from 11 popular Python repositories. The model autonomously drives a multi-turn agent loop inside a per-instance D…

SWE-bench Multilingual Agentic is the agentic-mode evaluation of SWE-bench Multilingual, a 300-task SWE-bench-style benchmark spanning 42 repositories and 9 programming languages. The model autonomously explores, edits, and submits a patch through a multi-t…

SWE-benchPro is a challenging benchmark from Scale AI evaluating LLMs/Agents on long-horizon software engineering tasks across multiple programming languages. Given a codebase and an issue, the model must autonomously explore the repository, edit source fil…

SWE-bench Verified is a human-validated subset of 500 samples from SWE-bench, designed to test systems' ability to automatically resolve real-world GitHub issues. Each sample represents a genuine bug fix or feature implementation from popular Python reposit…

SWE-bench Verified Agentic is the agentic-mode evaluation of SWE-bench Verified, a human-validated subset of 500 samples from SWE-bench. Unlike the oracle single-turn variant, the model must autonomously explore the repository, run shell commands, edit sour…

SWE-bench Verified Mini is a compact subset of SWE-bench Verified, containing 50 carefully selected samples that maintain the same distribution of performance, test pass rates, and difficulty as the full dataset while requiring only 5GB of storage instead o…

SWE-bench Verified Mini Agentic is the agentic-mode evaluation of SWE-bench Verified Mini, a compact 50-sample subset that maintains the same distribution of performance, test pass rates, and difficulty as the full Verified set while requiring only 5GB of s…

τ²-bench (Tau Squared Bench) is an extension and enhancement of the original τ-bench. It evaluates conversational AI agents in domain-specific scenarios with expanded capabilities including telecom domain support.

τ³-bench (Tau Cubed Bench) is the v1.0.0 release of the tau-bench family. It extends τ²-bench with a knowledge-retrieval domain, voice/audio-native evaluation, and 75+ task fixes across the existing domains.

τ-bench (Tau Bench) is a benchmark for evaluating conversational AI agents that interact with users through domain-specific API tools and policy guidelines. It simulates dynamic, multi-turn conversations where a language model acts as both the user and the…

Terminal-Bench v2 is a command-line benchmark suite that evaluates AI agents on 89 real-world, multi-step terminal tasks. Tasks range from compiling and debugging to system administration, running within isolated containers with rigorous validation.

Terminal-Bench v2.1 is an improved iteration of Terminal-Bench 2.0, with 26 task fixes addressing bugs, timeout adjustments, and reward hacking prevention. Recommended over v2.0 for new evaluations.

TIFA-160 is a text-to-image benchmark with 160 carefully curated prompts designed to evaluate the faithfulness and quality of generated images using automated VQA-based evaluation.

TIR-Bench (Thinking-with-Images Reasoning Benchmark) is a comprehensive multimodal benchmark that evaluates agentic visual reasoning capabilities of vision-language models. It covers diverse task categories requiring spatial, compositional, and multi-step v…

ToolBench-Static is a benchmark for evaluating AI models' ability to use tools and execute function calls in a static evaluation setting. It tests models on realistic tool-use scenarios with ground-truth comparisons.

Toolathlon is an agent benchmark for realistic, long-horizon tool use across many MCP-backed software environments. This EvalScope benchmark is a wrapper around the official Toolathlon remote evaluation service, not a local reimplementation of the MCP envir…

TORGO is a specialized database of dysarthric speech designed for evaluating ASR systems on speakers with motor speech disorders. It contains aligned acoustic and articulatory data from speakers with cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS).

TriviaQA is a large-scale reading comprehension dataset containing over 650K question-answer-evidence triples. Questions are collected from trivia enthusiast websites and paired with Wikipedia articles as evidence documents.

TriviaQA-Indic-MCQ reformats TriviaQA trivia questions as 4-way multiple-choice questions, translated into 10 Indic languages plus English, for evaluating multilingual world-knowledge recall.

TruthfulQA is a benchmark designed to measure whether language models generate truthful answers to questions. It focuses on questions where humans might give false answers due to misconceptions, superstitions, or false beliefs.

TVBench is a temporal video understanding benchmark for evaluating whether multimodal models can reason over dynamic visual events rather than isolated frames. It covers a broad set of video reasoning skills, including action recognition, action counting, t…

Tweebank-NER is an English Twitter corpus created by annotating the syntactically-parsed Tweebank V2 with four types of named entities: Person, Organization, Location, and Miscellaneous. It addresses NER challenges in informal social media text.

TweetNER7 is a large-scale NER dataset featuring over 11,000 tweets from 2019-2021, annotated with seven entity types to facilitate the study of short-term temporal shifts in social media language.

Video-MME-v2 is a public comprehensive video understanding benchmark. It contains 800 videos, 3,200 multiple-choice QA instances, and word-level subtitles with timestamps. The native adapter uses the shared DatasetHub abstraction for both annotation loading…

VisFactor evaluates foundational visual cognition in multimodal large language models using 20 vision-centric subtests adapted from the Factor-Referenced Cognitive Test (FRCT). It isolates abilities that support higher-level visual reasoning instead of meas…

VisuLogic is a benchmark for evaluating visual reasoning capabilities of Multimodal Large Language Models (MLLMs), independent of textual reasoning. It features carefully constructed visual reasoning tasks that are inherently difficult to articulate using l…

VLMs Are Biased (VLMBias) evaluates whether vision-language models answer objective visual questions from the image or fall back to memorized prior knowledge. It uses counterfactual images whose visible properties conflict with familiar concepts, such as an…

VQAv2 is the balanced Visual Question Answering benchmark built on COCO images. It evaluates whether multimodal models can answer open-ended natural-language questions grounded in image content.

VBench is a benchmark designed for evaluating visual search capabilities within multimodal reasoning systems. It focuses on actively locating and identifying specific visual information in high-resolution images, crucial for fine-grained visual understanding.

VTCBench (Vision-Text Compression Benchmark) evaluates long-context understanding when text is represented as rendered images, and compares it with a pure-text baseline.

WenetSpeech is a large-scale Mandarin Chinese speech corpus with over 10,000 hours of multi-domain transcribed audio data, designed for speech recognition research.

WideSearch evaluates search agents on broad web information-seeking tasks. Each task asks the agent to collect many atomic facts and return one structured Markdown table. EvalScope uses the ModelScope bytedance-community/WideSearch dataset.

Winogrande is a large-scale benchmark for commonsense reasoning, specifically designed to test pronoun resolution in the Winograd Schema Challenge format. It contains 44K problems that require understanding of physical and social commonsense.

WMT2024++ is a comprehensive machine translation benchmark based on the WMT 2024 news translation task. It supports 54 language pairs with English as the source language, enabling evaluation of translation quality across diverse target languages.

The WNUT2017 dataset is a collection of user-generated text from various social media platforms, like Twitter and YouTube, specifically designed for named entity recognition tasks focusing on emerging and unusual entities.

WorldVQA is a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). It measures models' ability to ground and name visual entities across a stratified taxonomy, spanning from common head-class objects…

ZebraLogicBench is a comprehensive evaluation framework for assessing LLM reasoning performance on logic grid puzzles derived from constraint satisfaction problems (CSPs). It tests systematic logical reasoning abilities.

ZeroBench is a challenging visual reasoning benchmark for Large Multimodal Models (LMMs). It consists of 100 high-quality, manually curated questions covering numerous domains, reasoning types, and image types designed to be beyond current model capabilities.