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Evaluation

35 items tagged with "evaluation"

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Benchmarks34

Benchmark

MMLU (Massive Multitask Language Understanding)

A 57-subject multiple-choice benchmark testing broad academic and professional knowledge across STEM, humanities, social sciences, and law.

Benchmark

MMLU-Pro

A harder, reasoning-focused successor to MMLU with ten answer options and tougher questions designed to separate frontier models that saturated the original.

Benchmark

GSM8K (Grade School Math 8K)

A benchmark of ~8,500 grade-school math word problems that test multi-step arithmetic reasoning with a single numeric answer.

Benchmark

MATH (Competition Mathematics)

A benchmark of 12,500 competition-style math problems across algebra, geometry, number theory, and calculus, graded on exact final-answer match.

Benchmark

HumanEval

A code-generation benchmark of 164 Python programming problems graded by executing unit tests, popularizing the pass@k metric.

Benchmark

MBPP (Mostly Basic Python Problems)

A benchmark of ~1,000 entry-level Python programming tasks with test cases, used to evaluate basic code synthesis from short descriptions.

Benchmark

SWE-bench

A benchmark of real GitHub issues from open-source Python repositories where a model must produce a patch that resolves the issue and passes tests.

Benchmark

SWE-bench Verified

A 500-task, human-validated subset of SWE-bench with clear specifications and reliable tests, used as the standard clean measure of agentic coding.

Benchmark

BIG-bench (Beyond the Imitation Game)

A collaborative suite of 200+ diverse tasks probing reasoning, knowledge, and emergent abilities beyond conventional language benchmarks.

Benchmark

BBH (BIG-bench Hard)

A 23-task subset of BIG-bench focused on challenging multi-step reasoning where chain-of-thought prompting yields large gains.

Benchmark

HellaSwag

A commonsense sentence-completion benchmark where models pick the most plausible continuation among adversarially generated distractors.

Benchmark

ARC (AI2 Reasoning Challenge)

A grade-school science question benchmark split into Easy and Challenge sets, the latter built from questions retrieval methods answer incorrectly.

Benchmark

TruthfulQA

A benchmark measuring whether models avoid generating false answers that mimic common human misconceptions and falsehoods.

Benchmark

GPQA (Graduate-Level Google-Proof Q&A)

A benchmark of expert-written, graduate-level science questions designed to be extremely hard even with web access, testing deep domain reasoning.

Benchmark

MMMU (Massive Multi-discipline Multimodal Understanding)

A multimodal benchmark of college-level questions requiring joint reasoning over text and images such as diagrams, charts, and figures.

Benchmark

MT-Bench

A multi-turn conversational benchmark where a strong LLM judge scores model responses across categories on a 1-10 quality scale.

Benchmark

Chatbot Arena

A live, crowdsourced evaluation where users compare two anonymous model responses and votes are aggregated into Elo-style rankings.

Benchmark

HELM (Holistic Evaluation of Language Models)

A standardized framework evaluating language models across many scenarios and multiple metrics including accuracy, robustness, fairness, and efficiency.

Benchmark

DROP (Discrete Reasoning Over Paragraphs)

A reading-comprehension benchmark requiring discrete operations like addition, counting, sorting, and comparison over passage content.

Benchmark

WinoGrande

A large-scale commonsense benchmark of pronoun-resolution sentence pairs designed to require world knowledge rather than lexical cues.

Benchmark

AGIEval

A benchmark built from human standardized exams such as college entrance, law, and civil-service tests to measure human-centric reasoning.

Benchmark

LiveCodeBench

A contamination-resistant coding benchmark that continuously collects new competitive-programming problems and evaluates by execution over time.

Benchmark

AIME (Competition Math Benchmark)

An olympiad-level math benchmark using American Invitational Mathematics Examination problems with integer answers, a key frontier reasoning test.

Benchmark

tau-bench (Tool-Agent-User Benchmark)

An agentic benchmark testing tool-using models in simulated customer-service dialogues that require following domain policies and calling APIs correctly.

Benchmark

Terminal-Bench

An agentic benchmark evaluating models on completing real command-line tasks inside a sandboxed terminal, verified by automated checks.

Benchmark

RULER (Long-Context Benchmark)

A synthetic long-context benchmark with configurable tasks measuring a model's effective context length beyond simple retrieval.

Benchmark

IFEval (Instruction-Following Eval)

A benchmark that measures whether LLMs follow precise, verifiable formatting and content instructions using automatic checks rather than subjective judgment.

Benchmark

SimpleQA

A factuality benchmark of short, fact-seeking questions with single verifiable answers, designed to measure how often LLMs are correct, wrong, or appropriately abstain.

Benchmark

LiveBench

A contamination-resistant benchmark that continuously refreshes questions from recent sources and grades automatically against objective ground truth across many task categories.

Benchmark

BigCodeBench

A code-generation benchmark for realistic programming tasks that require composing many library calls, evaluated with rigorous test suites and high branch coverage.

Benchmark

RewardBench

A benchmark for reward models and LLM judges that measures how well they prefer better responses over worse ones across chat, reasoning, safety, and refusal cases.

Benchmark

AlpacaEval

An automated LLM-as-judge benchmark that estimates a model's win rate against a reference model on open-ended instructions, with a length-controlled variant to reduce verbosity bias.

Benchmark

Arena-Hard

An automatic benchmark of challenging, real-user-derived prompts graded by an LLM judge, built to align closely with human preference rankings and separate strong models.

Benchmark

MMBench (Multimodal Benchmark)

A systematic multimodal benchmark that evaluates vision-language models across many fine-grained ability dimensions using a robustness-checked multiple-choice protocol.