Difference between revisions of "AI benchmarks"
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* [https://epoch.ai/ Epoch AI] | * [https://epoch.ai/ Epoch AI] | ||
** [https://epoch.ai/data/notable-ai-models Notable AI models] | ** [https://epoch.ai/data/notable-ai-models Notable AI models] | ||
| + | |||
| + | ==Lists of Benchmarks== | ||
| + | * 2025-05: [https://x.com/scaling01 Lisan al Gaib]: [https://x.com/scaling01/status/1919092778648408363 The Ultimate LLM Benchmark list] | ||
| + | ** [https://x.com/scaling01/status/1919217718420508782 Average across 28 benchmarks] | ||
| + | |||
| + | ==Analysis of Methods== | ||
| + | * 2025-04: [https://arxiv.org/abs/2504.20879 The Leaderboard Illusion] | ||
=Methods= | =Methods= | ||
| Line 30: | Line 37: | ||
==Software/Coding== | ==Software/Coding== | ||
* 2025-02: [https://arxiv.org/abs/2502.12115SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?] ([https://github.com/openai/SWELancer-Benchmark code]) | * 2025-02: [https://arxiv.org/abs/2502.12115SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?] ([https://github.com/openai/SWELancer-Benchmark code]) | ||
| + | |||
| + | ==Math== | ||
| + | * [https://www.vals.ai/benchmarks/aime-2025-03-13 AIME Benchmark] | ||
==Visual== | ==Visual== | ||
| Line 39: | Line 49: | ||
==Creativity== | ==Creativity== | ||
| + | * See also: [AI creativity] | ||
* 2024-10: [https://arxiv.org/abs/2410.04265 AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text] | * 2024-10: [https://arxiv.org/abs/2410.04265 AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text] | ||
* 2024-11: [https://openreview.net/pdf?id=fz969ahcvJ AidanBench: Evaluating Novel Idea Generation on Open-Ended Questions] ([https://github.com/aidanmclaughlin/AidanBench code]) | * 2024-11: [https://openreview.net/pdf?id=fz969ahcvJ AidanBench: Evaluating Novel Idea Generation on Open-Ended Questions] ([https://github.com/aidanmclaughlin/AidanBench code]) | ||
Latest revision as of 09:37, 28 May 2025
Contents
General
- Models Table (lifearchitect.ai)
- Artificial Analysis
- Epoch AI
Lists of Benchmarks
Analysis of Methods
- 2025-04: The Leaderboard Illusion
Methods
- AidanBench: Evaluating Novel Idea Generation on Open-Ended Questions (code)
- Leaderboard
- Suggestion to use Borda count
- 2025-04: add Quasar Alpha, Optimus Alpha, Llama-4 Scout and Llama-4 Maverick
- ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning. Assess reasoning using puzzles of tunable complexity.
Task Length
- 2020-09: Ajeya Cotra: Draft report on AI timelines
- 2025-03: Measuring AI Ability to Complete Long Tasks Measuring AI Ability to Complete Long Tasks
Assess Specific Attributes
Various
- LMSYS: Human preference ranking leaderboard
- Tracking AI: "IQ" leaderboard
- LiveBench: A Challenging, Contamination-Free LLM Benchmark
- LLM Thematic Generalization Benchmark
Hallucination
Software/Coding
Math
Visual
- 2024-06: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs (preprint, leaderboard)
- 2025-03: Can Large Vision Language Models Read Maps Like a Human? MapBench
Conversation
- 2025-01: MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs (project, code, leaderboard)
Creativity
- See also: [AI creativity]
- 2024-10: AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text
- 2024-11: AidanBench: Evaluating Novel Idea Generation on Open-Ended Questions (code)
- 2024-12: LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context
- LLM Creative Story-Writing Benchmark
Reasoning
- ENIGMAEVAL: "reasoning" leaderboard (paper)
- Sober Reasoning Leaderboard
Assistant/Agentic
- GAIA: a benchmark for General AI Assistants
- Galileo AI Agent Leaderboard
- Smolagents LLM Leaderboard: LLMs powering agents
- OpenAI PaperBench: Evaluating AI’s Ability to Replicate AI Research (paper, code)
Science
See: Science Benchmarks
