Skill-Details
scientific-llm-benchmarks
Useful for evaluating scientific ML systems, not general DS workflows.
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SKILL.md
Dieser Auszug wurde bei der Prüfung gespeichert. Die externe Quelle enthält die vollständige und aktuelle Version.
--- name: scientific-llm-benchmarks description: "A comprehensive reference of benchmarks for evaluating large language models on scientific reasoning and discovery." compatibility: ">" allowed-tools: Bash Read Write Edit Glob Grep WebFetch metadata: tags: scientific-llm-benchmarks, llm-benchmarks, science-llms, evaluation, awesome-list, reasoning, discovery, stem version: 1.0.0 source: "https://github.com/subinium/Awesome-Scientific-LLM-Benchmarks" license: MIT --- # Scientific LLM Benchmarks This skill provides references to benchmarks used for evaluating large language models on scientific reasoning and discovery. The data comes from the Awesome-Scientific-LLM-Benchmarks repository. ## Contents The complete benchmark list is stored locally within this skill: - **References List:** `references/benchmarks.md` - **Data (YAML format):** `data/benchmarks.yaml` ## Benchmark Domains Covered - **General / Multi-domain Science:** Cross-disciplinary STEM reasoning benchmarks. - **Mathematics:** Arithmetic, competition, olympiad, and frontier / formal-proof mathematics. - **Physics and Astronomy:** Physics olympiad, graduate physics, computational physics, and astronomy. - **Chemistry:** Molecular property, reaction, retrosynthesis, safety, and chemical knowledge. - **Materials Science:** Crystals, materials property prediction, and materials-science knowledge. - **Biology and Life Sciences:** Genomics, proteins, bioinformatics agents, protocols, and research biology. - **Agentic Science and AI Research:** LLM agents that write research code, run data analyses, attempt autonomous discovery, and conduct ML/AI research. ## Helper Scripts Also included are python scripts inside `scripts/`: - `generate_readme.py`: Regenerates the markdown tables and list from `data/benchmarks.yaml`. - `fetch_examples.py`: Fetches real sample rows from HuggingFace dataset URLs specified in the dataset metadata.Vollständige Quelle auf GitHub lesen (öffnet externe Seite)