Anthropic Evals
https://github.com/anthropics/evals- Category
- AI Tools
- Rank
- No. 1278Tools index
Previous survey · No. 1285 ·
- Listed in
- #5 Evaluate an LLM application
- Pricing
- Open Source
- Type
- TOOL
- Builder
- anthropics
- GitHub
- 426 stars
- Date
About
Public evaluation suite from Anthropic. Reference tasks and frameworks for benchmarking Claude and other models.
What it does
A collection of model-written question sets for probing behavioral tendencies in dialogue agents. Each example pairs a prompt with answers that match or oppose a target behavior, sometimes adding classifier confidence. Collections cover persona, sycophancy, advanced AI risk, and gender bias.
Why it's ranked here
This is valuable research data, but a narrow tool. It offers unusually explicit behavioral labels and documented prompt conventions. However, the supplied repository material describes datasets rather than a ready-made evaluation runner, so users must build model adapters, scoring, and reporting themselves.
What's good
The labels make scoring intent concrete: each item identifies both matching and opposing answers. Persona examples include label confidence. Advanced AI risk collections pair language-model-generated questions with crowdworker-generated counterparts, supporting useful comparisons between synthetic and human-authored evaluation data.
Tradeoffs
The dialogue prompt format targets Anthropic's models and explicitly requires adaptation elsewhere. Many shown persona items are close paraphrases, which may reduce linguistic diversity. Scoring assumes probabilities for short binary completions. Some datasets contain biases, stereotypes, and potentially harmful or offensive material.
How to use it well
Use it for behavioral research on dialogue models, especially when comparing persona, sycophancy, risk-related tendencies, or gender bias. Ingest the JSONL records, translate prompts into your model's chat format, and score the labeled alternatives. It does not replace an evaluation runner, broad capability suite, or results dashboard.
Technical notes+
README.md defines four dataset collections and points readers to the associated paper for generation, validation, and analysis details. persona/README.md specifies JSONL records containing question, statement, answer_matching_behavior, answer_not_matching_behavior, and label_confidence; persona/openness.jsonl and persona/narcissism.jsonl demonstrate that schema. advanced-ai-risk/README.md describes paired human-generated and LM-generated datasets, A/B answer labels, few-shot generation from five randomly selected gold examples, and probability-based completion scoring. LICENSE contains the Creative Commons Attribution 4.0 International license.
Observed
- License
- Creative Commons Attribution 4.0 International
- Data format
- Datasets are stored as JSONL records.
- Interface
- Repository text presents datasets for direct ingestion; it describes no CLI, library API, MCP server, or web API.
- Dataset scope
- Collections cover persona, sycophancy, advanced AI risk, and Winogender-style gender bias evaluation.
- Model support
- Designed for dialogue agents, with adaptation described as possible for other model types.
- Comparison data
- Advanced AI risk behaviors include both crowdworker-generated and language-model-generated datasets.
Read from README.md, LICENSE, persona/README.md, persona/openness.jsonl, persona/narcissism.jsonl, persona/neuroticism.jsonl, persona/psychopathy.jsonl, persona/risk-averse.jsonl, advanced-ai-risk/README.md, persona/extraversion.jsonl.
What it can do
Benchmark Claude model performance
Claude model and evaluation tasks → Performance metrics and scores
Benchmark other AI models
AI model and evaluation tasks → Performance metrics and scores
Run reference evaluation tasks
AI model and standardized task prompts → Task completion results and accuracy scores
Execute evaluation frameworks
AI model and evaluation framework parameters → Structured evaluation reports
Compare model performance
Multiple AI models and evaluation criteria → Comparative performance analysis
Generate evaluation reports
Model test results and evaluation data → Formatted evaluation reports
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