A statistical approach to model evaluations \ Anthropic

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Evaluations A statistical approach to model evaluations Nov 19, 2024 Read the paper Suppose an AI model outperforms another model on a benchmark of interest—testing its general knowledge, for example, or its ability to solve computer-coding questions. Is the difference in capabilities real, or could one model simply have gotten lucky in the choice of questions on the benchmark? With the amount of public interest in AI model evaluations—informally called “evals”—this question remains surprisingly understudied among the AI research community. This month, we published a new research paper that at

saved by

• Anson Yu
• Tazik Sh
• Ratan Kaliani
• Akira Yoshiyama
• libby boellhoff
• Mersad Abbasi

related reading

• [2411.00640] Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluationsarxiv.org
• Applying Statistics to LLM Evaluationscameronrwolfe.substack.com
• Quantifying infrastructure noise in agentic coding evals \ Anthropicanthropic.com
• Demystifying evals for AI agents \ Anthropicanthropic.com
• f514cec81cb148559cf475e7426eed5e-Paper.pdfproceedings.neurips.cc
• Challenges in evaluating AI systems \ Anthropicanthropic.com
• [2412.00543] Evaluating the Consistency of LLM Evaluatorsarxiv.org
• Recommendations for Technical AI Safety Research Directionsalignment.anthropic.com
• Automated Alignment Researchers: Using large language models to scale scalable oversight \ Anthropicanthropic.com
• Core views on AI safety: When, why, what, and how \ Anthropicanthropic.com
• Discovering Language Model Behaviors with Model-Written Evaluations — LessWronglesswrong.com
• We need a Science of Evals — AI Alignment Forumalignmentforum.org