AI Dataset2026
Evaluates retrieval-augmented generation by decomposing user queries into sub-queries and answers into atomic claims, scoring retrieval by query coverage and generation by claim verifiability. Reference-free benchmark with 800 queries, inlined retrieved chunks, and answers from multiple RAG systems; runs locally without API keys.
AI Dataset2026
Kunal Handa, Miranda Zhang +22Anthropic, Social and Language Technologies (SALT) Lab, Stanford University +2
Provides aggregated, privacy-preserving cluster outputs from three external research teams' analyses of ~250k Claude/Claude Code conversations; includes per-team CSVs (Stanford, Oxford, METR) for studying human–AI collaboration and model behavior without raw conversations.