Retrieval eval datasets are error-prone; LLM relabeling proposed as fix
CShorten30 · x · 2026-09-20
A discussion in the retrieval community flags quality issues in existing evaluation datasets: human labels contain mistakes that can mislead benchmarks. The suggestion is to actively relabel current retrieval eval datasets with LLMs and build newer, better ones, have LLMs review misaligned pairs, and manually eyeball sample pairs as a recommended sanity check.
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