Chemistry retrieval model Valence targets near-miss passages: 81.8% hit rate in-field, versus 2% to 4.5% for general models
Albert released Valence, a chemistry-native retrieval model for picking the passage that actually answers a formulation-science question. General-purpose retrieval models sort broadly across fields but struggle to separate same-field passages that share vocabulary and classifications yet answer different questions; on FormBench's hard test, general models from OpenAI and Voyage found the correct passage in their top 100 results 2% to 4.5% of the time, while Valence did so 81.8% of the time. On a stricter measure, Valence scored 0.41 against 0.009 for the leading general-purpose model.