GoKawiilGoKawiil's Jev reranker tested with LLM-generated relevance rubrics
GoKawiil describes an experimental reranking technique for its Jev model that decomposes a search query into a set of yes/no relevance questions generated by an LLM, rather than asking a single relevance question. Each question is scored by Jev for the probability of a 'yes' answer, and these probabilities are summed to produce an overall relevance score for a document against the query.