TA-RAG uses LLM-extracted time intervals, time-filtered retrieval with averaged temporal query embeddings, and chronologically ordered context to beat standard RAG by 13-27 points on the new ADQAB benchmark of 525 multiple-choice questions.
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Reading Between the Timelines: RAG for Answering Diachronic Questions
TA-RAG uses LLM-extracted time intervals, time-filtered retrieval with averaged temporal query embeddings, and chronologically ordered context to beat standard RAG by 13-27 points on the new ADQAB benchmark of 525 multiple-choice questions.