Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence
Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries.
ProofPaper ↗
Key points
- Most embedding-based retrievers compare single-vector representations of queries and memories, even when relevance depends on a small part of the content.
- Such local evidence can be matched to queries using the contextual token representations already computed by the same embedding models.
- Our method uses sparse token matching to improve retrieval without additional training.
- These retrieval gains translate into higher question-answering accuracy on long-term memory benchmarks.
Sources (1)
- [1]Beyond the Final Embedding: Retrieving Memories from Token-Level EvidencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 09:27 AM
Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries.
Most embedding-based retrievers compare single-vector representations of queries and memories, even when relevance depends on a small part of the content.
Extractive summary: sentences quoted from the sources.
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