Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering
Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversational memory question answering. However, existing methods suffer from two key challenges: (1) fragmented evidence scattered across temporally distant sessions, and (2) noisy content within retrieved sessions that triggers the lost-in-the-middle effect. To address these challenges, we propose MemLoc, a unified Retrieve-Localize-Generate framework for long-term conversational memory QA. For retrieval, MemLoc decomposes each session into multi-granularity memory units and performs query routing via an inner-memory gr...
| Authors | Yifan Wang, Xinkui Lin, Yongxiu Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 September 2026 |
| Last updated at source | 14 September 2026 |
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