Improving Multi-turn Dialogue Consistency with Self-Recall Thinking
Large language model (LLM) based multi-turn dialogue systems often struggle to track dependencies across non-adjacent turns, undermining both consistency and scalability. As conversations lengthen, essential information becomes sparse and is buried in irrelevant context, while processing the entire dialogue history incurs severe efficiency bottlenecks. Existing solutions either rely on high latency external memory or lose fine-grained details through iterative summarization. In this paper, we propose Self-Recall Thinking (SRT), a framework designed to address long-range contextual dependency and sparse informative signals in multi-turn dialogue. SRT identifies helpful historical turns and...
| Authors | Renning Pang, Tian Lan, Leyuan Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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