Self-Recall Thinking Framework Improves Multi-Turn Dialogue Consistency
A novel approach known as Self-Recall Thinking (SRT) has been developed to improve the consistency of multi-turn dialogue systems that utilize large language models (LLMs). This method tackles the difficulty of managing dependencies in non-adjacent dialogue turns, where crucial information can become obscured by irrelevant context as discussions progress. Current techniques often face issues with high latency in external memory or the loss of intricate details through repeated summarization. SRT effectively identifies relevant historical turns to craft contextually suitable responses, facilitating selective recall and reasoning during inference. This method fosters an internal reasoning pathway without relying on external memory, enhancing both scalability and consistency. The research can be found on arXiv with the ID 2605.15102.
Key facts
- SRT stands for Self-Recall Thinking.
- The framework addresses long-range contextual dependency in multi-turn dialogue.
- SRT identifies helpful historical turns for generating responses.
- It avoids high latency external memory and iterative summarization.
- The approach yields an endogenous reasoning process.
- The paper is published on arXiv with ID 2605.15102.
- The announcement type is cross.
- The framework targets consistency and scalability in LLM-based dialogue systems.
Entities
Institutions
- arXiv