Efficient and Accurate Memorable Conversation Model using DPO based on sLLM
This addresses the challenge of memory accumulation in dialog systems for applications requiring continuous session updates, though it appears incremental as it builds on existing methods like SFT and DPO.
The paper tackles the problem of efficiently managing memory in multi-session dialog systems to improve inference accuracy, resulting in improvements such as a 0.0591 increase in BERTScore for memory accuracy and gains in fluency, coherence, and consistency.
In multi-session dialog system, it is essential to continuously update the memory as the session progresses. Simply accumulating memory can make it difficult to focus on the content of the conversation for inference due to the limited input sentence size. Therefore, efficient and accurate conversation model that is capable of managing memory to reflect the conversation history continuously is necessary. This paper presents a conversation model that efficiently manages memory as sessions progress and incorporates this into the model to reflect the conversation history accurately with 3 methodologies: SFT, DPO and DPO with SFT model. Our model using DPO algorithm shows an improvement about 0.0591 of BERTScore in memory accuracy, and the rate of responses reflecting the memory increased as well. Also, response generation performance enhanced about 4.292 in fluency, 3.935 in coherence, and 2.896 in consistency. This paper describes a training method that yields better performance than models with more than twice the parameter size, even when the model size is smaller. Thus, our model demonstrates efficiency not only in terms of accuracy but also in resource utilization.