  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.
