# Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent

**Authors:** Junfeng Liu, Zhuocheng Mei, Kewen Peng, Ranga Raju Vatsavai  
**arXiv:** [2312.00774](https://arxiv.org/abs/2312.00774)  
**Date:** 2023-12-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.00774v1/

## Key points

- Conversational agents leveraging AI, particularly deep learning, are prevalent in academic research and real-world applications.
- Challenges faced by conversational agents include disregarding knowledge and facts, lacking personalization to user preferences, and requiring significant computational resources.
- Recent research has focused on supplementing conversational agents with auxiliary information to address these issues.
- The paper titled "Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent" proposes a novel method called PK-NCLI.
- PK-NCLI accurately and efficiently identifies relevant auxiliary information to improve the quality of conversational responses.
- Experimental results show that PK-NCLI outperforms the state-of-the-art method PK-FoCus in terms of perplexity, knowledge grounding, and training efficiency.
- PK-NCLI maintains a similar level of persona grounding performance.
- The authors provide a detailed analysis of factors such as language model choices and trade-offs on training weights that impact the performance of PK-NCLI.
- This paper presents an innovative approach that effectively leverages relevant auxiliary information to enhance conversational agent performance.

## Summary

Conversational agents leveraging AI, particularly deep learning, are becoming increasingly prevalent in academic research and real-world applications. However, these agents still encounter challenges such as disregarding knowledge and facts, lacking personalization to user preferences, and requiring significant computational resources during training and inference. To address these issues, recent research has focused on supplementing conversational agents with auxiliary information. However, current methods struggle to effectively utilize this supplementary data to enhance the performance of conversational agents and the language models they employ. In this paper titled "Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent," authors Junfeng Liu, Zhuocheng Mei, Kewen Peng, and Ranga Raju Vatsavai propose a novel method called PK-NCLI. This approach accurately and efficiently identifies relevant auxiliary information to improve the quality of conversational responses by learning the relevance among persona (user profile), chat history, and knowledge background through low-level normalized contextual latent interaction. Experimental results demonstrate that PK-NCLI outperforms the state-of-the-art method PK-FoCus in terms of perplexity (by 47.80%), knowledge grounding (by 30.61%), and training efficiency (by 24.14%). Additionally, PK-NCLI maintains a similar level of persona grounding performance. The authors also provide a detailed analysis of how different factors such as language model choices and trade-offs on training weights impact the performance of PK-NCLI. Overall, this paper presents an innovative approach that effectively leverages relevant auxiliary information to enhance conversational agent performance. The findings contribute to addressing existing challenges in conversational AI systems while providing insights into optimizing language model choices for improved outcomes.