Enhancing LLMs' Knowledge Integration in Dialogue Generation with Entity Anonymization

This research explores a practical method to improve the performance of Large Language Models (LLMs) in dialogue generation. The proposed entity anonymization technique addresses a key challenge in integrating external knowledge into LLM responses.

• •Entity anonymization is employed to improve LLMs' use of external knowledge.
• •The research aims to enhance the factual accuracy and coherence of dialogue responses.
• •The findings likely offer insights into building more reliable conversational AI systems.