  Personalizing conversational agents can enhance the quality of conversations
and increase user engagement. However, they often lack external knowledge to
appropriately tend to a user's persona. This is particularly crucial for
practical applications like mental health support, nutrition planning,
culturally sensitive conversations, or reducing toxic behavior in
conversational agents. To enhance the relevance and comprehensiveness of
personalized responses, we propose using a two-step approach that involves (1)
selectively integrating user personas and (2) contextualizing the response with
supplementing information from a background knowledge source. We develop K-PERM
(Knowledge-guided PErsonalization with Reward Modulation), a dynamic
conversational agent that combines these elements. K-PERM achieves
state-of-the-art performance on the popular FoCus dataset, containing
real-world personalized conversations concerning global landmarks. We show that
using responses from K-PERM can improve performance in state-of-the-art LLMs
(GPT 3.5) by 10.5%, highlighting the impact of K-PERM for personalizing
chatbots.
