Enhancing User Engagement in Socially-Driven Dialogue through Interactive LLM Alignments

• URL: http://arxiv.org/abs/2506.21497v1
• Date: Thu, 26 Jun 2025 17:26:17 GMT
• Title: Enhancing User Engagement in Socially-Driven Dialogue through Interactive LLM Alignments
• Authors: Jiashuo Wang, Kaitao Song, Chunpu Xu, Changhe Song, Yang Xiao, Dongsheng Li, Lili Qiu, Wenjie Li,
• Abstract summary: Enhancing user engagement through interactions plays an essential role in socially-driven dialogues.<n>We enable interactive LLMs to learn user engagement by leveraging signals from the future development of conversations.<n>Experiments conducted on two socially-driven dialogue scenarios demonstrate that our method effectively enhances user engagement in interactive LLMs.
• Score: 36.632855175566746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enhancing user engagement through interactions plays an essential role in socially-driven dialogues. While prior works have optimized models to reason over relevant knowledge or plan a dialogue act flow, the relationship between user engagement and knowledge or dialogue acts is subtle and does not guarantee user engagement in socially-driven dialogues. To this end, we enable interactive LLMs to learn user engagement by leveraging signals from the future development of conversations. Specifically, we adopt a more direct and relevant indicator of user engagement, i.e., the user's reaction related to dialogue intention after the interaction, as a reward to align interactive LLMs. To achieve this, we develop a user simulator to interact with target interactive LLMs and explore interactions between the user and the interactive LLM system via \textit{i$\times$MCTS} (\textit{M}onte \textit{C}arlo \textit{T}ree \textit{S}earch for \textit{i}nteraction). In this way, we collect a dataset containing pairs of higher and lower-quality experiences using \textit{i$\times$MCTS}, and align interactive LLMs for high-level user engagement by direct preference optimization (DPO) accordingly. Experiments conducted on two socially-driven dialogue scenarios (emotional support conversations and persuasion for good) demonstrate that our method effectively enhances user engagement in interactive LLMs.

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