An LLM Feature-based Framework for Dialogue Constructiveness Assessment

• URL: http://arxiv.org/abs/2406.14760v2
• Date: Wed, 02 Oct 2024 11:03:16 GMT
• Title: An LLM Feature-based Framework for Dialogue Constructiveness Assessment
• Authors: Lexin Zhou, Youmna Farag, Andreas Vlachos,
• Abstract summary: Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructiveness outcomes following dialogues for such use cases. These objectives can be achieved by training either interpretable feature-based models or neural models such as pre-trained language models. We propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature-based and neural approaches.
• Score: 8.87747076871578
• License:
• Abstract: Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructiveness outcomes following dialogues for such use cases. These objectives can be achieved by training either interpretable feature-based models (which often involve costly human annotations) or neural models such as pre-trained language models (which have empirically shown higher task accuracy but lack interpretability). In this paper we propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature-based and neural approaches, while mitigating their downsides. The framework first defines a set of dataset-independent and interpretable linguistic features, which can be extracted by both prompting an LLM and simple heuristics. Such features are then used to train LLM feature-based models. We apply this framework to three datasets of dialogue constructiveness and find that our LLM feature-based models outperform or performs at least as well as standard feature-based models and neural models. We also find that the LLM feature-based model learns more robust prediction rules instead of relying on superficial shortcuts, which often trouble neural models.

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