Summary of An Llm Feature-based Framework For Dialogue Constructiveness Assessment, by Lexin Zhou et al.
An LLM Feature-based Framework for Dialogue Constructiveness Assessment
by Lexin Zhou, Youmna Farag, Andreas Vlachos
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper focuses on developing an approach to assess the constructiveness of dialogues in various scenarios, such as debates or persuasive conversations. The proposed framework combines strengths from feature-based and neural approaches, while addressing their limitations. Specifically, it defines a set of interpretable linguistic features that can be extracted using both pre-trained language models (LLMs) and simple heuristics. These features are then used to train LLM feature-based models for dialogue constructiveness assessment. The paper evaluates the proposed framework on three datasets and demonstrates its effectiveness in outperforming or matching standard feature-based models and neural models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop a way to measure how well people agree or disagree with each other during conversations. The goal is to create a system that can predict whether a conversation will be productive, respectful, or persuasive. To achieve this, the researchers propose a new approach that combines two existing methods. They define specific features of language that are important for understanding dialogue constructiveness and use these features to train their model. The results show that this new approach works well on three different datasets. |
