Summary of Chatasu: Evoking Llm’s Reflexion to Truly Understand Aspect Sentiment in Dialogues, by Yiding Liu and Jingjing Wang and Jiamin Luo and Tao Zeng and Guodong Zhou
ChatASU: Evoking LLM’s Reflexion to Truly Understand Aspect Sentiment in Dialogues
by Yiding Liu, Jingjing Wang, Jiamin Luo, Tao Zeng, Guodong Zhou
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes a new task called Chat-based Aspect Sentiment Understanding (ChatASU) that leverages large language models (LLMs) to understand aspect sentiments in dialogue scenarios. The task is designed to address the limitation of existing studies on interactive ASU, which ignore the coreference issue for opinion targets. To tackle this challenge, the paper introduces an auxiliary sub-task called Aspect Chain Reasoning (ACR), which aims to reason about aspect relationships. A Trusted Self-reflexion Approach (TSA) is proposed as a backbone to ChatASU, treating ACR as an auxiliary task and incorporating trusted learning into reflexion mechanisms to alleviate factual hallucination problems. Experimental results demonstrate the effectiveness of TSA in significantly outperforming state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how computers can better understand people’s opinions on different topics in conversations. It creates a new task called ChatASU that uses large language models to understand these opinions in dialogue scenarios. The challenge is that existing studies ignore the issue of linking related opinions together, which is important for understanding sentiments. To address this, the paper introduces a sub-task that reasons about relationships between opinions. A proposed approach, TSA, treats this sub-task as an auxiliary task and improves its performance by using trusted learning mechanisms to reduce hallucination errors. The results show that this approach outperforms existing methods. |
Keywords
» Artificial intelligence » Coreference » Hallucination
