Summary of Prove Your Point!: Bringing Proof-enhancement Principles to Argumentative Essay Generation, by Ruiyu Xiao et al.
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation
by Ruiyu Xiao, Lei Wu, Yuhang Gou, Weinan Zhang, Ting Liu
First submitted to arxiv on: 30 Oct 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 presents a two-stage framework, called Proof-Enhancement and Self-Annotation (PESA), for generating argumentative essays on controversial topics. Current methods can generate individual opinions but often fail to connect them logically, leading to confused or contradictory results. PESA addresses this by introducing pseudo-labels for logical information, claims, and grounds using a large language model. The framework then employs a tree planning approach that incorporates proof principles to ensure logical consistency. Experimental results show that PESA outperforms strong baseline models in generating essays with better logical validity and persuasiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers write persuasive essays on hot topics, like politics or social issues. Right now, computers can generate opinions but often don’t connect them well, making the results confusing or hard to understand. The new system, called PESA, fixes this by adding a layer of logic and consistency to the computer’s writing. It does this by using a big language model to identify key points and then organizing those points in a logical way. By doing so, PESA makes essays that are more convincing and easier to follow than what computers can do on their own. |
Keywords
» Artificial intelligence » Language model » Large language model
