Summary of Eliciting Informative Text Evaluations with Large Language Models, by Yuxuan Lu et al.
Eliciting Informative Text Evaluations with Large Language Models
by Yuxuan Lu, Shengwei Xu, Yichi Zhang, Yuqing Kong, Grant Schoenebeck
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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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 paper proposes a novel approach to motivate high-quality feedback through peer prediction mechanisms, offering provable guarantees. Building upon recent advancements in large language models, the researchers aim to expand these techniques from simple scalar or multiple-choice reports to the broader domain of text-based reports. This increased applicability has significant implications for various feedback channels, including peer reviews, e-commerce customer reviews, and social media comments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to make it easier for people to give good feedback by using big language models. Right now, most methods only work for simple things like multiple-choice questions or numbers. But what about when we want people to write longer messages? That’s a much harder problem! The researchers are trying to solve this by taking ideas from the latest advancements in language models and applying them to text-based feedback. This could make it easier and more useful for people to leave comments on social media, review products, or give feedback on academic papers. |
