Summary of Elicitationgpt: Text Elicitation Mechanisms Via Language Models, by Yifan Wu et al.
ElicitationGPT: Text Elicitation Mechanisms via Language Models
by Yifan Wu, Jason Hartline
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); 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 Machine learning models that generate probabilistic forecasts are crucial components in incentivized elicitation of information and training of machine learning models. This paper proposes novel mechanisms for scoring text-based forecasts against ground truth text, leveraging domain-knowledge-free queries to large language models like ChatGPT. The proposed methods are evaluated empirically on peer reviews from a peer-grading dataset, with results compared to manual instructor scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to score predictions made by big language models. It shows that we can use special questions to ask these models what they think about certain text, and then compare their answers to what real people think is correct. The researchers tested this idea using reviews written by students, and found that the model’s scores matched up pretty well with how human teachers would grade them. |
Keywords
* Artificial intelligence * Machine learning
