ElicitationGPT: Text Elicitation Mechanisms via Language Models

• URL: http://arxiv.org/abs/2406.09363v2
• Date: Wed, 19 Jun 2024 00:12:35 GMT
• Title: ElicitationGPT: Text Elicitation Mechanisms via Language Models
• Authors: Yifan Wu, Jason Hartline,
• Abstract summary: This paper develops mechanisms for scoring elicited text against ground truth text using domain-knowledge-free queries to a large language model. An empirical evaluation is conducted on peer reviews from a peer-grading dataset and in comparison to manual instructor scores for the peer reviews.
• Score: 12.945581341789431
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scoring rules evaluate probabilistic forecasts of an unknown state against the realized state and are a fundamental building block in the incentivized elicitation of information and the training of machine learning models. This paper develops mechanisms for scoring elicited text against ground truth text using domain-knowledge-free queries to a large language model (specifically ChatGPT) and empirically evaluates their alignment with human preferences. The empirical evaluation is conducted on peer reviews from a peer-grading dataset and in comparison to manual instructor scores for the peer reviews.

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