Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring

TL;DR

This paper introduces a novel framework that uses thought trees and preference optimization to generate faithful rationales for science question scoring, achieving performance comparable to black-box classifiers and improving assessment accuracy.

Contribution

It presents a new method combining thought trees and synthetic data for calibrating LLMs, enhancing rationale quality and scoring accuracy.

Findings

38% improvement in assessment performance (QWK score)

Higher-quality rationales verified by humans and LLMs

Effective use of synthetic preference data from thought trees

Abstract

Generating rationales that justify scoring decisions has been a promising way to facilitate explainability in automated scoring systems. However, existing methods do not match the accuracy of classifier-based methods. Plus, the generated rationales often contain hallucinated information. To address these issues, we propose a novel framework capable of generating more faithful rationales and, more importantly, matching performance with classifier-based black-box scoring systems. We first mimic the human assessment process by querying Large Language Models (LLMs) to generate a thought tree. We then summarise intermediate assessment decisions from each thought tree path for creating synthetic rationale data and rationale preference data. Finally, we utilise the generated synthetic data to calibrate LLMs through a two-step training process: supervised fine-tuning and preference optimization. Extensive experimental results demonstrate that our framework achieves a 38% assessment performance improvement in the QWK score compared to prior work while producing higher-quality rationales, as recognised by human evaluators and LLMs. Our work sheds light on the effectiveness of performing preference optimization using synthetic preference data obtained from thought tree paths. Data and code are available at https://github.com/lijiazheng99/thought_tree_assessment.