Debiasing LLMs by Fine-tuning

TL;DR

This paper introduces a supervised fine-tuning method using Low-Rank Adaptation to reduce extrapolation bias in large language models, demonstrating effectiveness in forecasting and stock return prediction.

Contribution

It presents a novel fine-tuning approach that intervenes at the parameter level to mitigate bias, outperforming prompt-based methods in real-world tasks.

Findings

Fine-tuning corrects extrapolation bias out-of-sample.

Method is low-cost and generalizable.

Effective in forecasting and stock return prediction.

Abstract

Prior research shows that large language models (LLMs) exhibit systematic extrapolation bias when forming predictions from both experimental and real-world data, and that prompt-based approaches appear limited in alleviating this bias. We propose a supervised fine-tuning (SFT) approach that uses Low-Rank Adaptation (LoRA) to train off-the-shelf LLMs on instruction datasets constructed from rational benchmark forecasts. By intervening at the parameter level, SFT changes how LLMs map observed information into forecasts and thereby mitigates extrapolation bias. We evaluate the fine-tuned model in two settings: controlled forecasting experiments and cross-sectional stock return prediction. In both settings, fine-tuning corrects the extrapolative bias out-of-sample, establishing a low-cost and generalizable method for debiasing LLMs.