Learning to Generate Answers with Citations via Factual Consistency Models

TL;DR

This paper introduces a weakly-supervised fine-tuning method using factual consistency models to improve citation accuracy in language models, significantly reducing factual errors and enhancing verifiability of generated answers.

Contribution

The paper presents a novel weakly-supervised fine-tuning approach that leverages factual consistency models to improve citation accuracy in language models.

Findings

Achieves 34.1 citation F1 point improvement on ALCE benchmark.

Demonstrates robust transfer of citation generation to unseen datasets.

Reduces factual error rate in generated answers.

Abstract

Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. One approach to address this issue is to provide citations to relevant sources alongside generated content, enhancing the verifiability of generations. However, citing passages accurately in answers remains a substantial challenge. This paper proposes a weakly-supervised fine-tuning method leveraging factual consistency models (FCMs). Our approach alternates between generating texts with citations and supervised fine-tuning with FCM-filtered citation data. Focused learning is integrated into the objective, directing the fine-tuning process to emphasise the factual unit tokens, as measured by an FCM. Results on the ALCE few-shot citation benchmark with various instruction-tuned LLMs demonstrate superior performance compared to in-context learning, vanilla supervised fine-tuning, and state-of-the-art methods, with an average improvement of $34.1$, $15.5$, and $10.5$ citation F$_1$ points, respectively. Moreover, in a domain transfer setting we show that the obtained citation generation ability robustly transfers to unseen datasets. Notably, our citation improvements contribute to the lowest factual error rate across baselines.