Training Language Models to Generate Text with Citations via Fine-grained Rewards

TL;DR

This paper introduces a training framework with fine-grained rewards to improve LLMs in generating accurate, citation-supported responses, reducing hallucinations and enhancing credibility, especially for smaller models.

Contribution

It presents a novel reward-based training method that significantly improves citation generation and response correctness in LLMs, outperforming traditional training strategies.

Findings

Fine-grained rewards enhance citation relevance and support in LLMs.

The method outperforms baseline models, including GPT-3.5-turbo.

Improved performance demonstrated on ALCE and EXPERTQA datasets.

Abstract

While recent Large Language Models (LLMs) have proven useful in answering user queries, they are prone to hallucination, and their responses often lack credibility due to missing references to reliable sources. An intuitive solution to these issues would be to include in-text citations referring to external documents as evidence. While previous works have directly prompted LLMs to generate in-text citations, their performances are far from satisfactory, especially when it comes to smaller LLMs. In this work, we propose an effective training framework using fine-grained rewards to teach LLMs to generate highly supportive and relevant citations, while ensuring the correctness of their responses. We also conduct a systematic analysis of applying these fine-grained rewards to common LLM training strategies, demonstrating its advantage over conventional practices. We conduct extensive experiments on Question Answering (QA) datasets taken from the ALCE benchmark and validate the model's generalizability using EXPERTQA. On LLaMA-2-7B, the incorporation of fine-grained rewards achieves the best performance among the baselines, even surpassing that of GPT-3.5-turbo.