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Computer Science > Computation and Language

arXiv:2402.04315 (cs)
[Submitted on 6 Feb 2024]

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

Authors:Chengyu Huang, Zeqiu Wu, Yushi Hu, Wenya Wang
Download a PDF of the paper titled Training Language Models to
Generate Text with Citations via Fine-grained Rewards, by Chengyu
Huang and 3 other authors
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    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.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2402.04315 [cs.CL]
          (or arXiv:2402.04315v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2402.04315
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Chengyu Huang [view email]
[v1] Tue, 6 Feb 2024 19:00:40 UTC (7,723 KB)
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