Learning Fine-Grained Grounded Citations for Attributed Large Language Models
<|reference_start|>Learning Fine-Grained Grounded Citations for Attributed Large Language Models: Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, have shown potential in mitigating hallucinations and improving verifiability. However, current approaches suffer from suboptimal citation quality due to their reliance on in-context learning. Furthermore, the practice of citing only coarse document identifiers makes it challenging for users to perform fine-grained verification. In this work, we introduce FRONT, a training framework designed to teach LLMs to generate Fine-Grained Grounded Citations. By grounding model outputs in fine-grained supporting quotes, these quotes guide the generation of grounded and consistent responses, not only improving citation quality but also facilitating fine-grained verification. Experiments on the ALCE benchmark demonstrate the efficacy of FRONT in generating superior grounded responses and highly supportive citations. With LLaMA-2-7B, the framework significantly outperforms all the baselines, achieving an average of 14.21% improvement in citation quality across all datasets, even surpassing ChatGPT.<|reference_end|>
@article{huang2024learning,
    title={Learning Fine-Grained Grounded Citations for Attributed Large Language
  Models},
    author={Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong,
  Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin},
    journal={arXiv preprint arXiv:2408.04568},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04568},
    primaryClass={cs.CL cs.AI}
}