@inproceedings{huang-etal-2026-improving,
    title = "Improving the Faithfulness of {LLM}-based Abstractive Summarization with Span-level Unlikelihood Training",
    author = "Huang, Sicong  and
      Yan, Qianqi  and
      Wang, Shengze  and
      Lane, Ian",
    editor = "Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Krishna, Satyapriya  and
      Das, Anubrata  and
      Dhamala, Jwala  and
      Cao, Yang Trista  and
      Kumarage, Tharindu  and
      Ramakrishna, Anil  and
      Christodoulopoulos, Christos  and
      Wan, Yixin  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.trustnlp-main.28/",
    pages = "425--438",
    ISBN = "979-8-89176-418-7",
    abstract = "Abstractive summarization using large language models (LLMs) has become an essential tool for condensing information. Despite their ability to generate fluent summaries, these models often produce texts that are unfaithful to the original documents, manifested through hallucinations of specific words, phrases, or concepts. Current approaches to mitigating unfaithfulness typically involve post-processing corrections or contrastive learning from synthetically generated negative samples, which do not fully address the spectrum of errors that can arise in LLM-generated summaries. In this paper, we introduce a novel approach to fine-tune LLMs specifically to reduce the occurrence of unfaithful spans of text in generated summaries. We first annotate span-level hallucinations in LLM-generated summaries using automatic labeling with GPT-4. We then fine-tune the LLM using both summaries with no hallucinations and spans of hallucinated text to improve the faithfulness of the model. This paper introduces a dataset labeled to distinguish between faithful and unfaithful content and compare the performance of three techniques: gradient ascent, unlikelihood training, and task vector negation. Our experimental results show that unlikelihood training can effectively use span-level annotations to enhance summary faithfulness, reducing the number of summaries with hallucinations from 31{\%} to 13{\%}, a reduction of 58{\%} on the CNN summarization dataset and from 33{\%} to 20{\%}, a reduction of 39{\%} on the SAMSum dataset."
}