@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Improving the Faithfulness of LLM-based Abstractive Summarization with Span-level Unlikelihood Training
%A Huang, Sicong
%A Yan, Qianqi
%A Wang, Shengze
%A Lane, Ian
%Y Chang, Kai-Wei
%Y Mehrabi, Ninareh
%Y Krishna, Satyapriya
%Y Das, Anubrata
%Y Dhamala, Jwala
%Y Cao, Yang Trista
%Y Kumarage, Tharindu
%Y Ramakrishna, Anil
%Y Christodoulopoulos, Christos
%Y Wan, Yixin
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%S Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-418-7
%F huang-etal-2026-improving
%X 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.
%U https://aclanthology.org/2026.trustnlp-main.28/
%P 425-438
Markdown (Informal)
[Improving the Faithfulness of LLM-based Abstractive Summarization with Span-level Unlikelihood Training](https://aclanthology.org/2026.trustnlp-main.28/) (Huang et al., TrustNLP 2026)
ACL