Fine-Tuning Large Language Models Using Entity Hallucination Index for Text Summarization

Summary

We propose a reinforcement-learning-based fine-tuning approach for large language models that uses the Enti Hallucination Index (EHI) as a reward signal to reduce entity-level hallucination in text summarization. Experiments on meeting transcripts show that this method improves entity faithfulness and accuracy.

Abstract

Recent advancements in large language models (LLMs) have led to notable improvements in abstractive summarization quality. However, hallucination - especially entity-level hallucination where non-existent or incorrect entities are introduced - remains a critical challenge. In this work, we propose a reward-driven fine-tuning framework for summarization models using the Entity Hallucination Index (EHI) as a guiding metric. The methodology here begins with generating initial summaries from pre-trained models such as Flan-T5, DistilBART, and Mistral (or other popular LLM) on structured transcript datasets, XSUM. We compute EHI by extracting named entities from both generated summaries and gold references, evaluating precision, and penalizing fabricated entities. The fine-tuning process is guided by reinforcement learning, where EHI serves as the reward signal. We adopt a REINFORCE-style update mechanism to optimize the summarization model towards maximizing entity faithfulness. Experiments demonstrate that models fine-tuned with EHI achieve lower hallucination rates without compromising informativeness. Furthermore, we show that EHI-guided models generalize better on out-of-domain summarization tasks, suggesting enhanced robustness. The approach here offers a practical direction for improving factuality in summarization, emphasizing the critical role of accurate entity representation.

Introduction

Abstractive summarization models, powered by large language models (LLMs), have achieved impressive results across various domains. However, a persistent challenge remains hallucination, where generated summaries include incorrect or fabricated information not grounded in the source input^1,2. In high-stakes applications such as meeting summarization, medical reporting, or financial documentation, hallucinations, particularly involving named entities, can significantly undermine trustworthiness and utility³.

Researchers conducted a large-scale....

Protocol

The objective of this study is to fine-tune a language model (LM) to generate summaries with reduced entity hallucination. Hallucinations occur when a model "confidently produces incorrect or irrelevant output" because it generates text according to statistical likelihood rather than factual verification. This is achieved by designing a reward function based on the Entity Hallucination Index (EHI) and applying reinforcement learning to maximize it.

Problem formulation
Given an input document X_i, the goal is to generate a summary Y_i such that entities mentioned in Y_i are grounded in X

Results

The experiments here focus on quantitative evidence for the impact that EHI-guided fine-tuning makes in reducing entity-level hallucinations across multiple model architectures and datasets. The results demonstrate consistent improvements in hallucination metrics while maintaining factual consistency and preserving question-answering capabilities.

Dataset performances
Dialogue dataset performance:
Table 5 presents the results for hallu.......

Discussion

QA-based metrics (e.g., FEQA/QuestEval/QAFactEval) and NLI-based metrics (e.g., FactCC/SummaC) require auxiliary QA/entailment components and often provide sentence-level judgments. In contrast, EHI is a lightweight, reference-free, entity-level score that (i) directly targets the primary failure mode we observe -- entity hallucination; (ii) is model-agnostic and fast to compute; and (iii) serves as a dense reward for RL fine-tuning. Empirically, EHI complements QA/NLI metrics by pinpointing which entities drive factuali.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Colab NVIDIA A100 GPU	NVIDIA	N/A	Google Colaboratory Pro
DistilBART (Encoder–Decoder)	Hugging Face	https://huggingface.co/sshleifer/distilbart-cnn-12-6
dslim/bert-base-NER	Hugging Face (dslim)	https://huggingface.co/dslim/bert-base-NER	Fine-tuned BERT base for English NER
Evaluation Models  QAGS (QA-based)	Salesforce Research	N/A	Factuality classifier
Evaluation Models (Factuality) FactCC	Google Research	N/A	Factuality classifier
Flan-T5 (Encoder–Decoder)	Google	https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5	open-source, text-to-text, large language model
Hugging Face Transformers	Hugging Face, Inc.	https://huggingface.co/docs/transformers/index	Model loading & fine-tuning
Nous-Hermes-2-Mistral-7B-DPO	Mistral	https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO	7 billion parameter Language model
Programming Language Python 3.10 (Colab runtime)	Python Software Foundation	Version 3.10	Core implementation
Streamlit	Open Source	https://streamlit.io/	Factuality classifier
XLSUM dataset	BUET	https://github.com/csebuetnlp/xl-sum
XSUM dataset	University of Edinburgh	https://github.com/EdinburghNLP/XSum