Hallucination Diversity-Aware Active Learning for Text Summarization (2024)

Abstract: Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallucinations typically require costly human annotations to identify and correct hallucinations in LLM outputs. Moreover, most of these methods focus on a specific type of hallucination, e.g., entity or token errors, which limits their effectiveness in addressing various types of hallucinations exhibited in LLM outputs. To our best knowledge, in this paper we propose the first active learning framework to alleviate LLM hallucinations, reducing costly human annotations of hallucination needed. By measuring fine-grained hallucinations from errors in semantic frame, discourse and content verifiability in text summarization, we propose HAllucination Diversity-Aware Sampling (HADAS) to select diverse hallucinations for annotations in active learning for LLM finetuning. Extensive experiments on three datasets and different backbone models demonstrate advantages of our method in effectively and efficiently mitigating LLM hallucinations.

## introduction despite the prominent capabilities of large language models (llms) in natural language generation (nlg) tasks @xcite @xcite , a notable limitation of them lies in their propensity to hallucinate @xcite @xcite @xcite , where models generate seemingly plausible but ungrounded outputs that either contradict or cannot be verified by existing sources. the phenomenon of hallucination poses a crucial challenge to the real-world applications of llms, where the models' faithfulness and trustworthiness are emphasized @xcite . ## hallucination typology revisit since llms may hallucinate in different forms @xcite , evaluations of hallucination have received increasing attention in recent studies. particularly, a variety of factuality metrics have been developed @xcite @xcite @xcite , including entity or token hallucination @xcite , sentence hallucination @xcite , and relation hallucination @xcite . aiming to comprehensively mitigate hallucinations in text summarization, we follow the typology proposed in @xcite and introduce three types of common hallucinations in text summarization: i) semantic frame error, ii) discourse error, and iii) content verifiability error. to provide more background information, we provide illustrative examples of these three types of hallucination in hence, by evaluating hallucinations in these various aspects, we can derive a more comprehensive understanding of hallucinations in llm text summarization. this motivates us to capture and mitigate diverse types of hallucinations and enhance the faithfulness of llms in text summarization. ## methodology we first present our proposed active learning framework for llm finetuning in hallucination mitigation in section 4.1. section 4.2 details how we capture diverse hallucination types using off-theshelf detection models. then in section 4.3, we describe in detail our hallucination diversity-aware sample selection strategy. ## conclusion in this work, we propose the first active learning framework to mitigate hallucinations in llms, reducing the need for intensive human annotation efforts. by measuring various types of hallucinations in text summarization and developing a novel hallucination diversity-aware sample selection method, we effectively and efficiently mitigate llm hallucinations in summarizations in a comprehensive manner. extensive experiments on several datasets and backbone models demonstrate the advantages of our method across various factuality metrics while maintaining high summarization quality.