Hallucination Diversity-Aware Active Learning for Text Summarization
Yu Xia Xu Liu Tong Yu Sungchul Kim Ryan A. Rossi Anup Rao Tung Mai Shuai Li
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.