Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models

Zichao Lin, Shuyan Guan, Wending Zhang, Huiyan Zhang, Yugang Li, Huaping Zhang*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Recently, large language models (LLMs) have attracted considerable attention due to their remarkable capabilities. However, LLMs’ generation of biased or hallucinatory content raised significant concerns, posing major challenges for their practical application. Many studies have dedicated efforts to address these critical issues, adopting various approaches to mitigate bias and hallucinations in LLM-generated content. Remarkably, no review papers have synthesized insights on these two primary problems. Addressing this gap, this paper aims to conduct a simultaneous and dual-focused review of the current landscape of research. The discussions encompass widely used and newly proposed benchmarks and evaluation methods on bias and hallucination in LLMs. This paper also investigates advanced mitigation methods and present a taxonomy based on different mitigation strategies. Moreover, a comparative analysis of the sources, mitigation methods, and evaluation methods for bias and hallucination is included. In the end, this paper provides a synthesis of current research trends and suggests potential directions for future research to address bias and hallucination in LLMs, considering the ongoing challenges in this field.

源语言英语
文章编号243
期刊Artificial Intelligence Review
57
9
DOI
出版状态已出版 - 9月 2024

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