Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models

• URL: http://arxiv.org/abs/2308.11764v4
• Date: Wed, 13 Sep 2023 18:01:36 GMT
• Title: Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models
• Authors: Mohamed Elaraby, Mengyin Lu, Jacob Dunn, Xueying Zhang, Yu Wang, Shizhu Liu, Pingchuan Tian, Yuping Wang, Yuxuan Wang
• Abstract summary: Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) Open-source LLMs with fewer parameters often suffer from severe hallucinations compared to their larger counterparts. This paper focuses on measuring and reducing hallucinations in BLOOM 7B, a representative of such weaker open-source LLMs.
• Score: 11.497989461290793
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP). Although convenient for research and practical applications, open-source LLMs with fewer parameters often suffer from severe hallucinations compared to their larger counterparts. This paper focuses on measuring and reducing hallucinations in BLOOM 7B, a representative of such weaker open-source LLMs that are publicly available for research and commercial applications. We introduce HaloCheck, a lightweight BlackBox knowledge-free framework designed to quantify the severity of hallucinations in LLMs. Additionally, we explore techniques like knowledge injection and teacher-student approaches to alleviate hallucinations in low-parameter LLMs. Our experiments effectively demonstrate the reduction of hallucinations in challenging domains for these LLMs.

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