A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
(2401.01313)Abstract
As LLMs continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded. This issue of hallucination is arguably the biggest hindrance to safely deploying these powerful LLMs into real-world production systems that impact people's lives. The journey toward widespread adoption of LLMs in practical settings heavily relies on addressing and mitigating hallucinations. Unlike traditional AI systems focused on limited tasks, LLMs have been exposed to vast amounts of online text data during training. While this allows them to display impressive language fluency, it also means they are capable of extrapolating information from the biases in training data, misinterpreting ambiguous prompts, or modifying the information to align superficially with the input. This becomes hugely alarming when we rely on language generation capabilities for sensitive applications, such as summarizing medical records, financial analysis reports, etc. This paper presents a comprehensive survey of over 32 techniques developed to mitigate hallucination in LLMs. Notable among these are Retrieval Augmented Generation (Lewis et al, 2021), Knowledge Retrieval (Varshney et al,2023), CoNLI (Lei et al, 2023), and CoVe (Dhuliawala et al, 2023). Furthermore, we introduce a detailed taxonomy categorizing these methods based on various parameters, such as dataset utilization, common tasks, feedback mechanisms, and retriever types. This classification helps distinguish the diverse approaches specifically designed to tackle hallucination issues in LLMs. Additionally, we analyze the challenges and limitations inherent in these techniques, providing a solid foundation for future research in addressing hallucinations and related phenomena within the realm of LLMs.
Overview
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The survey addresses different techniques to mitigate hallucination in LLMs, which is crucial for accuracy in high-stakes applications.
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Techniques include prompting optimization, retrieval-augmented generation, self-refinement, novel architectures such as Context-Aware Decoding, and the use of Knowledge Graphs.
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Supervised fine-tuning and methods like Knowledge Injection and Refusal-Aware Instruction Tuning are used to refine responses and teach models about their knowledge limits.
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Challenges remain due to dataset reliability, language domain complexity, and requiring a unified solution across tasks.
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Future research directions aim at hybrid models, unsupervised learning, and models with built-in safety features to prevent hallucinations.
Introduction
Hallucination in LLMs is a recognized problem that occurs when these models generate text containing inaccurate or unfounded information. This presents substantial challenges in applications like summarizing medical records or providing financial advice, where accuracy is vital. The survey explored in this discussion tackles over thirty-two different techniques developed to mitigate hallucinations in LLMs.
Hallucination Mitigation Techniques
The survey categorizes these techniques into several groups. Prompting methods focus on optimizing instructions to generate more accurate responses. For instance, Retrieval-Augmented Generation (RAG) incorporates external knowledge to update and enrich model responses. Techniques that unfold through self-refinement leverage feedback to improve subsequent outputs, such as the Self-Reflection Methodology that iteratively refines medical QA responses.
Furthermore, studies have also proposed novel model architectures specifically designed to tackle hallucinations, including decoding strategies like Context-Aware Decoding (CAD), which emphasizes context-relevant information, and the utilization of Knowledge Graphs (KGs) that enable models to ground responses in verified information.
Supervised Fine-Tuning
Supervised fine-tuning refines the model on task-specific data, which can significantly improve the relevance and reliability of text produced by LLMs. For example, Knowledge Injection techniques infuse domain-specific knowledge, while others like Refusal-Aware Instruction Tuning (R-Tuning) teach the model when to avoid responding to certain prompts due to knowledge limitations.
Challenges and Future Directions
The survey addresses the challenges and limitations associated with current hallucination mitigation techniques. These include the varying reliability of tagged datasets and the complexity of implementing solutions that could work across different language domains and tasks. Looking forward, potential directions include hybrid models that integrate multiple mitigation approaches, unsupervised learning methods to reduce reliance on labeled data, and the development of models with inherent safety features to tackle hallucinations.
Conclusion
The thorough survey presented in this discussion offers a structured categorization of hallucination mitigation techniques, providing a basis for future research. It underscores the need for continued advancement in this area, as the reliability and accuracy of LLMs are critical for their practical application. With ongoing review and development of mitigation strategies, we move closer to the goal of creating language models that can consistently produce coherent and contextually relevant information, while minimizing the risk and impact of hallucination.