LLMs Will Always Hallucinate, and We Need to Live With This

• URL: http://arxiv.org/abs/2409.05746v1
• Date: Mon, 9 Sep 2024 16:01:58 GMT
• Title: LLMs Will Always Hallucinate, and We Need to Live With This
• Authors: Sourav Banerjee, Ayushi Agarwal, Saloni Singla,
• Abstract summary: This work argues that hallucinations in language models are not just occasional errors but an inevitable feature of these systems. It is, therefore, impossible to eliminate them through architectural improvements, dataset enhancements, or fact-checking mechanisms.
• Score: 1.3810901729134184
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As Large Language Models become more ubiquitous across domains, it becomes important to examine their inherent limitations critically. This work argues that hallucinations in language models are not just occasional errors but an inevitable feature of these systems. We demonstrate that hallucinations stem from the fundamental mathematical and logical structure of LLMs. It is, therefore, impossible to eliminate them through architectural improvements, dataset enhancements, or fact-checking mechanisms. Our analysis draws on computational theory and Godel's First Incompleteness Theorem, which references the undecidability of problems like the Halting, Emptiness, and Acceptance Problems. We demonstrate that every stage of the LLM process-from training data compilation to fact retrieval, intent classification, and text generation-will have a non-zero probability of producing hallucinations. This work introduces the concept of Structural Hallucination as an intrinsic nature of these systems. By establishing the mathematical certainty of hallucinations, we challenge the prevailing notion that they can be fully mitigated.

Related papers

• Banishing LLM Hallucinations Requires Rethinking Generalization [2.0155206466638016]
  Large Language Models (LLMs) frequently hallucinate despite their powerful chat, coding, and reasoning abilities. We show that LLMs augmented with a massive Mixture of Memory Experts (MoME) can easily memorize large datasets of random numbers. We use our findings to design a first generation model for removing hallucinations -- Lamini-1 -- that stores facts in a massive mixture of millions of memory experts.
  arXiv  Detail & Related papers  (2024-06-25T15:31:01Z)
• Mitigating Large Language Model Hallucination with Faithful Finetuning [46.33663932554782]
  Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks. They are prone to generating fluent yet untruthful responses, known as "hallucinations"
  arXiv  Detail & Related papers  (2024-06-17T07:16:07Z)
• On Large Language Models' Hallucination with Regard to Known Facts [74.96789694959894]
  Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics. Our study shed light on understanding the reasons for LLMs' hallucinations on their known facts, and more importantly, on accurately predicting when they are hallucinating.
  arXiv  Detail & Related papers  (2024-03-29T06:48:30Z)
• Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models [52.957842999317506]
  Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. We propose a Logical Closed Loop-based framework for Object Hallucination Detection and Mitigation, namely LogicCheckGPT. As a plug-and-play method, it can be seamlessly applied to all existing LVLMs.
  arXiv  Detail & Related papers  (2024-02-18T15:28:39Z)
• Hallucination is Inevitable: An Innate Limitation of Large Language Models [3.8711997449980844]
  We show that it is impossible to eliminate hallucination in large language models. Since the formal world is a part of the real world which is much more complicated, hallucinations are also inevitable for real world LLMs.
  arXiv  Detail & Related papers  (2024-01-22T10:26:14Z)
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models [134.6697160940223]
  hallucination poses great challenge to trustworthy and reliable deployment of large language models. Three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them. This work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation.
  arXiv  Detail & Related papers  (2024-01-06T12:40:45Z)
• A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models [7.705767540805267]
  Large Language Models (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 paper presents a survey of over 32 techniques developed to mitigate hallucination in LLMs.
  arXiv  Detail & Related papers  (2024-01-02T17:56:30Z)
• Alleviating Hallucinations of Large Language Models through Induced Hallucinations [67.35512483340837]
  Large language models (LLMs) have been observed to generate responses that include inaccurate or fabricated information. We propose a simple textitInduce-then-Contrast Decoding (ICD) strategy to alleviate hallucinations.
  arXiv  Detail & Related papers  (2023-12-25T12:32:49Z)
• HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data [102.56792377624927]
  hallucinations inherent in machine-generated data remain under-explored. We present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. Our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA.
  arXiv  Detail & Related papers  (2023-11-22T04:52:58Z)
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions [42.007305423982515]
  Large language models (LLMs) produce hallucinations, resulting in content inconsistent with real-world facts or user inputs. This survey aims to provide a thorough and in-depth overview of recent advances in the field of LLM hallucinations.
  arXiv  Detail & Related papers  (2023-11-09T09:25:37Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.