You’ve probably interacted with AI models like ChatGPT, Claude, and Gemini for various tasks – answering questions, generating creative content, or assisting with research. But did you know these are examples of large language models (LLMs)? These powerful AI systems are trained on enormous text datasets, enabling them to understand and produce text that feels remarkably human.
If you asked about my understanding of large language models (LLMs), I’d say I’m just scratching the surface. So, to learn more about it, I have been reading a lot about LLMs lately to get more clarity on how they work and make our lives easier.
On this quest, I came across this research paper: Hallucination is Inevitable:Â An Innate Limitation of Large Language Models by Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli.
This paper discusses Hallucinations in LLMs and says that despite countless efforts to address the issue, it’s impossible to eliminate them completely. These hallucinations occur when a seemingly reliable AI confidently delivers information that, although plausible-sounding, is entirely fabricated. This persistent flaw reveals a significant weakness in the technology behind today’s most advanced AI systems.
In this article, I will tell you everything about the research that formalizes the concept of hallucination in LLMs and delivers a sobering conclusion: hallucination is not just a glitch but an inherent feature of these models.
Large language models (LLMs) have significantly advanced artificial intelligence, particularly in natural language processing. However, they face the challenge of “hallucination,” where they generate plausible but incorrect or nonsensical information. This issue raises concerns about safety and ethics as LLMs are increasingly applied in various fields.
Research has identified multiple sources of hallucination, including data collection, training processes, and model inference. Various methods have been proposed to reduce hallucination, such as using factual-centered metrics, retrieval-based methods, and prompting models to reason or verify their outputs.
Despite these efforts, hallucination remains a largely empirical issue. The paper argues that hallucination is inevitable for any computable LLM, regardless of the model’s design or training. The study provides theoretical and empirical evidence to support this claim, offering insights into how LLMs should be designed and deployed in practice to minimize the impact of hallucination.
Hallucinations in language models can be classified based on outcomes or underlying processes. A common framework is the intrinsic-extrinsic dichotomy: Intrinsic hallucination occurs when the output contradicts the given input, while extrinsic hallucination involves outputs that the input information cannot verify. Huang et al. introduced “faithfulness hallucination,” focusing on inconsistencies in user instructions, context, and logic. Rawte et al. further divided hallucinations into “factual mirage” and “silver lining,” with each category containing intrinsic and extrinsic types.