Are hallucinations in large language models (LLMs) a flaw or an intrinsic part of their design?

As LLMs like GPT generate text, they sometimes produce errors, often referred to as “hallucinations.” But are these mistakes simply artifacts, or are they deeply rooted in the way these models function?

In the paper “Decoding Hallucinations in Large Language Models: A Token-Level Truthfulness Analysis” by Sayan Ghosh, Jason Wei, and Diyi Yang, 2024, the authors explore how LLMs encode the truthfulness of their outputs within specific tokens. This discovery could lead to better error detection, though generalizing these methods across datasets remains a challenge.

Even more interesting is the finding that LLMs often “know” the right answer internally but still generate incorrect responses. This disconnect between internal knowledge and external behavior provides a clearer view of how LLMs function — and highlights key areas for future improvement in AI systems.

To me, this paper is interesting because it challenges how we understand so-called “hallucinations” in LLMs. Perhaps the term oversimplifies a more complex internal process. Recognizing these discrepancies is crucial for refining how we interact with AI systems, leading to more reliable outputs and encouraging smarter model development moving forward.