LLM hallucinations can be understood as Popperian conjectures and may drive progress when paired with effective mechanisms of refutation.

Large Language Models (LLMs) are notorious for their tendency to “hallucinate” and generate outputs that sound plausible but are entirely false. Critics like Gary Marcus argue that these hallucinations are not just occasional glitches but an inherent consequence of how LLMs function. At their core, LLMs use statistical methods, predicting each new word (or “token”) based on probabilities derived from vast amounts of training data. Because they excel at estimating these probabilities, their outputs are frequently accurate, but precisely because this process is purely statistical, they have no internal representation of truth or reality. As Marcus points out, LLMs “hallucinate every single output,” in the sense that every response is generated through statistical likelihood rather than genuine understanding. Thus, even though LLMs often produce correct outputs through their predictions, they’re just as capable of confidently presenting incorrect statements without realising the difference. For Marcus and other critics, hallucinations are a key reason why they believe LLMs won’t lead to Artificial General Intelligence (AGI) and beyond. I am more optimistic than Marcus: firstly, in my interactions with LLMs, I’ve noticed a significant reduction in hallucinations over the past year; secondly, as I will argue below, I believe that these hallucinations, if properly managed, could become a valuable creative tool.

In this post, I explore whether hallucinations might be more than just a bug and could instead serve as a creative feature, playing a role similar to Popperian conjectures. Karl Popper’s core insight was that we can learn from our mistakes, and indeed, that this is the only way we genuinely advance our understanding. We progress by making bold guesses (conjectures) about the world and then subjecting them to rigorous testing to see if they survive. LLM hallucinations – often highly plausible, even when they conflict with widely accepted knowledge – can be seen as bold conjectures, which might be wrong, but could just as well help challenge our assumptions and bring us closer to the truth. Although their misleading nature poses clear risks, they can occasionally point toward fruitful new avenues of inquiry or prompt us to re...

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