Do Large Language Models Hallucinate Electric Fata Morganas?
AI hallucinations - that is, outputs which are made up, cannot be verified, or contradict the source material - are generally regarded as an engineering flaw to be dealt with. This paper contends that they also have philosophical significance when it comes to the question of machine consciousness. We examine the known causes of hallucinations in large language models - such as source-target divergence, discrepancies between training and inference, and overfitting - and we present two empirical investigations. In the first, we apply successive generations of the GPT model to ambiguous factual q
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- PossiblePossibly related (embedding) · 63%Models Produce Hallucinations Because of Probabilistic Training - Let's Data Science →
- PossiblePossibly related (embedding) · 63%Hallucination →
- PossiblePossibly related (embedding) · 60%AI Hallucinations: Why Artificial Intelligence Can Sound Convincing While Getting the Facts Wrong - USA Herald →
- PossiblePossibly related (embedding) · 58%Understanding large language models demands distinguishing human projection from machine cognition - Nature →
- LinkedLinked via arxiv author · 85%Kristina Šekrst →
“Do Large Language Models Hallucinate Electric Fata Morganas?”
- PossiblePossibly related (embedding) · 52%Implicit-bias-like patterns in reasoning models →
- PossiblePossibly related (embedding) · 63%Researchers develop cost-efficient method for detecting hallucinations in large language models - Tech Xplore →
- PossiblePossibly related (embedding) · 55%Causal evidence that language models use confidence to drive behaviour →