Taxonomies of the LLM hallucination phenomena and evaluation benchmarks are presented, existing approaches aiming at mitigating LLM hallucination are analyzed, and potential directions for future research are discussed.
A natural way to decrease hallucinations is every time the authors have new pairs on which to train, they should also again train on the pairs corresponding to well-established facts and rules.
T. Ilina, M. Ceberio, Marcelo F. Frias et al.· 0 citations
This work proposes a five-step diagnostic protocol covering feature correlation, bootstrap stability, sparse versus dense ranking disagreement, intervention baselines, and cross-dataset evaluation as a minimum standard for sparse-neuron localization claims, and investigates prior work using the proposed approach.
Huseyin Cavus, Sebin Sabu, J. Spear et al.· 0 citations
Language, truth, and LLMs How can LLMs appear to know facts about the world when they are trained only on patterns in language rather than direct experience of reality? Hallucinations shouldn’t surprise us. The real mystery is how these systems get anything right at all.
This work proposes a method to rank feed-forward neurons at the final prompt token using a custom neuron selection dataset, and transfers the selected neuron identities to train hallucination classifiers on other factual question answering datasets.
Ali Derogar Odolou, Reza Nazari, Mostafa Salehi· 0 citations
It is shown that epistemic uncertainty leaves a measurable trace within intermediate layers, where spikes in attention entropy are associated with reasoning breakdowns, and that attention dispersion is a promising complement to traditional hallucination detection methods.
This paper clarifies the above three aspects and how they work together to form a closed-loop hallucination cycle, starting with user feedback as a departure point, moving to the next step of turning error-correction signals into training instances at the data tier, and lastly to internalising the ability of veracity d...
Jun-Hua Chen· Applied and Computational En...· 0 citations
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