arXiv:2606. 03731v1 Announce Type: new Abstract: Large Language Models remain plagued by hallucinations.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
Large Language Models remain plagued by hallucinations. Recent work has sought to tame their prevalence using statistical techniques based on conformal prediction, with both theoretical and empirical success.
arXiv:2605. 28910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications.
arXiv:2603. 09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against.
The paper presents a geometric framework for quantifying uncertainty in large language models (LLMs) at both the prompt and answer levels. By modeling a prompt-conditioned semantic distribution in answer embedding space and using archetypal analysis on multiple sampled answers, the method estimates distribution entropy for prompt-level uncertainty and atypicality for individual answer reliability. Experiments demonstrate comparable or superior performance to existing techniques on short-form QA datasets and notably better results on medical datasets where hallucinations pose critical risks.
arXiv:2510. 05566v2 Announce Type: replace-cross Abstract: Large language models have achieved impressive performance across diverse tasks.
arXiv:2606. 07521v1 Announce Type: cross Abstract: This study investigates the phenomenon of hallucinations in domain-adapted Large Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini dataset.