arXiv:2601. 11429v2 Announce Type: replace-cross Abstract: Hallucination is a central failure mode of language models (LMs).
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arXiv:2606. 11105v1 Announce Type: cross Abstract: Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them.
The paper investigates why large language models (LLMs) produce hallucinations—outputs that are fabricated, unverifiable, or contradictory to source material—and argues that these hallucinations have philosophical implications for machine consciousness. It reviews known causes such as source‑target divergence, training‑inference discrepancies, and overfitting, and presents two empirical studies: one showing that higher temperature settings in GPT models yield plausible but incorrect answers, while lower temperatures produce accurate ones; and another demonstrating that an encoder‑only model trained on encyclopedic data answers factually without embellishment, suggesting hallucinations arise from exposure to subjective, socially diverse data rather than cognitive ability. Drawing on Turing, Searle’s Chinese Room, the frame problem, and cybernetic theory, the authors contend that a model’s self‑reports of emotion or sentience fall within the definition of hallucination, implying that any future machine consciousness may remain epistemically inaccessible because it would be indistinguishable from an advanced hallucination.
arXiv:2609.22152v1 Announce Type: new Abstract: Imagination performs as a high-level function of large language models (LLMs) which determines the potential of how an LLM creates unseen or creative c...
The paper explores using a language model’s low‑level symbolic skill—specifically SQL—to detect hallucinations without fine‑tuning. By having the model construct an SQL database from reference documents, it can reason over both the source and the model’s output, creating a neurosymbolic check. Experiments on RAGTruth and DiaHalu show this method outperforms direct prediction and rivals state‑of‑the‑art detectors, highlighting the value of leveraging inherent symbolic competences in LLMs.
RelCheck is a training‑free post‑hoc correction pipeline that addresses relational hallucinations in multimodal large language models. It augments object‑level visual grounding with two forms of relational evidence—learned scene‑graph triples from RelTR and deterministic spatial predicates derived from bounding‑box geometry—forming a three‑layer visual knowledge base. When applied to LLaVA v1 13B, RelCheck improves the overall MME hallucination score from 585.0 to 630.0, with the most significant gain on spatial position accuracy.
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.