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Found 8 papers, 2 papers with code
FACTOID: FACtual enTailment fOr hallucInation Detection
We present FACTOID (FACTual enTAILment for hallucInation Detection), a benchmark dataset for FE.
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
We have fine-tuned an LLM with injected [PAUSE] tokens, allowing the LLM to pause while reading lengthier prompts.
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations
The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI.
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded.
The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations
Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI).
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination.
A Survey of Hallucination in Large Foundation Models
Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information.
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain.
