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May 1, 2025 · In this paper, we discuss these open challenges covering state-of-the-art datasets and benchmarks as well as methods for knowledge integration and evaluating hallucinations.
This survey reviews current research on hallucinations in LLMs, including their types, causes, impacts, and mitigation strategies.
As some inevitable knowledge noises may also lead to hallucinations, it is emergent to investigate the reason and future directions for building noise-tolerant methods in KGD tasks. In this …
Our work identify knowledge overshadowing as a contributional cause of LLMs hallucination, where dominant knowledge suppresses less frequent facts, leading to fact distortions.
Aug 16, 2024 · While this creative flexibility can be valuable in certain contexts, it can also result in errors or hallucinations, such as fabricating quotes from non-existent books or generating …
Feb 22, 2025 · To address it, we propose a novel concept: knowledge overshadowing, where model's dominant knowledge can obscure less prominent knowledge during text generation, …
We present three different ways in which this is successfully implemented to the enhancement of knowledge and understanding of hallucinations and related phenomena.
Jul 10, 2024 · Hallucination is often regarded as a major impediment for using large language models (LLMs), especially for knowledge-intensive tasks. Even when the training corpus …
Specifically, when extracting knowledge from a language model using queries involving multiple conditions, we observe a tendency for certain conditions to overshadow others, thereby giving …
Introduction: Hallucinations research is increasingly incorporating philosophy or the work of philosophically trained individuals. We present three different ways in which this is successfully …
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