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LLMs Know More Than They Show
The study focuses on understanding how large language models (LLMs) represent and encode information about their own truthfulness, especially in the context of generating hallucinations—incorrect or nonsensical outputs.
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Understanding and Mitigating Hallucination in LLMs
This document explores the phenomenon of hallucination in Large Language Models (LLMs), a critical challenge for AI engineers aiming to deploy reliable AI systems. Hallucination refers to the generation of nonsensical or factually incorrect responses, which can undermine trust in AI applications. We present a comprehensive overview of the mechanisms behind hallucination, an experimental framework…
Tag: LLM_HALLUCINATIONS
Examining how large language models sometimes generate false information with high confidence, and what this reveals about their capabilities and limitations.