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Hallucination: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
Hallucinations in large language models (LLMs) like GPT and Bing Chat are a fascinating and critical aspect of artificial intelligence research. These instances, where an LLM generates information that is misleading, irrelevant, or downright false, present significant challenges and opportunities for the development of more reliable and accurate AI systems. | [[Hallucinations]] in large language models ([[LLMs]]) like [[GPT]] and [[Bing Chat]] are a fascinating and critical aspect of [[artificial intelligence]] research. These instances, where an LLM generates information that is misleading, irrelevant, or downright false, present significant challenges and opportunities for the development of more reliable and accurate [[AI systems]]. | ||
== Definition and Overview == | == Definition and Overview == | ||
Hallucinations in LLMs refer to the phenomenon where the model generates text that deviates from factual accuracy or logical coherence. These can range from minor inaccuracies to complete fabrications or contradictory statements, impacting the reliability and trustworthiness of AI-generated content. | [[Hallucinations]] in LLMs refer to the phenomenon where the model generates text that deviates from factual accuracy or logical coherence. These can range from minor inaccuracies to complete fabrications or contradictory statements, impacting the reliability and trustworthiness of AI-generated content. | ||
=== Types of Hallucinations === | === Types of Hallucinations === | ||
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=== Data Quality Issues === | === Data Quality Issues === | ||
LLMs are trained on vast corpora of text sourced from the internet, including sites like Wikipedia and Reddit. The quality of this data varies, with inaccuracies, biases, and inconsistencies being inadvertently learned by the model. | LLMs are trained on vast corpora of text sourced from the internet, including sites like [[Wikipedia]] and [[Reddit]]. The quality of this data varies, with inaccuracies, biases, and inconsistencies being inadvertently learned by the model. | ||
=== Generation Methods === | === Generation Methods === | ||