Detecting hallucinations in large language models using semantic entropy - Nature Lingua教授: 亲爱的同事们,我们今天聚集在这里,讨论生成式语言模型中的一个微妙问题,即“幻觉”,特别是被称为“虚构化”的子集——模型做出任意和错误的陈述。在文本生成的广阔领域中,我们的目标是通过测量模型生成的意义的不确定性...
Large language model (LLM) systems, such as ChatGPT1or Gemini2, can show impressive reasoning and question-answering capabilities but often ‘hallucinate’ false outputs and unsubstantiated answers3,4. Answering unreliably or without the necessary information prevents adoption in diverse fields, with p...
Nature:Detecting hallucinations in large language models using semantic entropy(使用语义熵来检测大模型中的幻觉) 作者:Sebastian Farquhar, Jannik Kossen , Lorenz Kuhn & Yarin Gal 单位:牛津大学,计算机科学学院,OATML实验室 期刊:Nature 时间线:2023年7月提交 → 2024年6月发表 概要 大型语言模型(LLM),如c...
Large language model (LLM) systems, such as ChatGPTor Gemini, can show impressive reasoning and question-answering capabilities but often 'hallucinate' false outputs and unsubstantiated answers. Answering unreliably or without the necessary information prevents adoption in diverse fields, with problems ...
(TLM), a fundamental advance in generative AI that the company says can detect when large language models (LLMs) are hallucinating. Steven Gawthorpe, PhD, Associate Director and Senior Data Scientist at Berkeley Research Group, called the Trustworthy Language Model “the first viable an...
Mitigating Hallucinations in Large Vision-Language Models via DPO:On-Policy Data Hold the KeyZhihe Yang 1,3 * Xufang Luo 2† Dongqi Han 2 Yunjian Xu 1,3‡ Dongsheng Li 21 The Chinese University of Hong Kong, Hong Kong SAR, China2 Microsoft Research Asia, Shanghai, China3 The Chinese...
[1] https://www.lakera.ai/blog/guide-to-hallucinations-in-large-language-models#mitigating-hallucinations-in-large-language-models [2] https://link.springer.com/chapter/10.1007/978-3-031-43458-7_34 Adiós ! ···
Hallucinations in the context of Large Language Models (LLMs) refer to instances where the model generates information that is factually incorrect, nonsensical, or unrelated to the input prompt. These are not intentional fabrications, but rather errors in the model's output that can appear convin...
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge RetrievalO网页链接这篇论文探讨了大型语言模型(LLMs)在各个领域表现出惊人的能力,但它们易受虚构(hallucination)的影响,这在医疗等关键领域提出了重大挑战。为了解决这个问题,从知识图谱(KGs)检索相关事实被视为一种有前景的...
Large Vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned. Despite their success, LVLMs still suffer from the issue of object hallucinations, where models generate...