Hallucination in Large Language Models (LLMs) entails the creation of factually erroneous information spanning a multitude of subjects. Given the extensive domain coverage of LLMs, their application extends across numerous scholarly and professional areas. These include, but are not limited to, academi...
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models[C]//Bouamor H, Pino J, Bali K. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore: Association for Computational Linguistics, 2023: 9004-9017.3. 使用多...
我使用了一个包含800行数据的数据集,并使用了GPT-3.5 turbo,因为该API具有较高的性价比。 用于评估大模型幻觉的其他基准测试: Knowledge-oriented LLM Assessment benchmark (KoLA)[8] TruthfulQA: Measuring How Models Imitate Human Falsehoods[9] Med-HALT: Medical Domain Hallucination Test for Large Language...
{sampled_passages[1]} \n\n \ {sampled_passages[2]}."""completion=client.chat.completions.create(model="gpt-3.5-turbo",messages=[{"role":"system","content":""},{"role":"user","content":prompt}])returncompletion.choices[0].message.content Evelyn Hartwell的自相似性得分为0。Nicolas Cage...
Moreover, we successfully mitigate hallucination by finetuning MiniGPT4 and mPLUG-Owl on LRV-Instruction while improving performance on several public datasets compared to state-of-the-art methods. Additionally, we observed that a balanced ratio of positive and negativ...
GPT-4 and GPT-4 Turbo have the lowest rates of hallucination compared to other AI models. GPT-4 and GPT-4 Turbo had a hallucination rate of three percent, with GPT-3.5 Turbo coming in second place, having a hallucination rate of 3.5 percent. Evidently, the newer GPT versions have an im...
Large language models, such as GPT-3, have become integral to natural language processing and text generation. Trained on vast datasets, they possess the uncanny ability to produce coherent and context-sensitive human-like text. These models have found applications in a myriad of fields, from con...
reliabilitycalibrationsafetyawesome-listuncertainty-quantificationuncertainty-estimationrobustnesshallucinationgpt-3gpt-4in-context-learninglarge-language-modelsprompt-engineeringpromptingllmschain-of-thoughtchatgpt UpdatedFeb 28, 2025 VITA-MLLM/Woodpecker
Deepmind发布的论文:LARGE LANGUAGE MODELS CANNOT SELF-CORRECT REASONING YET(大语言模型还不能在推理上自我纠错) 实验结果:让LLM(GPT-4和Llama-2)多次纠错后,准确率都下滑,次数越多,下滑越厉害。GPT-4比Llama-2降幅小。 GPT-4在没有外部信息指导情况下,纠错能力堪忧。通过纠错实现内在幻觉矫正的路子行不通 ...
Detecting hallucinations in Large Language Models (LLMs) remains a critical challenge for their reliable deployment in real-world applications. To address this, we introduce SelfCheckAgent, a novel framework integrating three different agents: the Symbolic Agent, the Specialized Detection Agent, and ...