Extrinsic Hallucinations in LLMs
Lilian Weng 研究 入门 Impact: 8/10
This article explores the phenomenon of extrinsic hallucinations in large language models, analyzing their causes and detection methods, and proposes effective strategies to reduce hallucinations while emphasizing the risks of knowledge updates.
Key Points
- Extrinsic hallucinations refer to model outputs that are inconsistent with pre-training data, requiring outputs to be factual and verifiable.
- The quality of pre-training data directly affects model performance, with outdated or incorrect information leading to hallucinations.
- When fine-tuning new knowledge, the model learns unknown information more slowly, increasing the risk of hallucinations.
- New methods like retrieval-augmented evaluation can better quantify and detect hallucination phenomena.
Analysis
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