The study delves into the mechanisms of hallucinations in large language models, focusing on inner representations and in-context activations. By proposing an entropy-based metric to measure sharpness and incorporating it into decoding, the approach shows consistent effectiveness across various benchmarks. The findings highlight the importance of understanding internal processes to improve factuality and mitigate errors.
Large language models (LLMs) often produce factual errors due to hallucinations, prompting the need for better mitigation strategies. The study introduces an innovative approach based on in-context sharpness to quantify and address these errors effectively. By leveraging insights from inner representations and hidden states, the proposed method demonstrates significant improvements in factuality across multiple datasets.
The research emphasizes the significance of internal mechanisms in LLMs for detecting and mitigating hallucinations. Through detailed experiments and analyses, the study showcases how measuring in-context sharpness can lead to more accurate and reliable text generation. Overall, the findings contribute to enhancing model performance by focusing on core aspects of factuality and error detection.
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by Shiqi Chen,M... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01548.pdfDeeper Inquiries