Papers with calibration of quantized models

1 papers
When Quantization Affects Confidence of Large Language Models? (2024.findings-naacl)

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Challenge: Existing studies have shown that quantization compromises performance and exacerbates biases in Large Language Models.
Approach: They propose an explanation for quantization loss based on confidence levels . they propose a range of efficient compression and acceleration methods including quan-tization .
Outcome: The proposed methods show that quantization decreases confidence regarding true labels and that it exacerbates biases across different scales.

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