When Quantization Preserves Accuracy but Not Evidence: Explanation-Aware Post-Training Quantization for Medical LLMs
Paper recorded by Signals 4 on 2026-09-21 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22
Category: cs.CL · 自然语言处理 · first seen 2026-09-22
Abstract
Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generic reconstruction, perplexity, or answer accuracy. But in explanation-critical domains, preserving only the final answer may be insufficient, since users may also inspect generated rationales to judge whether a prediction is trustworthy. We study thi
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Cite this page: When Quantization Preserves Accuracy but Not Evidence: Explanation-Aware Post-Training Quantization for Medical LLMs: the #11 most recent of 212 cs.CL papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/when-quantization-preserves-accuracy-but-not-evidence-explanation-aware-post-tra.html
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