NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems
Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is widely adopted to improve grounding, confidence calibration in RAG settings remains poorly understood. We conduct a systematic study across four benchmarks, revealing that LLMs exhibit poor calibration performance due to noisy retrieved contexts. Specifically, contradictory or irrelevant evidence tends to inflate the model's false certainty, leading to severe overconfidence. To address this, we propose NAACL Rules (Noise-AwAre Confidence CaLibration Rules) to provide a principled foundation for resolving overconfidence under noise. We further design NAACL, a noise-aware calibration framework that synthesizes supervision from about 2K HotpotQA examples guided by these rules. By performing supervised fine-tuning (SFT) with this data, NAACL equips models with intrinsic noise awareness without relying on stronger teacher models. Empirical results show that NAACL yields substantial gains, improving ECE scores by 10.9% in-domain and 8.0% out-of-domain. By bridging the gap between retrieval noise and verbal calibration, NAACL paves the way for both accurate and epistemically reliable LLMs.
696ea9fa5750539a88acc758
Jiayu Liu
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2026-01-22T08:47:29.789Z
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Jeff
JiayuJeff
user
JiayuJeff
696ea9fa5750539a88acc759
Rui Wang
696ea9fa5750539a88acc75a
Qing Zong
696ea9fa5750539a88acc75b
Qingcheng Zeng
696ea9fa5750539a88acc75c
Tianshi Zheng
696ea9fa5750539a88acc75d
Haochen Shi
696ea9fa5750539a88acc75e
Dadi Guo
696ea9fa5750539a88acc75f
Baixuan Xu
696ea9fa5750539a88acc760
Chunyang Li
696ea9fa5750539a88acc761
Yangqiu Song
NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems
Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is widely adopted to improve grounding, confidence calibration in RAG settings remains poorly understood. We conduct a systematic study across four benchmarks, revealing that LLMs exhibit poor calibration performance due to noisy retrieved contexts. Specifically, contradictory or irrelevant evidence tends to inflate the model's false certainty, leading to severe overconfidence. To address this, we propose NAACL Rules (Noise-AwAre Confidence CaLibration Rules) to provide a principled foundation for resolving overconfidence under noise. We further design NAACL, a noise-aware calibration framework that synthesizes supervision from about 2K HotpotQA examples guided by these rules. By performing supervised fine-tuning (SFT) with this data, NAACL equips models with intrinsic noise awareness without relying on stronger teacher models. Empirical results show that NAACL yields substantial gains, improving ECE scores by 10.9% in-domain and 8.0% out-of-domain. By bridging the gap between retrieval noise and verbal calibration, NAACL paves the way for both accurate and epistemically reliable LLMs.
Large language models suffer from poor confidence calibration in retrieval-augmented generation due to noisy contexts, but a noise-aware calibration framework significantly improves calibration performance.
retrieval-augmented generation confidence calibration noise-aware calibration supervised fine-tuning ECE scores