NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems
arXiv:2601.11004v3 Announce Type: replace Abstract: 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 especially when noisy contexts are retrieved. Specifically, contradictory or irrelevant evidence tends to exacerbate the model's overconfidence issue. To add
The deployment of LLMs in critical applications necessitates a deeper understanding of their reliability, especially as RAG systems introduce new complexities like noisy data.
Ensuring the trustworthiness and accuracy of AI systems, particularly LLMs integrated into factual and mission-critical domains, is paramount for their safe and effective adoption.
This research highlights the specific vulnerability of RAG systems to overconfidence when processing unreliable information, demanding more robust calibration methods for real-world deployment.
- · AI safety researchers
- · Enterprises deploying RAG
- · Makers of LLM calibration tools
- · Uncalibrated RAG systems
- · Users relying on unverified LLM outputs
Increased focus on confidence calibration techniques for LLMs, especially within RAG architectures.
Development of new standards and benchmarks for assessing the reliability of AI systems in critical applications.
Greater public trust and regulatory acceptance for AI deployments that can rigorously demonstrate their knowledge of ignorance.
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