Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction
Incorporating specific knowledge into large language models via retrieval-augmented generation (RAG) is a widespread technique that fuels many of today's industry AI applications. A fundamental problem is to assess if the context retrieved by some similarity search provides indeed supporting facts, or instead misguides the generator with irrelevant information. It is critical to associate meaningful confidence measures about the factuality of the retrieval process with the generated answers. We present a new, two-staged approach to predict fact faithfulness of the output of retrieval-augmented generations. First, we employ conformal prediction to select only those retrieved chunks who hav...
| Authors | Florian Geissler, Francesco Carella, Laura Fieback et al. |
|---|---|
| Primary category | cs.IR |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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