"I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation
Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LLMs) as they provide human-level communication capabilities, but potentially hallucinate in the content that they generate. Moreover, they express over-confidence in their answers, making it difficult for users to judge their truthfulness. An important human value that users seek is benevolence, which can be met by LLM's self-reflection leading to reliable and honest answers. Accordingly, this paper proposes conveying appropriate levels of self-reflected certainty to build appropriate trust. Our contributions are twofold: 1) We develop CERTA ...
| Authors | Daan Di Scala, Maaike de Boer, Pınar Yolum |
|---|---|
| Primary category | cs.IR |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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