Year: 2026
Type: conference-paper
Abstract: Hallucination remains a major challenge for the safe and trustworthy deployment of large language models (LLMs) in factual content generation.Prior work has explored confidence estimation as an effective approach to hallucination detection, but often relies on post-hoc self-consistency methods that require computationally expensive sampling.Verbalized confidence offers a more efficient alternative, but existing approaches are largely limited to shortform question answering (QA) tasks and do not generalize well to open-ended generation.In this paper, we propose LOVEC (Long-form Verbalized Confidence), a novel reinforcement learning (RL)-based method that trains LLMs to append an on-the-fly numerical confidence score to each generated statement during longform generation.The confidence score serves as a direct and interpretable signal of the factuality of generation.We introduce two evaluation settings, free-form tagging and iterative tagging, to assess different verbalized confidence estimation methods.Experiments on three long-form QA datasets show that our RLtrained models achieve better calibration and generalize robustly across domains.Also, our method is highly efficient, being 20× faster than traditional self-consistency methods while achieving better calibration.
Institution: University of Cambridge
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166878685