Large language models have demonstrated strong performance across a wide range of tasks, yet they remain prone to generating incorrect information with high confidence. To mitigate this reliability issue, it is necessary to properly assess model confidence before answer generation. However, existing methods either rely on post-hoc estimation based on generated tokens or sampling results, or require additional training based on supervised learning, which limits their computational efficiency and generalizability. In this paper, we propose PReemptive Internal Confidence Estimator (PRICE), which estimates confidence before answer generation by leveraging internal signals of LLMs that can be extracted from a single forward pass. PRICE combines signals that capture pattern agreement across attention heads and the semantic clarity of hidden states. Through experiments on diverse models and benchmarks, we show that PRICE achieves competitive performance against classifier-based methods without requiring additional training. These results suggest that PRICE can contribute to improving both the efficiency of LLMs and their safety in high-risk domains.

Question Answering (QA) with large language models has shown impressive performance, yet hallucinations still persist, particularly when user queries carry incorrect premises, insufficient context, or linguistic ambiguity. To address this issue, we propose Lightweight Query Checkpoint (LQC), a small classification model that detects verification-required queries before the LLM generates a potentially faulty answer. LQC leverages hidden states extracted from intermediate layers of a smaller-scale, non-instruct-tuned LLM to effectively distinguish queries requiring verification from clear queries. We first systematically define categories of queries that need verification, construct a dataset comprising both defective and clear queries, and train a binary contrastive learning model. Through extensive experiments on various QA datasets, we demonstrate that incorporating LQC into QA pipelines reduces hallucinations while preserving strong answer quality.