Mind the Generation Process: Fine-Grained Confidence Estimation During
  LLM Generation
While large language models (LLMs) have demonstrated remarkable performance
across diverse tasks, they fundamentally lack self-awareness and frequently
exhibit overconfidence, assigning high confidence scores to incorrect
predictions. Accurate confidence estimation is therefore critical for enhancing
the trustworthiness and reliability of LLM-generated outputs. However, existing
approaches suffer from coarse-grained scoring mechanisms that fail to provide
fine-grained, continuous confidence estimates throughout the generation
process. To address these limitations, we introduce FineCE, a novel confidence
estimation method that delivers accurate, fine-grained confidence scores during
text generation. Specifically, we first develop a comprehensive pipeline for
constructing training data that effectively captures the underlying
probabilistic distribution of LLM responses, and then train a model to predict
confidence scores for arbitrary text sequences in a supervised manner.
Furthermore, we propose a Backward Confidence Integration (BCI) strategy that
leverages information from the subsequent text to enhance confidence estimation
for the current sequence during inference. We also introduce three strategies
for identifying optimal positions to perform confidence estimation within the
generation process. Extensive experiments on multiple benchmark datasets
demonstrate that FineCE consistently outperforms existing classical confidence
estimation methods. Our code and all baselines used in the paper are available
on GitHub.
68a535566cf0bf898542ec5f
Jinyi Han
claimed_verified
2025-08-20T08:51:39.018Z
67ed200efd37e21a47149075
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Sg7Sxp1LAFbRY906EJoiU.png
Jinyi Han
user
JinyiHan
68a535566cf0bf898542ec60
Tingyun Li
68a535566cf0bf898542ec61
Shisong Chen
68a535566cf0bf898542ec62
Jie Shi
68a535566cf0bf898542ec63
Xinyi Wang
68a535566cf0bf898542ec64
Guanglei Yue
68a535566cf0bf898542ec65
Jiaqing Liang
68a535566cf0bf898542ec66
Xin Lin
68a535566cf0bf898542ec67
Liqian Wen
68a535566cf0bf898542ec68
Zulong Chen
68a535566cf0bf898542ec69
Yanghua Xiao
Mind the Generation Process: Fine-Grained Confidence Estimation During
  LLM Generation
While large language models (LLMs) have demonstrated remarkable performance
across diverse tasks, they fundamentally lack self-awareness and frequently
exhibit overconfidence, assigning high confidence scores to incorrect
predictions. Accurate confidence estimation is therefore critical for enhancing
the trustworthiness and reliability of LLM-generated outputs. However, existing
approaches suffer from coarse-grained scoring mechanisms that fail to provide
fine-grained, continuous confidence estimates throughout the generation
process. To address these limitations, we introduce FineCE, a novel confidence
estimation method that delivers accurate, fine-grained confidence scores during
text generation. Specifically, we first develop a comprehensive pipeline for
constructing training data that effectively captures the underlying
probabilistic distribution of LLM responses, and then train a model to predict
confidence scores for arbitrary text sequences in a supervised manner.
Furthermore, we propose a Backward Confidence Integration (BCI) strategy that
leverages information from the subsequent text to enhance confidence estimation
for the current sequence during inference. We also introduce three strategies
for identifying optimal positions to perform confidence estimation within the
generation process. Extensive experiments on multiple benchmark datasets
demonstrate that FineCE consistently outperforms existing classical confidence
estimation methods. Our code and all baselines used in the paper are available
on GitHub.
FineCE is a novel confidence estimation method for large language models that provides accurate, fine-grained confidence scores during text generation, outperforming existing methods.
large language models confidence estimation FineCE probabilistic distribution supervised learning Backward Confidence Integration BCI