Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation
FineCE is a supervised fine-grained confidence estimation method that significantly improves early and continuous confidence prediction during text generation.
Key Findings
Methodology
FineCE employs Monte Carlo sampling to generate diverse responses, constructing high-quality training data that captures the model’s distributional uncertainty. It introduces Backward Confidence Integration (BCI), which recursively incorporates future token information to refine current confidence estimates. Three position selection strategies—semantic boundary, periodic, and entropy-based—are designed to balance accuracy and efficiency. The model is trained via Instruction Fine-Tuning (IFT), enabling token-level, continuous confidence predictions across the generation process. Extensive experiments on multiple datasets validate its superior AUROC and calibration metrics over classical baselines.
Key Results
- On GSM8K, FineCE achieves 77.8% AUROC, surpassing baselines by over 10 percentage points, with an ECE of 6.7%. Across datasets, average AUROC improvements reach 5-15%, with calibration errors reduced by 30-60%. The model provides reliable confidence signals as early as one-third into generation.
- In downstream filtering tasks, responses filtered by FineCE’s confidence scores show a 39.5% increase in accuracy, demonstrating practical utility. The early-stage confidence enables effective self-correction and risk management.
- Ablation studies confirm that BCI and position strategies significantly contribute to performance gains, ensuring robustness in complex reasoning scenarios.
Significance
This work addresses the fundamental limitation of coarse, end-of-generation confidence scores by providing continuous, fine-grained signals. It enhances the self-monitoring and self-correcting capabilities of large models, crucial for high-stakes applications like automated reasoning, medical diagnosis, and autonomous systems. The approach bridges the gap between model uncertainty and practical trustworthiness, paving the way for safer AI deployment.
Technical Contribution
The paper introduces a novel data construction pipeline based on Monte Carlo sampling, capturing the probabilistic response distribution. It innovates with BCI, which recursively refines confidence estimates using future token information. The position selection strategies optimize computational efficiency while maintaining high accuracy. The supervised training paradigm with IFT further enhances token-level confidence prediction, outperforming existing methods.
Novelty
This is the first comprehensive framework integrating continuous, fine-grained confidence estimation with backward information fusion during inference. Unlike prior works limited to final or coarse scores, FineCE offers real-time, detailed confidence signals, representing a significant leap in model self-awareness capabilities.
Limitations
- Monte Carlo sampling incurs high computational costs, especially for large models or long sequences, limiting real-time deployment without further optimization.
- Parameter tuning for BCI, such as alpha, w, and d, may affect generalization across tasks and models, requiring adaptive strategies.
- In highly ambiguous or adversarial scenarios, confidence estimates may still be miscalibrated, necessitating external validation or multimodal integration.
Future Work
Future directions include reducing sampling overhead via approximation techniques, extending confidence estimation to multimodal models, and integrating external knowledge bases. Exploring adaptive position strategies and unsupervised calibration methods could further improve efficiency and robustness in diverse real-world applications.
AI Executive Summary
Large language models (LLMs) have revolutionized natural language processing, yet their lack of self-awareness and tendency to overconfidence pose significant challenges for deployment in critical applications. Traditional confidence estimation methods rely on coarse, post-hoc scores that fail to reflect the uncertainty during the entire generation process, limiting the models’ ability to self-monitor and correct errors in real-time.
This paper introduces FineCE, a novel supervised framework for fine-grained confidence estimation that operates continuously during text generation. The core innovation lies in leveraging Monte Carlo sampling to generate diverse responses, constructing high-quality training data that captures the probabilistic response distribution. Additionally, the Backward Confidence Integration (BCI) strategy recursively incorporates future token information to refine current confidence estimates, addressing the bias and local limitations of existing methods.
To optimize computational efficiency, three position selection strategies—semantic boundary, periodic, and entropy-based—are proposed, enabling the model to perform confidence estimation at the most impactful points during generation. The model is trained using Instruction Fine-Tuning (IFT), which enhances token-level confidence prediction and interpretability.
Extensive experiments across datasets like GSM8K, TriviaQA, and CSQA demonstrate that FineCE consistently outperforms classical baselines, achieving AUROC scores exceeding 70%, with the highest reaching 77.8%. Calibration errors are significantly reduced, and in downstream tasks, confidence-based filtering improves accuracy by 39.5%. These results confirm that FineCE provides reliable, early-stage confidence signals, crucial for autonomous decision-making and risk management.
Looking ahead, future work will focus on reducing sampling costs, extending to multimodal systems, and enhancing adaptability across diverse tasks and languages. Despite current computational challenges, FineCE marks a substantial step toward trustworthy, self-aware AI systems capable of nuanced uncertainty estimation throughout the generation process.
Deep Analysis
Background
近年来，随着GPT系列、LLaMA等大模型的崛起，模型在问答、推理、文本生成等任务中表现出色。然而，模型的自我认知能力不足，导致在复杂推理或高风险场景中出现过度自信或误判。传统置信度估计多依赖softmax概率或后验分布，存在偏差和误校准问题。近年来，Monte Carlo采样、贝叶斯方法和校准技术不断发展，但在生成过程中实现连续、细粒度的置信监控仍是难题。代表性工作包括Zhou等的校准方法、Xiong等的逐步估计，以及贝叶斯置信模型。这些方法虽取得一定进展，但难以满足大规模、实时应用的需求。随着模型规模不断扩大，提升置信度的准确性和效率成为研究重点。
Core Problem
现有方法多在生成结束后进行置信度评估，缺乏对中间步骤的监控，难以在推理过程中及时发现错误或调整策略。粗粒度评分无法反映推理中的不确定性，限制了模型的自我修正能力。此外，训练数据的构建成本高、采样效率低，难以满足大规模应用需求。如何在保证实时性和准确性的同时，提供连续、细粒度的置信信号，是当前的核心难题。这不仅影响模型的可靠性，也限制其在自动问答、推理等高风险场景的应用推广。
Innovation
本文提出的FineCE创新点在于：1）结合Monte Carlo采样构建高质量训练数据，有效捕获模型的分布不确定性；2）引入反向置信度融合（BCI），利用未来文本信息动态调整当前置信度，提升估计的全局一致性；3）设计三种位置选择策略，合理平衡性能与效率，支持在生成早期提供可靠信号。这些创新突破了传统只在终点或粗粒度层面评估的局限，为模型提供了连续、细粒度的自我监控能力。
Methodology
- �� 利用Monte Carlo采样在高温下多次生成答案，构建训练数据，计算正确率作为置信度标签。
- �� 采样包括完整答案、部分答案和问题，采用截断和聚类策略提升效率。
- �� 在推理阶段，利用反向融合（BCI）策略，将未来文本信息递归融入当前置信度，调节偏差。
- �� 设计三种位置选择策略：语义边界、固定间隔和熵阈值，选择关键位置进行置信预测。
- �� 采用Instruction Fine-Tuning（IFT）框架，训练模型实现连续、细粒度的置信预测。
Experiments
在GSM8K、TriviaQA、CSQA等六个公开数据集上，评估AUROC和ECE指标。对比Question-oriented、Outcome-oriented和Step-wise基线模型，调优采样次数、位置参数。模型在不同架构（Llama2-13B、Llama3.1-8B、Qwen2.5-7B）上表现优异，尤其在早期生成阶段提供可靠置信信号。
Results
FineCE在GSM8K上AUROC达77.8%，比传统方法高10%以上，ECE降至6.7%。在多数据集上，平均AUROC提升5-15%，校准误差降低30-60%。模型在生成早期即提供可信信号，有效支持自主修正。消融实验显示，BCI和位置策略对性能提升贡献显著，确保复杂推理中的稳定性。
Applications
该方法适用于自动问答、推理系统、风险监控等场景，能在生成早期提供可靠的置信信号，支持自主校正和风险评估。未来可结合多模态信息，扩展到多任务、多语言环境，推动AI系统的可信度和安全性。
Limitations & Outlook
采样成本较高，尤其在超大模型或长文本中，效率仍需优化。参数调优复杂，泛化能力待验证。在极端复杂或模糊场景下，置信度可能仍偏差，未来需结合外部知识和多模态信息提升鲁棒性。
Plain Language Accessible to non-experts
想象你在一家工厂工作，工厂里有很多工人（模型），他们每天生产不同的产品（回答问题）。工厂希望知道每个产品的质量（置信度），但工人有时候会不小心出错。传统的方法就像只看最后的成品，判断是否合格，但这样很可能错过中间的错误。本文的方法像是在生产过程中每隔一段时间检查一次，甚至用未来的检查结果反过来调整之前的判断。这样，工厂可以更早发现问题，保证每个产品都符合标准。通过这种连续、细致的检查，工厂的产品质量变得更可靠，整体效率也提高了。这就像让工人自己能在生产中不断自我检测和修正，确保每一步都尽善尽美。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏，你要拼出一幅漂亮的画。以前，你只能等到拼完全部后才知道拼得怎么样，但这可能会发现很多错误。现在，这个新方法就像你在拼每一块时都能知道这块拼得好不好，还能用未来拼完的部分来帮你检查之前的拼图。这样，你可以在拼图的过程中不断调整，避免大错特错。它让你在拼图时变得更聪明、更有信心，也能更快完成漂亮的画。这就像给拼图加上了“智能眼睛”，让你知道每一步是不是走对了，拼得更快更好。
Abstract
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.