A Calibrated Reflection Approach for Enhancing Confidence Estimation in LLM s (2025)

Abstract: A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate their confidence, enabling systems to determine when to trust model outputs and when to seek human intervention. In this paper, we present a Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs, a framework that combines structured reasoning with distance-aware calibration techniques. Our approach introduces three key innovations: (1) a Maximum Confidence Selection (MCS) method that comprehensively evaluates confidence across all possible labels, (2) a reflection-based prompting mechanism that enhances reasoning reliability, and (3) a distance-aware calibration technique that accounts for ordinal relationships between labels. We evaluate our framework across diverse datasets, including HelpSteer2, Llama T-REx, and an internal conversational dataset, demonstrating its effectiveness across both conversational and fact-based classification tasks. This work contributes to the broader goal of developing reliable and well-calibrated confidence estimation methods for LLMs, enabling informed decisions about when to trust model outputs and when to defer to human judgement.

## introduction llms have revolutionized many domains, but ensuring their outputs are trustworthy remains a pressing challenge. a key aspect of this trustworthiness is confidence estimation-developing methods to gauge the likelihood of an llm's answer being correct. this is challenging due to the frequent miscalibration of their confidence scores. in traditional classification, a model's predicted probability can serve as a confidence estimate, but these probabilities must be well-calibrated to be meaningful. calibration ensures that if a model claims 90% confidence, it should be correct about 90% of the time. in practice, an llm might generate a fluent, plausible-sounding answer with near-certain confidence, yet be factually wrong -an undesirable situation if not detected by a confidence calibration mechanism. techniques like chain-of-thought reasoning and self-consistency have been explored to improve the model's self-evaluation, yet often yield overconfident estimates. the challenge of confidence estimation becomes more nuanced in ordinal classification problems (e.g., user ratings, sentiment levels, risk assessments). unlike nominal categories, ordinal labels enable consideration of distance between predictions: mistaking a rating of 5 for 4 is a smaller error than mistaking it for 1. however, most existing confidence estimation methods treat each label independently, failing to differentiate between close and far errors. this limitation is particularly critical in sensitive settings where miscalibrated confidence on an ordinal decision can have serious consequences. existing approaches to confidence estimation in llms can be broadly categorized into four categories: (1) probability-based methods that utilize model logits and calibration algorithms @xcite , but are restricted by the availability of model logits. (2) fine-tuning methods require extensive training data and, while effective on in-domain datasets, struggle with generalization to out-ofdomain scenarios. (3) prompting-based techniques elicit self-evaluation from the model @xcite . despite their intuitiveness appeal, these methods frequently yield overconfident estimates, undermining their reliability. (4) ensemble methods @xcite can enhance reliability, but they incur significant computational overhead and do not inherently address the fundamental issue of calibrating confidence scores. a critical gap in current research is the lack of methods that account for ordinal relationships between labels. recent work @xcite suggests that effective confidence estimation requires both robust reasoning capabilities and proper cali-bration of confidence scores. in this paper, we propose a novel framework for confidence estimation that addresses these limitations through a synergistic combination of advanced prompting techniques and distance-aware calibration. our approach integrates: we evaluate the calibrated reflection approach on diverse datasets, including conversational and fact-based classification tasks. across multiple benchmarks, our framework consistently improves confidence calibration and overall predictive performance. we observe significantly lower expected calibration error (ece) and brier score (indicating better alignment between predicted confidence and actual accuracy) compared to baselines, while also achieving higher discrimination metrics like auroc and auprc. these improvements hold without any fine-tuning of the llm and making a single llm invokation, making our framework readily applicable in real-world scenarios. our approach, integrating structured reasoning with distance-aware calibration, significantly advances confidence estimation for llms, enabling wellcalibrated confidence scores in ordinal classification, thereby enhancing reliability and trustworthiness in llm-driven applications. ## related work existing approaches to confidence estimation in llms have evolved from basic probability-based methods to more sophisticated techniques incorporating multiple strategies. 1) early methods @xcite focused on sequence probability, which estimates confidence by computing average log probabilities assigned to output tokens. while these approaches have been applied in various contexts, including close tasks and qa setups @xcite , they require wellcalibrated probabilities to accurately reflect correctness @xcite , and generally don't represent the actual probability of the predicted results in llms. 2) verbalized confidence estimation has emerged as a direct approach where llms assess their own confidence @xcite . chain of thought prompting @xcite improves explanation and justification by breaking down reasoning into smaller steps. selfconsistency @xcite estimates confidence by evaluating consensus across multiple reasoning paths. recent extensions have incorporated debate-style prompting @xcite and reflection prompting @xcite , where models internally challenge their decisions and self-assess potential errors. 3) model aggregation methods: combine signals from multiple sources for confidence estimation. while ensemble methods @xcite ) merge outputs from multiple llms at high computational cost, the rescorer @xcite offers a more efficient approach by aggregating multiple roscoe metrics into comprehensive confidence scores. 4) surrogate models @xcite have been proposed to assess main model outputs, with extensions like mpc @xcite incorporating knowledge injection from stronger models. the trained probe method @xcite represents a newer approach, training lightweight models on llm internal representations to extract confidence signals. uncertainty-aware instruction tuning (uait) @xcite presents a promising direction in self-training, aligning llms' uncertainty perception with their outputs. ## experimentation in this section, we outline the datasets, evaluation metrics, and comparison methods. we experiment with claude-3-haiku (anthropic, 2024) (closed-source), and mistral-7b-instruct (opensource) models @xcite , and perform all experiments in a zero-shot setting, utilizing a fixed temperature of 0.1, with single llm invokation.