Related papers: QA-Calibration of Language Model Confidence Scores

Related papers

Asymptotic Risk Calibration for Selective Question Answering [0.0]
Large language models (LLMs) may generate fluent but incorrect answers.<n>We propose A-CRC-QA, a post-hoc calibration framework for uncertainty-aware selective question answering.<n>A-CRC-QA is model-agnostic, requires no additional training, and can be combined with different uncertainty estimators.
arXiv Detail & Related papers (2026-08-12T12:45:45Z)
Uncertainty-Aware Abstention in Large Language Models with Provable Alignment Guarantees [0.5371337604556311]
Large language models (LLMs) are increasingly deployed in question answering (QA) systems.<n>They may generate hallucinated or misaligned responses without reliable confidence estimates.<n>We propose CIC, a confidence-interval-based calibration framework that converts arbitrary uncertainty scores into risk-controlled selective answering rules.
arXiv Detail & Related papers (2026-07-05T17:50:32Z)
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation [47.91529693614168]
Existing methods are predominantly answer-first, producing confidence only after generating an answer.<n>We study a confidence-first paradigm, where the model outputs its confidence before answering, interpreting this score as the model's probability of answering the question correctly.<n>We propose CoCA, a reinforcement learning framework that jointly optimize confidence calibration and answer accuracy via segmented credit assignment.
arXiv Detail & Related papers (2026-03-06T04:03:13Z)
On Calibration of Large Language Models: From Response To Capability [66.59139960234326]
Large language models (LLMs) are widely deployed as general-purpose problem solvers.<n>We introduce capability calibration, which targets the model's expected accuracy on a query.<n>Our results demonstrate that capability-calibrated confidence improves pass@$k$ prediction and inference budget allocation.
arXiv Detail & Related papers (2026-02-14T01:07:45Z)
Benchmarking Uncertainty Calibration in Large Language Model Long-Form Question Answering [7.1559850008795385]
Large Language Models (LLMs) are commonly used in Question Answering (QA) settings.<n>Existing UQ approaches remain weakly validated in scientific QA.<n>We introduce the first large-scale benchmark for evaluating UQ metrics in reasoning-demanding QA.
arXiv Detail & Related papers (2026-01-30T20:02:34Z)
Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations [49.84786015324238]
Confidence estimation (CE) indicates how reliable the answers of large language models (LLMs) are, and can impact user trust and decision-making.<n>We present a comprehensive evaluation framework for CE that measures their confidence quality on three new aspects.<n>These include robustness of confidence against prompt perturbations, stability across semantic equivalent answers, and sensitivity to semantically different answers.
arXiv Detail & Related papers (2026-01-12T23:16:50Z)
LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
arXiv Detail & Related papers (2025-12-01T11:27:09Z)
Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal [31.458406135473805]
We present UniCR, a unified framework that turns heterogeneous uncertainty evidence into a calibrated probability of correctness.<n>UniCR learns a lightweight calibration head with temperature scaling and proper scoring.<n>Experiments on short-form QA, code generation with execution tests, and retrieval-augmented long-form QA show consistent improvements in calibration metrics.
arXiv Detail & Related papers (2025-09-01T13:14:58Z)
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
arXiv Detail & Related papers (2025-06-25T07:04:49Z)
Adaptive Set-Mass Calibration with Conformal Prediction [60.47079469141295]
We develop a new calibration procedure that starts with conformal prediction to obtain a set of labels that gives the desired coverage.<n>We then instantiate two simple post-hoc calibrators: a mass normalization and a temperature scaling-based rule, tuned to the conformal constraint.
arXiv Detail & Related papers (2025-05-21T12:18:15Z)
Graph-based Confidence Calibration for Large Language Models [22.394717844099684]
We propose a novel method to develop a well-calibrated confidence estimation model. We use a weighted graph to represent the consistency among the large language models' responses to a question. We then train a graph neural network to estimate the probability of correct responses.
arXiv Detail & Related papers (2024-11-03T20:36:44Z)
Optimizing Estimators of Squared Calibration Errors in Classification [2.3020018305241337]
We propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors. Our approach advocates for a training-validation-testing pipeline when estimating a calibration error.
arXiv Detail & Related papers (2024-10-09T15:58:06Z)
Calibrating Long-form Generations from Large Language Models [34.72041258464477]
Large Language Models' (LLMs) confidence scores should align with the actual likelihood of its responses being correct. Current confidence elicitation methods and calibration metrics rely on a binary true/false assessment of response correctness. We introduce a unified calibration framework, in which both the correctness of the LLMs' responses and their associated confidence levels are treated as distributions across a range of scores.
arXiv Detail & Related papers (2024-02-09T17:00:32Z)
Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
arXiv Detail & Related papers (2023-12-14T19:09:22Z)
Uncertainty-aware Language Modeling for Selective Question Answering [107.47864420630923]
We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs. Our approach is model- and data-agnostic, is computationally-efficient, and does not rely on external models or systems.
arXiv Detail & Related papers (2023-11-26T22:47:54Z)
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics [56.629245030893685]
We introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. We provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions.
arXiv Detail & Related papers (2023-10-31T06:19:40Z)
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity [0.0]
This article aims to show that consistency and adaptivity are complementary validation targets, and that a good consistency does not imply a good adaptivity. Adapted validation methods are proposed and illustrated on a representative example.
arXiv Detail & Related papers (2023-09-12T13:58:04Z)
Calibrating AI Models for Wireless Communications via Conformal Prediction [55.47458839587949]
Conformal prediction is applied for the first time to the design of AI for communication systems. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees.
arXiv Detail & Related papers (2022-12-15T12:52:23Z)
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey [0.0]
In high-risk settings, it is important that a model produces uncertainty to reflect its own confidence and avoid failures. In this paper we survey recent works on uncertainty (UQ) for deep learning, in particular distribution-free Conformal Prediction method for its mathematical and wide applicability.
arXiv Detail & Related papers (2022-09-08T06:08:48Z)
Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics [74.28810048824519]
Question answering-based summarization evaluation metrics must automatically determine whether the QA model's prediction is correct or not. We benchmark the lexical answer verification methods which have been used by current QA-based metrics as well as two more sophisticated text comparison methods.
arXiv Detail & Related papers (2022-04-21T15:43:45Z)
Estimating Expected Calibration Errors [1.52292571922932]
Uncertainty in probabilistics predictions is a key concern when models are used to support human decision making. Most models are not intrinsically well calibrated, meaning that their decision scores are not consistent with posterior probabilities. We build an empirical procedure to quantify the quality of $ECE$ estimators, and use it to decide which estimator should be used in practice for different settings.
arXiv Detail & Related papers (2021-09-08T08:00:23Z)
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration [118.26862029820447]
We introduce a new notion -- emphdecision calibration -- that requires the predicted distribution and true distribution to be indistinguishable'' to a set of downstream decision-makers. Decision calibration improves decision-making on skin lesions and ImageNet classification with modern neural network.
arXiv Detail & Related papers (2021-07-12T20:17:28Z)
Distribution-free uncertainty quantification for classification under label shift [105.27463615756733]
We focus on uncertainty quantification (UQ) for classification problems via two avenues. We first argue that label shift hurts UQ, by showing degradation in coverage and calibration. We examine these techniques theoretically in a distribution-free framework and demonstrate their excellent practical performance.
arXiv Detail & Related papers (2021-03-04T20:51:03Z)
Localized Calibration: Metrics and Recalibration [133.07044916594361]
We propose a fine-grained calibration metric that spans the gap between fully global and fully individualized calibration. We then introduce a localized recalibration method, LoRe, that improves the LCE better than existing recalibration methods.
arXiv Detail & Related papers (2021-02-22T07:22:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.