Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling

• URL: http://arxiv.org/abs/2604.13271v1
• Date: Tue, 14 Apr 2026 20:03:08 GMT
• Title: Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
• Abstract summary: Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks.<n>LLMs-generated confidence scores are often biased and unreliable, frequently exhibiting systematic overconfidence.<n>We propose a novel Twin-Pass Chain of Thought (CoT)-Ensembling methodology for improving confidence estimation.
• Score: 1.8332654441845688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks, including 3GPP specification analysis and O-RAN network troubleshooting. However, a critical limitation remains: LLM-generated confidence scores are often biased and unreliable, frequently exhibiting systematic overconfidence. This lack of trustworthy self-assessment makes it difficult to verify model outputs and safely rely on them in practice. In this paper, we study confidence calibration in telecom-domain LLMs using the representative Gemma-3 model family (4B, 12B, and 27B parameters), evaluated on TeleQnA, ORANBench, and srsRANBench. We show that standard single-pass, verbalized confidence estimates fail to reflect true correctness, often assigning high confidence to incorrect predictions. To address this, we propose a novel Twin-Pass Chain of Thought (CoT)-Ensembling methodology for improving confidence estimation by leveraging multiple independent reasoning evaluations and aggregating their assessments into a calibrated confidence score. Our approach reduces Expected Calibration Error (ECE) by up to 88% across benchmarks, significantly improving the reliability of model self-assessment. These results highlight the limitations of current confidence estimation practices and demonstrate a practical path toward more trustworthy evaluation of LLM outputs in telecommunications.

Related papers

• Process Supervision of Confidence Margin for Calibrated LLM Reasoning [52.373121066425455]
  Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability.<n>Yet, outcome-based reward often incentivizes models to be overconfident, leading to hallucinations, unreliable confidence-based control, and unnecessary compute allocation.<n>We introduce Reinforcement Learning with Confidence Margin (textbfRLCM), a calibration-aware RL framework that jointly optimize correctness and confidence reliability.
  arXiv  Detail & Related papers  (2026-04-25T14:40:13Z)
• Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models [11.878003218072765]
  Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks.<n>We propose a monotone confidence fusion framework to merge dual-channel signals and cross-channel consistency to estimate correctness.<n>Experiments on diverse open-source and closed-source MLLMs show that our method consistently yields more reliable confidence estimates.
  arXiv  Detail & Related papers  (2026-04-19T06:07:39Z)
• BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence [22.382291859991472]
  We introduce the Behavioral Alignment Score (BAS), a decision-theoretic metric for evaluating how well large language models support abstention-aware decision making.<n>BAS is derived from an explicit answer-or-abstain utility model and aggregates realized utility across a continuum of risk thresholds.<n>We show theoretically that truthful confidence estimates uniquely maximize expected BAS utility, linking calibration to decision-optimal behavior.
  arXiv  Detail & Related papers  (2026-04-03T17:44:32Z)
• Direct Confidence Alignment: Aligning Verbalized Confidence with Internal Confidence In Large Language Models [6.918665116014629]
  Internal confidence of a model, derived from token probabilities, is not well aligned with its verbalized confidence.<n>We propose Direct Confidence Alignment (DCA) to align an LLM's verbalized confidence with its internal confidence.
  arXiv  Detail & Related papers  (2025-12-12T19:29:05Z)
• BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents [58.05949210993854]
  We investigate whether search agents have the ability to communicate their own confidence through verbalized confidence scores after long sequences of actions.<n>We propose Test-Time Scaling (TTS) methods that use confidence scores to determine answer quality, encourage the model to try again until reaching a satisfactory confidence level.
  arXiv  Detail & Related papers  (2025-10-27T15:58:51Z)
• Calibrating Verbalized Confidence with Self-Generated Distractors [24.56911906044891]
  We introduce Distractor-Normalized Coherence (DINCO)<n>DINCO estimates and accounts for an LLM's suggestibility bias by having the model its confidence independently across several self-generated distractors.<n>We frame the popular approach of self-consistency as leveraging coherence across sampled generations, and normalized verbalized confidence as leveraging coherence across validations on incompatible claims.
  arXiv  Detail & Related papers  (2025-09-29T21:41:22Z)
• Can Large Language Models Express Uncertainty Like Human? [71.27418419522884]
  We release the first diverse, large-scale dataset of hedging expressions with human-annotated confidence scores.<n>We conduct the first systematic study of linguistic confidence across modern large language models.
  arXiv  Detail & Related papers  (2025-09-29T02:34:30Z)
• ConfTuner: Training Large Language Models to Express Their Confidence Verbally [58.63318088243125]
  Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as science, law, and healthcare.<n>LLMs are often observed to generate incorrect answers with high confidence, a phenomenon known as "overconfidence"
  arXiv  Detail & Related papers  (2025-08-26T09:25:32Z)
• Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation [63.49409574310576]
  Large language models (LLMs) exhibit overconfidence, assigning high confidence scores to incorrect predictions.<n>We introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation.<n>Our code and all baselines used in the paper are available on GitHub.
  arXiv  Detail & Related papers  (2025-08-16T13:29:35Z)
• Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution [20.607071807794195]
  Large Language Models (LLMs) are widely used as automated judges, where practical value depends on both accuracy and trustworthy, risk-aware judgments.<n>Existing approaches predominantly focus on accuracy, overlooking the necessity of well-calibrated confidence.<n>We advocate a shift from accuracy-centric evaluation to confidence-driven, risk-aware LLM-as-a-Judge systems.
  arXiv  Detail & Related papers  (2025-08-08T11:11:22Z)
• SteerConf: Steering LLMs for Confidence Elicitation [11.872504642312705]
  Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence.<n>We propose SteerConf, a novel framework that systematically steers LLMs' confidence scores to improve their calibration and reliability.
  arXiv  Detail & Related papers  (2025-03-04T18:40:49Z)
• Fact-Level Confidence Calibration and Self-Correction [64.40105513819272]
  We propose a Fact-Level framework that calibrates confidence to relevance-weighted correctness at the fact level. We also develop Confidence-Guided Fact-level Self-Correction ($textbfConFix$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones.
  arXiv  Detail & Related papers  (2024-11-20T14:15:18Z)
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection [90.71323430635593]
  We propose a novel self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers. Building upon this paradigm, we introduce a two-step framework, which firstly instructs LLM to reflect and provide justifications for each candidate answer. This framework can be seamlessly integrated with existing approaches for superior self-detection.
  arXiv  Detail & Related papers  (2024-03-15T02:38:26Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.