Reducing Conversational Agents’ Overconfidence Through Linguistic Calibration

Attention Is All You Need

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Language Models are Few-Shot Learners

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Advances in Neural Information Processing Systems 29

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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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Gaussian Error Linear Units (GELUs)

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Semantic Parsing on Freebase from Question-Answer Pairs

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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

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DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

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The Pushshift Reddit Dataset

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Recipes for Building an Open-Domain Chatbot

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CTRL: A Conditional Transformer Language Model for Controllable Generation

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Towards a Human-like Open-Domain Chatbot

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Wizard of Wikipedia: Knowledge-Powered Conversational agentsPreprint

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Neural Text Generation with Unlikelihood Training

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What makes a good conversation? How controllable attributes affect human judgments

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Calibration of Pre-trained Transformers

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Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations

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Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills

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How Can We Know <i>When</i> Language Models Know? On the Calibration of Language Models for Question Answering

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Retrieve and Refine: Improved Sequence Generation Models For Dialogue

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Recipes for Safety in Open-domain ChatbotsPreprint

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Controlling Style in Generated DialoguePreprint

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Calibrating Structured Output Predictors for Natural Language Processing

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Chain-of-Verification Reduces Hallucination in Large Language Models

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"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust

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Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

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A Survey of Confidence Estimation and Calibration in Large Language Models . -

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Mirages. On Anthropomorphism in Dialogue Systems

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Do large language models have a legal duty to tell the truth?

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ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

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ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

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FaithDial : A Faithful Benchmark for Information-Seeking Dialogue

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A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

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Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

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Towards Human-centered Proactive Conversational Agents

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Prompting is not a substitute for probability measurements in large language models

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Know Your Limits: A Survey of Abstention in Large Language Models

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Chain-of-Verification Reduces Hallucination in Large Language Models

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Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models

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RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding

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One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations

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Fostering effective hybrid human-LLM reasoning and decision making

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Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

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Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions

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Diving Deep into Modes of Fact Hallucinations in Dialogue Systems

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Confronting verbalized uncertainty: Understanding how LLM's verbalized uncertainty influences users in AI-assisted decision-making

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Chatbots With Attitude: Enhancing Chatbot Interactions Through Dynamic Personality Infusion

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Creating General User Models from Computer Use

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Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?

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Defining and evaluating decision and composite risk in language models applied to natural language inference

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ChatGPT Incorrectness Detection in Software Reviews

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Redesigning healthcare service delivery processes through medical chatbot integrations: balancing chatbot features and patients’ individual differences

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LM-PACE: Confidence Estimation by Large Language Models for Effective Root Causing of Cloud Incidents

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Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts

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Reliable Text-to-SQL with Adaptive Abstention

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Calibrated Interpretation: Confidence Estimation in Semantic Parsing

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Natural Expression of a Machine Learning Model's Uncertainty Through Verbal and Non-Verbal Behavior of Intelligent Virtual Agents

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Simple Token-Level Confidence Improves Caption Correctness

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Understanding Knowledge Drift in LLMs Through Misinformation

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Addressing Overreliance on AI

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Examining epistemological challenges of large language models in law

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Confidence Estimation For LLM-Based Dialogue State Tracking

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Conformal Autoregressive Generation: Beam Search with Coverage Guarantees

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EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering

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Refining medical large language models: key insights from instruction tuning

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When to Read Documents or QA History: On Unified and Selective Open-domain QA

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Exploring In-Context Learning for Knowledge Grounded Dialog Generation

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Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models

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SSR-RAG: A smart Retrieval-Augmented Generation Framework with Rollback Mechanisms for Safer and More Accurate Answers

Wang, H., Qi, Z., Wang, N., Yu, Z., Li, Z., & Lv, W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227721 · Cited by 2 · see citing works

CarICL: Mitigating causal hallucinations to enhance event causality identification

Feng, Y., Li, L., Qin, X., & Li, Z. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104500 · Cited by 2 · see citing works

When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation

Ren, J. F., Li, B., Xu, Z., Zhang, X., Fayek, H. M., & Li, X. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792358 · Cited by 5 · see citing works

Towards In-Context Non-Expert Evaluation of Reflection Generation for Counselling Conversations

Wu, Z., Balloccu, S., Helaoui, R., Recupero, D. R., & Riboni, D. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.9 · Cited by 2 · see citing works

Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?

Liu, K., Casper, S., Hadfield-Menell, D., & Andreas, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.291 · Cited by 2 · see citing works

Automatic Validation of Unstructured LLM-Generated Outputs: An Approach for Q&A Applications in Process Automation

Elsheikh, M., Schoch, N., Palacio, S., Strem, N., Stärk, K. D. C., & Hoernicke, M. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205595 · Cited by 1 · see citing works

Robust Uncertainty Quantification for Factual Generation of Large Language Models

Zhang, Y., Yang, Z., & Zhou, L. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227634 · Cited by 1 · see citing works

Supporting Learners' Use of Imperfect Generative Pedagogical Chatbots: The Role of Chatbot Response Uncertainty and Reduced Verbosity

Li, T. W., Song, Y., Sundaram, H., & Karahalios, K. G. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791940 · Cited by 1 · see citing works

Improved Instruction Ordering in Recipe-Grounded Conversation

Le, D., Guo, R., Xu, W. H., & Ritter, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.561 · Cited by 5 · see citing works

A Judge Should Know What Changed:Construct Validity for LLM-as-a-Judge Evaluation

Chen, J., Chen, W., Lin, Z., & Vong, C. M. (2026) · arXiv · doi:10.48550/arxiv.2608.24419

An Improved Chinese Pause Fillers Prediction Module Based on RoBERTa

Yu, L. H., Zhou, X., & Niu, F. (2023) · Applied Sciences · doi:10.3390/app131910652

Estimating the Confidence of Language Model Generation using Training Data

Yoshikawa, H., & Okazaki, N. (2025) · Journal of Natural Language Processing · doi:10.5715/jnlp.32.91

Can I Trust You? Advancing GUI Task Automation with Action Trust Score

Mei, H., Gao, D., Wei, X., Yang, X., & Shou, M. Z. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755618

Step-Aware Confidence Estimation in Natural Language Reasoning

Xia, C., Liu, Z., Chen, F., Zhou, H., & Wang, C. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228321

Overreliance on AI in Oil and Gas Cybersecurity: A Review of Risks and Mitigation Strategies

Alkhalaf, D., Shareef, R., Almousa, S., & Alshamrani, S. (2026) · IPTC Summit on AI for the Energy Industry · doi:10.2523/iptc-25174-ms

Cross-Platform Analysis of Hedging and Epistemic Stance in Conversational AI: A Comparative Study of Problem-Solving Discourse in ChatGPT, Claude, and GeminiPreprint

Wang, X., & Sarand, S. F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6321223

Towards Uncertainty-Aware Low-Bit Quantized LLMs for On-Device Inference

Sparrenberg, L., Schneider, T., Deußer, T., Berger, A., & Sifa, R. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11400739

Socratic Elenchus-inspired multi-agent debate for mitigating hallucinations in large language models

Huang, J., Han, T., Yu, N., & Yi, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132208

OpenCD: Empowering Diagnosis of Children's Mathematical Cognition through Open-ended Multimodal Tasks

Zheng, Z., Yu, C., Chen, W., Song, M., Liu, B., Liu, J., Wang, S., Wang, X. Q., Cai, J., & Shi, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790318

Beyond Answers: Trajectory Semantic Entropy for Reliable Uncertainty Quantification in LLMs

Song, B., Feng, Z., Wang, S., & Zhou, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463008

More is not better: Visual uncertainty cues and the fragility of trust calibration in LLM-assisted decision making

Ojewale, V., Ryan, J., Venkatasubramanian, S., & Boykin, C. M. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100307

Large language models for DAMA-aligned data management in telecommunication systems: a review of OSS/BSS and 5G/6G applications

Alsaleh, O. I. (2026) · Telecommunication Systems · doi:10.1007/s11235-026-01462-8

Uncertain AI – Better AI? Effects of Uncertainty Indicators in Collaborative Learning with a Human Peer or an Artificial Intelligence

Radtke, A., & Schnaubert, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29760-0_54

Beyond Benchmarks: A User-Centric Framework for Evaluating Large Language Models

Abdrabou, Y., Abdelrahman, Y., Bozkir, E., Mazen, Y., Alt, F., & Kasneci, E. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816227

Sycophantic Metacognition: Investigating the Dunning-Kruger Effect in Large Language Model Self-Assessment

Delaflor, M., & Toxtli, C. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816233

TRACE-QA: Task-routed constraint elimination for auditable multi-agent question answering

Lu, J., Chen, H., Zhu, Y., Xu, J., & Xu, A. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01141-3 · Cited by 1 · see citing works

From Calibrated Confidence to Calibrated Reliance: Human-Aware Confidence Communication for AI-Assisted Decision Making

Wang, Z., & Hu, K. (2026) · ACM Transactions on Social Computing · doi:10.1145/3843742

Dynamic adaptive ensemble decoding under uncertainty for large language models

Ma, W., Feng, X., Huang, L., Huang, Y., Ye, Y., Feng, X., & Qin, B. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116931

When AI Says It Is Not Sure: AI Uncertainty Expression, Trust, and Purchase Hesitation in Online Health Product Recommendation

Liu, D., & Niu, X. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21090291

Systematic review and comparative synthesis of cross lingual question answering systems for low resource Indic languages

Savant, S., Deshpande, U. U., & D’Silva, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01750-x

Why we believe chatbots: trust calibration as a design problem

Jaidka, K., & Cai, M. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1935527 · Cited by 1 · see citing works

The circular economy, explained by AI

Morseletto, P., & Bocken, N. M. P. (2026) · Journal of Industrial Ecology · doi:10.1007/s44498-026-00192-z

Pragmatics-zombies

Firt, E., & Nir, B. (2026) · Pragmatics and Society · doi:10.1075/ps.25161.fir

CoSu-UQ: Uncertainty Quantification for Reasoning via Confidence and Semantic Support

Feng, Y., Lin, Q., Zhang, Y., Yang, Z., & Zhou, L. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11730028

LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language ModelsPreprint

Stengel-Eskin, E., Hase, P., & Bansal, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.21028 · Cited by 1 · see citing works

Retrieval-Augmented Linguistic CalibrationPreprint

Yeh, Y.-F., Tao, L., Dong, M., Huang, T., Yu, J., Torr, P. H. S., & Xu, C. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.19344

Linguistic Calibration of Long-Form GenerationsPreprint

Band, N., Li, X., Ma, T., & Hashimoto, T. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.00474

Conformal Linguistic Calibration: Trading-off between Factuality and Specificity

Jiang, Z., Liu, A., & Durme, B. V. (2025) · neural information processing systems · doi:10.52202/085713-4338 · Cited by 1 · see citing works

Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models

Sicilia, A., Kim, H., Chandu, K. R., Alikhani, M., & Hessel, J. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.697 · Cited by 7 · see citing works