mlsanityData to 7 Oct 2026

Thermometer: Towards Universal Calibration for Large Language Models

arXiv 2403.08819 ↗First posted 20 Feb 2024cs.LG cs.CL stat.MLcited by 38

Not on the map. Why?

Abstract (arXiv)

We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational requirements of LLMs as from their versatility, which allows them to be applied to diverse tasks. Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs. THERMOMETER learns an auxiliary model, given data from multiple tasks, for calibrating a LLM. It is computationally efficient, preserves the accuracy of the LLM, and produces better-calibrated responses for new tasks. Extensive empirical evaluations across various benchmarks demonstrate the effectiveness of the proposed method.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 39 of its 52 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

39 of 52 references.

References · 39

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  7. 2017On Calibration of Modern Neural Networkscited by 4,860
  8. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  9. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  10. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  11. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  12. 2022Language Models (Mostly) Know What They Knowcited by 1,783
27 more
  1. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  2. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  3. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  4. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  5. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 461
  6. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 439
  7. 2019Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibrationcited by 359
  8. 2020Calibrating Deep Neural Networks using Focal Losscited by 353
  9. 2020Calibration of Pre-trained Transformerscited by 307
  10. 2021What Are Bayesian Neural Network Posteriors Really Like?cited by 307
  11. 2020Reducing conversational agents' overconfidence through linguistic calibrationcited by 209
  12. 2019MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehensioncited by 183
  13. 2022Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysiscited by 104
  14. 2017Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observationscited by 100
  15. 2023Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineeringcited by 91
  16. 2023On the Calibration of Large Language Models and Alignmentcited by 72
  17. 2018Improving Question Answering by Commonsense-Based Pre-Trainingcited by 66
  18. 2022Prototypical Calibration for Few-shot Learning of Language Modelscited by 58
  19. 2022A Close Look into the Calibration of Pre-trained Language Modelscited by 57
  20. 2022Sample-dependent Adaptive Temperature Scaling for Improved Calibrationcited by 48
  21. 2021Knowing More About Questions Can Help: Improving Calibration in Question Answeringcited by 47
  22. 2021Top-label calibration and multiclass-to-binary reductionscited by 46
  23. 2022Robust Calibration with Multi-domain Temperature Scalingcited by 39
  24. 2022On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliencycited by 31
  25. 2023Generative Calibration for In-context Learningcited by 20
  26. 2024Enhancing In-context Learning via Linear Probe Calibrationcited by 16
  27. 2022Are you using test log-likelihood correctly?cited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 17; 2026: 17.

38 papers.

Sort:

By area:

  1. 2025CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Modelscited by 28
  2. 2024Calibrating Reasoning in Language Models with Internal Consistencycited by 27
  3. 2025Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approachcited by 25
  4. 2025Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibratorcited by 19
  5. 2025Know What You Don't Know: Uncertainty Calibration of Process Reward Modelscited by 19
  6. 2024Black-box Uncertainty Quantification Method for LLM-as-a-Judgecited by 18
  7. 2024Large Language Model Confidence Estimation via Black-Box Accesscited by 14
  8. 2025Activated LoRA: Fine-tuned LLMs for Intrinsicscited by 9
  9. 2025Always Tell Me The Odds: Fine-grained Conditional Probability Estimationcited by 9
  10. 2025Beyond Accuracy: The Role of Calibration in Self-Improving Large Language Modelscited by 7
  11. 2025Conformal Information Pursuit for Interactively Guiding Large Language Modelscited by 7
  12. 2026BaseCal: Unsupervised Confidence Calibration via Base Model Signalscited by 7
26 more
  1. 2025B-score: Detecting biases in large language models using response historycited by 6
  2. 2024Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuningcited by 5
  3. 2026Streaming Hallucination Detection in Long Chain-of-Thought Reasoningcited by 5
  4. 2026LLMs as Signal Detectors: Sensitivity, Bias, and the Temperature-Criterion Analogycited by 5
  5. 2026Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigationcited by 5
  6. 2025Logits are All We Need to Adapt Closed Modelscited by 4
  7. 2026Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMscited by 4
  8. 2025Annotation-Efficient Universal Honesty Alignmentcited by 3
  9. 2025NeuCLIP: Efficient Large-Scale CLIP Training with Neural Normalizer Optimizationcited by 3
  10. 2026Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Spacecited by 3
  11. 2026Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Modelscited by 3
  12. 2025Distilling Calibration via Conformalized Credal Inferencecited by 2
  13. 2025SCOPE: Stochastic and Counterbiased Option Placement for Evaluating Large Language Modelscited by 2
  14. 2026Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?cited by 2
  15. 2025Meta-Learning at Scale for Large Language Models via Low-Rank Amortized Bayesian Meta-Learningcited by 1
  16. 2026DMAP: A Distribution Map for Textcited by 1
  17. 2026The Well-Tempered Classifier: Some Elementary Properties of Temperature Scalingcited by 1
  18. 2026Process Supervision of Confidence Margin for Calibrated LLM Reasoningcited by 1
  19. 2026MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordinationcited by 1
  20. 2026CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distractioncited by 1
  21. 2025A Library of LLM Intrinsics for Retrieval-Augmented Generationcited by 0
  22. 2025Experience Scaling: Post-Deployment Evolution For Large Language Modelscited by 0
  23. 2026Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revisioncited by 0
  24. 2026Inducing Artificial Uncertainty in Language Modelscited by 0
  25. 2026Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibrationcited by 0
  26. 2026Is This Your Final Answer? Cross-Contextual Consistency as a Measure of LLM Credibilitycited by 0