Papers with calibration error
Calibration of Pre-trained Transformers (2020.emnlp-main)
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| Challenge: | Pre-trained Transformers dominate benchmark tasks but use a large number of self-attention heads across many layers in a way that is difficult to unpack. |
| Approach: | They analyze pre-trained Transformer models' posterior probabilities to determine whether they are calibrated for three tasks: natural language inference, paraphrase detection, and commonsense reasoning. |
| Outcome: | The models are calibrated in-domain and out-of-domain, and their calibration error out-domain can be as much as 3.5x lower. |
Predict the Next Word: <Humans exhibit uncertainty in this task and language models _____> (2024.eacl-short)
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| Challenge: | Language models (LMs) are statistical models trained to assign probability to human-generated text. |
| Approach: | They evaluate language models' ability to reproduce variability that humans exhibit in the ‘next word prediction’ task. |
| Outcome: | The language models are trained to assign probability to human-generated text . they exhibit low calibration to human uncertainty, and advise against it . |
Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models (2026.acl-srw)
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| Challenge: | Model calibration is commonly used as a proxy for reliability, but low calibration error does not necessarily imply reliable decision rules. |
| Approach: | They investigate the calibration error gap in generative language models using the TOFU benchmark and attribution-based shortcut detection. |
| Outcome: | The proposed model calibrations achieve low calibration error compared to pretrained models and retain low calibration despite reduced accuracy on the forget split. |
Calibrating Zero-shot Cross-lingual (Un-)structured Predictions (2022.emnlp-main)
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| Challenge: | Existing need for model calibration when natural language models are deployed in critical tasks. |
| Approach: | They compare model calibration methods in a context of zero-shot cross-lingual transfer with pre-trained language models. |
| Outcome: | The proposed method fails to calibrate more complex confidence estimations in structured predictions compared to expressive alternatives like Gaussian Process Calibration. |
On the Effects of Transformer Size on In- and Out-of-Domain Calibration (2021.findings-emnlp)
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| Challenge: | Large pre-trained transformer language models are notoriously expensive to train . prior work has developed smaller, more compact models to reduce training costs . |
| Approach: | They propose to develop smaller, more compact transformer language models which can be calibrated in-domain . they show that smaller models can achieve competitive calibration compared to larger models . |
| Outcome: | The proposed models achieve competitive calibration and better calibration than larger models on a wide range of tasks. |
Posterior Calibrated Training on Sentence Classification Tasks (2020.acl-main)
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| Challenge: | Existing methods for posterior calibration have been used to correct poorly calibrated posterior probabilities. |
| Approach: | They propose a posterior calibration procedure that optimizes posterior probability distributions while minimizing calibration errors. |
| Outcome: | The proposed procedure reduces calibration error and improves performance on both objectives. |
Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers (2022.findings-acl)
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| Challenge: | Existing methods for posterior calibration return uncalibrated estimations of class posteriors, thus leading to poorer generalization. |
| Approach: | They propose an end-to-end trained calibrator that directly optimizes the objective while minimizing the difference between predicted and empirical posterior probabilities. |
| Outcome: | The proposed calibrator reduces calibration error and improves performance on benchmark NLP classification tasks. |
A Data Cartography based MixUp for Pre-trained Language Models (2022.naacl-main)
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| Challenge: | Using MixUp, additional samples are generated during training by combining random pairs of training samples and their labels. |
| Approach: | They propose a new MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples. |
| Outcome: | The proposed method achieves competitive performance using a smaller subset of training data compared with strong baselines and yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings. |
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback (2023.emnlp-main)
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, Christopher Manning
| Challenge: | Recent studies have shown that unsupervised pre-training produces large language models whose conditional probabilities are remarkably well-calibrated. |
| Approach: | They propose to use verbalized confidences to extract confidence from large language models with reinforcement learning from human feedback to improve their accuracy. |
| Outcome: | The proposed methods reduce the expected calibration error by 50% for RLHF-LMs such as ChatGPT, GPT-4, and Claude. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
Making Heads and Tails of Models with Marginal Calibration for Sparse Tagsets (2021.findings-emnlp)
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| Challenge: | despite high accuracy, modern neural networks can still suffer from severe miscalibration. |
| Approach: | They propose to use tag frequency grouping to measure calibration error in different frequency bands to reduce error. |
| Outcome: | The proposed techniques reduce calibration error across the marginal distribution for two existing sequence taggers. |
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis (2022.findings-emnlp)
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Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Pre-trained language models (PLMs) have gained increasing popularity due to compelling prediction performance in diverse natural language processing tasks. |
| Approach: | They compare three popular options for encoding and Temp Scaling for PLMs . they recommend using Temp Loss as uncertainty quantifier and Focal Loss for fine-tuning . |
| Outcome: | Using pre-trained language models, we compare three options on NLP classification tasks and domain shift. |
On Diversified Preferences of Large Language Model Alignment (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) can be fine tuned with human feedback, but human preferences can be diversified due to annotators’ different tastes, which hinders the effectiveness of LLM alignment methods. |
| Approach: | They propose a calibration error metric to evaluate large language models (LLMs) and a multi-objective reward learning method to enhance the calibration performance of RMs on shared preferences. |
| Outcome: | The proposed model can be adopted as a key calibration error and MORE can achieve superior alignment performance. |
InternalInspector I2: Robust Confidence Estimation in LLMs through Internal States (2024.findings-emnlp)
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Mohammad Beigi, Ying Shen, Runing Yang, Zihao Lin, Qifan Wang, Ankith Mohan, Jianfeng He, Ming Jin, Chang-Tien Lu, Lifu Huang
| Challenge: | Large Language Models (LLMs) often struggle with generating reliable outputs, often producing high-confidence inaccuracies known as hallucinations. |
| Approach: | They propose a framework that leverages contrastive learning on internal states including attention states, feed-forward states, and activation states of all layers to enhance confidence estimation in LLMs. |
| Outcome: | The framework outperforms existing methods in the hallucination detection benchmark HaluEval and the previous methods at the same time. |
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students (2024.emnlp-main)
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| Challenge: | Existing student models use study data like student's past responses to predict the probability a student can recall a flashcard. |
| Approach: | They propose to use student models to predict recall of flashcards to build a content-aware student model that uses deep knowledge tracing, retrieval, and BERT to predict student recall. |
| Outcome: | The proposed content-aware student model outperforms existing student models in AUC and calibration error and is more efficient than SOTA. |
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data (2024.emnlp-main)
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| Challenge: | Existing calibration methods negatively impact model accuracy due to the lack of diversity of validation data. |
| Approach: | They propose a calibration method that incorporates synthetic data without compromising accuracy. |
| Outcome: | The proposed method improves model accuracy on real data and reduces calibration error by 34% on four different tasks. |
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing (2023.findings-emnlp)
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| Challenge: | CASENT predicts ultra-fine entities mentioned in text into types with calibrated confidence scores. |
| Approach: | They propose a model that predicts ultra-fine entities with calibrated confidence scores for entity typing. |
| Outcome: | The proposed model outperforms existing models in terms of F1 score and calibration error while achieving 50 times faster inference speed. |
Quantized Can Still Be Calibrated: A Unified Framework to Calibration in Quantized Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to quantify uncertainty of large language models (LLMs) but their influence on uncertainty calibration remains unexplored. |
| Approach: | They propose an analytic method to estimate the upper bound of calibration error (UBCE) for quantized LLMs and propose a method to recover calibration errors through soft-prompt tuning. |
| Outcome: | The proposed method improves the calibration accuracy of quantized models on multiple datasets and LLMs. |