UNCERTAINTY ESTIMATION

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction (2023.eacl-main)

Challenge:	State-of-the-art classification and regression models are often not well calibrated and can be inaccurate.
Approach:	They quantify calibration of pre- trained language models for text regression . they apply uncertainty estimates to augment training data in low-resource domains .
Outcome:	The proposed model calibrations improve performance and generalizability in low-resource settings.

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Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob Gardner, Dan Roth, Muhao Chen

Test-time Adaptation for Machine Translation Evaluation by Uncertainty Minimization (2023.acl-long)

Challenge:	Existing models for event temporal relation extraction are based on data-driven machine learning . however, TEMPREL extraction is not accurate under distribution shifts.
Approach:	They propose to conduct counterfactual analysis to attenuate the effects of two types of training biases: the event trigger bias and the frequent label bias.
Outcome:	The proposed model extracts TempRel and timelines more faithfully compared to SOTA methods . it is based on two perspectives: one is to extract genuinely based upon contextual description . the other is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text .

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Runzhe Zhan, Xuebo Liu, Derek F. Wong, Cuilian Zhang, Lidia S. Chao, Min Zhang

Machine Translation

Challenge:	evaluators of machine translation systems often use text-based metrics to evaluate performance . however, these metrics lack semantic-level information and exhibit poor correlation with human ratings . authors propose a method to reduce inference bias of neural metrics in out-of-distribution data .
Approach:	They propose to reduce inference bias by using uncertainty estimation, test-time adaptation, and inference to reduce model uncertainty.
Outcome:	The proposed method reduces model uncertainty and improves correlation performance across models.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models (2025.emnlp-main)

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Weihao Xuan, Qingcheng Zeng, Heli Qi, Junjue Wang, Naoto Yokoya

Multimodality

Challenge:	Existing approaches to quantify uncertainty are limited in vision-language models . however, current models display notable miscalibration across diverse tasks and settings .
Approach:	They evaluate verbalized confidence in vision-language models using visual reasoning . they propose a prompting strategy that improves confidence alignment in multimodal settings .
Outcome:	The proposed method improves confidence alignment across multimodal settings.

Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models (2026.acl-srw)

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Divyaksh Shukla, Ashutosh Modi

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)

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.

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Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu

Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation (2024.acl-long)

Challenge:	Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty.
Approach:	They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges.
Outcome:	The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods.

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Wen Wu, Bo Li, Chao Zhang, Chung-Cheng Chiu, Qiujia Li, Junwen Bai, Tara Sainath, Phil Woodland

ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation (2023.emnlp-main)

Challenge:	Experimental results show that incorporating utterances without majority-agreed labels into an additional class reduces the classification performance of the other emotion classes.
Approach:	They propose to combine utterances without majority-agreed labels into an additional class . they propose to quantify uncertainty in emotion classification using evidential deep learning .
Outcome:	The proposed method retains classification accuracy while effectively detects ambiguous emotion expressions.

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Xinpeng Wang, Barbara Plank

How Certain is Your Transformer? (2021.eacl-main)

Challenge:	Recent studies indicate that learning from individual annotations outperforms learning from aggregated labels, though they require a considerable amount of annotation.
Approach:	They propose to use a multi-head model to learn from disagreements in an active learning setting to identify annotators with a single head.
Outcome:	The proposed model outperforms a single-head model in terms of uncertainty estimation and prediction while saving 70% of the annotation budget.

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Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov

Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission (2024.findings-naacl)

Challenge:	Obtaining reliable uncertainty estimations for such neural networks (NNs) is challenging due to the huge number of parameters in these deep learning models.
Approach:	They propose to use Monte Carlo dropout to estimate uncertainty for Transformer-based models and construct inexpensive estimates using Determinantal Point Processes.
Outcome:	The proposed estimates improve the quality of detection of error-prone instances.

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Jianfeng He, Linlin Yu, Shuo Lei, Chang-Tien Lu, Feng Chen

Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training (2020.findings-emnlp)

Challenge:	Named entity recognition tasks are often suboptimal for NER . previous work focused on UE-NER, which estimates uncertainty scores for ner .
Approach:	They propose to use a Sequential Labeling Posterior Network to estimate uncertainty for NER . they propose to consider wrong-span cases and to evaluate the specificity of wrong-pan cases.
Outcome:	The proposed system improves on three datasets and AUPR on MIT-Restaurant datasets.

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Dongha Choi, Hyunju Lee

STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models (2024.findings-acl)

Challenge:	Several natural language processing methods have been used to extract interactions between chemicals and proteins from biomedical text data.
Approach:	They propose a method to extract chemical–protein interactions from biomedical text data . they use a pre-trained language-understanding model and calibration techniques to estimate uncertainty .
Outcome:	The proposed approach achieves state-of-the-art performance on the Biocreative VI ChemProt task while preserving higher calibration abilities.

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Linhai Zhang, Jialong Wu, Deyu Zhou, Guoqiang Xu

Benchmarking Uncertainty Metrics for LLM Target-Aware Search (2025.findings-emnlp)

Challenge:	Existing studies show that supervised training is still necessary for complex reasoning tasks.
Approach:	They propose a method to integrate uncertainty-based active learning and LoRA to effectively integrate the two methods.
Outcome:	The proposed approach outperforms baseline models on three reasoning tasks.

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Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin

Information Retrieval

Challenge:	Existing uncertainty metrics for LLM search methods do not capture the diverse types of uncertainty needed to guide different optimization goals.
Approach:	They propose a framework for uncertainty benchmarking that captures four different uncertainty types . the uncertainty types Answer, Correctness, Aleatoric, and Epistemic serve different optimization goals .
Outcome:	The proposed framework identifies four different uncertainty types . the uncertainty types serve different optimization goals in LLM search .

Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation (2023.findings-acl)

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Zhu Liu, Ying Liu

Word Sense Disambiguation

Challenge:	Existing supervised methods treat word sense disambiguation as a classification task but ignore uncertainty estimation (UE) in the real-world setting, the data is always noisy and out of distribution.
Approach:	They propose to use word sense disambiguation to determine an appropriate sense for a word given its context to determine the most appropriate sense.
Outcome:	The proposed model reflects data uncertainty satisfactorily but underestimates model uncertainty.

Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks (2025.findings-naacl)

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Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

Green & Sustainable NLP

Challenge:	Existing methods for uncertainty estimation are inadequate for safety-critical applications.
Approach:	They propose a method that uses the distances from neighbors and the ratio of labels in neighbors to estimate uncertainty.
Outcome:	The proposed method outperforms baseline and density-based methods in calibration and uncertainty metrics.

Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)

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Tianjun Wei, Huizhong Guo, Yingpeng Du, Zhu Sun, Huang Chen, Dongxia Wang, Jie Zhang

Green & Sustainable NLP

Challenge:	Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume.
Approach:	They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data.
Outcome:	The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs.

NeuroSym-Cal: Bridging the Reasoning-Execution Gap in Code Generation via Hierarchical Calibration (2026.findings-acl)

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Peiyang Liu, Yining Wang, Youru Li, Long Li, Zhi Cai, Wei Ye

Programming Languages in NLP

Challenge:	Existing calibration methods rely on the assumption that consensus implies correctness . Existing methods fail under systematic errors, leading to miscalibrated high-confidence predictions.
Approach:	They propose a hierarchical calibration framework that measures confidence at two levels . they propose sensitivity analysis to measure local curvature of deductive process .
Outcome:	The proposed framework de-saturates overconfident errors and improves selective generation performance on OOD benchmarks.

Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue (2022.findings-acl)

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Alexios Gidiotis, Grigorios Tsoumakas

Text Generation

Challenge:	Xu et al., 2019; Lewis e t al, 2019) show that Bayesian summarization methods can generate high quality summaries but suffer from a couple of issues when inputs lie far from the training data distribution.
Approach:	They propose to extend state-of-the-art summarization models with Monte Carlo dropout and perform multiple stochastic forward passes to approximate Bayesian inference.
Outcome:	The proposed method outperforms deterministic summarization models on multiple benchmark datasets.

Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection (2024.naacl-long)

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Jianfeng He, Hang Su, Jason Cai, Igor Shalyminov, Hwanjun Song, Saab Mansour

Active Learning for Abstractive Text Summarization (2022.findings-emnlp)

Challenge:	Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data.
Approach:	They propose a scoring approach that encapsulates three primary dimensions of summarization model quality.
Outcome:	The proposed method reduces reliance on human-labeled data and improves the performance of summarization models.

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Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov

Accounting for Sycophancy in Language Model Uncertainty Estimation (2025.findings-naacl)

Challenge:	Abstractive text summarization (ATS) requires a long document and short summaries.
Approach:	They propose a query strategy for AL in abstractive text summarization that uses uncertainty estimation to reduce model performance.
Outcome:	The proposed query strategy improves ROUGE and consistency scores for annotated datasets . it also increases the performance of the model, compared to passive annotation.

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Anthony Sicilia, Mert Inan, Malihe Alikhani

Semantic Text Processing

Challenge:	Effective human-machine collaboration requires machine learning models to externalize uncertainty.
Approach:	They propose a generalization of the definition of sycophancy bias and a new algorithm to account for scophancies in uncertainty estimation.
Outcome:	The proposed algorithm can account for sycophancy in uncertainty estimation process.

ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation (2022.emnlp-main)

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Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang

Robustness in NLP

Challenge:	Existing methods show poor performance under Far Boundary (FB) adversarial examples.
Approach:	They propose to use a new technique to detect adversarial examples based on data and model uncertainty to outperform existing methods.
Outcome:	The proposed method outperforms existing methods by 3.6 and 6.0 AUC points under each scenario.

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)

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Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Jun Huang, Ming Gao, Xiang Li

Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources (2026.findings-acl)

Challenge:	Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated.
Approach:	They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty.
Outcome:	The proposed framework improves performance and efficiency over multiple tasks over multiple datasets.

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Tiejin Chen, Xiaoou Liu, Longchao Da, Jia Chen, Evangelos E. Papalexakis, Hua Wei

Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)

Challenge:	Existing methods for uncertainty quantification fail to capture multifaceted nature of natural language generation.
Approach:	They propose a multi-resource Uncertainty Quantification framework that integrates heterogeneous uncertainty signals into a unified measure.
Outcome:	The proposed framework outperforms existing methods on CoQA, NQ_Open, and HotpotQA.

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Liyan Xu, Xuchao Zhang, Xujiang Zhao, Haifeng Chen, Feng Chen, Jinho D. Choi

Cross-Lingual Transfer

Challenge:	Recent pre-trained language models have achieved remarkable zero-shot performance . we propose a self-learning framework that utilizes unlabeled data of target languages .
Approach:	They propose a self-learning framework that utilizes unlabeled data of target languages to select silver labels for cross-lingual transfer tasks.
Outcome:	The proposed framework outperforms baseline models on two cross-lingual tasks by 10 F1 on average and 2.5 accuracy on natural language inference (NLI).

Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments (2022.findings-emnlp)

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Maor Ivgi, Yair Carmon, Jonathan Berant

Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks (2023.acl-long)

Challenge:	Neural scaling laws define a predictable relationship between a model’s parameter count and its performance after training in the form of a power law.
Approach:	They perform an empirical investigation of language understanding tasks and evaluate their results to determine whether scaling laws can be used to accelerate model development.
Outcome:	The proposed scaling laws can be exploited for debugging convergence when training large models, and can predict the performance of larger models.

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Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, Artem Shelmanov

Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing (2024.lrec-main)

Challenge:	Existing methods for text classification tasks are inherently ambiguous and can cause errors.
Approach:	They propose a method that combines epistemic and aleatoric uncertainty to estimate toxicity detection errors.
Outcome:	The proposed method outperforms existing methods for toxicity detection and other ambiguous text classification tasks.

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Zhenyu Qian, Yiming Qian, Yuting Song, Fei Gao, Hai Jin, Chen Yu, Xia Xie

Semantic Text Processing

Challenge:	Existing methods for graph processing rely on assumptions about data relations that are inadequate when handling large and complex graph data.
Approach:	They propose a large language model enhanced by an uncertainty-aware module to provide a confidence score on the generated graph data.
Outcome:	The proposed approach surpasses state-of-the-art algorithms by a substantial margin on ten datasets.

Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Models (2025.findings-emnlp)

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Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)

Challenge:	Decoding strategies affect the probability distribution underlying the output of a language model and can therefore affect both generation quality and uncertainty.
Approach:	They investigate the impact of decoding strategies on uncertainty estimation in large language models .
Outcome:	The proposed methods improve the uncertainty estimation of large language models by reducing repetition.

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Jianfeng He, Runing Yang, Linlin Yu, Changbin Li, Ruoxi Jia, Feng Chen, Ming Jin, Chang-Tien Lu

Text Generation

Challenge:	Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods.
Approach:	They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets.
Outcome:	The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable.

Trick or Neat: Adversarial Ambiguity and Language Model Evaluation (2025.findings-acl)

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Antonia Karamolegkou, Oliver Eberle, Phillip Rust, Carina Kauf, Anders Søgaard

Adaptive Platt Scaling with Causal Interpretations for Self-Reflective Language Model Uncertainty Estimates (2025.findings-emnlp)

Challenge:	Direct prompting fails to detect ambiguity while linear probes can decode ambiguities with high accuracy, sometimes exceeding 90%.
Approach:	They introduce an adversarial ambiguity dataset that includes syntactic, lexical, and phonological ambiguities along with adversarials.
Outcome:	The proposed dataset includes syntactic, lexical, and phonological ambiguities along with adversarial variations.

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Anthony Sicilia, Malihe Alikhani

Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (2024.emnlp-main)

Challenge:	a new study examines the ability of large language models to self-monitor and ask for human intervention.
Approach:	They propose a formal analysis of LLM self-reflection for uncertainty estimation using domain adaptation theory.
Outcome:	The proposed method improves accuracy and human interpretation on reasoning tasks.

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Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

A Survey of Uncertainty Estimation Methods on Large Language Models (2025.findings-acl)

Challenge:	Named Entity Recognition (NER) is a key task in NLP to find mentions of named entities and classify them into predefined categories.
Approach:	They investigated the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks.
Outcome:	The data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting.

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Zhiqiu Xia, Jinxuan Xu, Yuqian Zhang, Hang Liu

The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities but could produce biased, hallucinated, or non-factual responses.
Approach:	They propose to conduct extensive experimental evaluations of LLM uncertainty estimation methods . large language models have demonstrated remarkable capabilities across tasks .
Outcome:	The proposed method could produce biased, hallucinated, or non-factual responses . a lack of comprehensive surveys on LLM uncertainty estimation is a problem .

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Xinyi Liu, Weiguang Wang, Hangfeng He

Towards Harmonized Uncertainty Estimation for Large Language Models (2025.acl-long)

Challenge:	Quantifying epistemic uncertainty in open-ended tasks is challenging due to the presence of aleatoric uncertainty, which arises from multiple valid answers.
Approach:	They conduct experiments on visual question answering tasks and find that mitigating prompt-introduced bias improves uncertainty quantification.
Outcome:	The proposed approach reduces uncertainty quantification in visual question answering tasks by mitigating prompt-introduced biases.

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Rui Li, Jing Long, Muge Qi, Heming Xia, Lei Sha, Peiyi Wang, Zhifang Sui

CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated exceptional capabilities in handling a wide range of downstream tasks.
Approach:	They propose a method that employs a lightweight model trained on data aligned with the target LLM’s performance to adjust uncertainty scores.
Outcome:	The proposed method achieves improvements of up to 60% over existing methods.

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Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis

Structured Data in NLP

Challenge:	In-context learning (ICL) uses few-shot labeled examples to perform selective annotation.
Approach:	They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples .
Outcome:	The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL.

DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025.findings-emnlp)

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Luke Yoffe, Alfonso Amayuelas, William Yang Wang

Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems (2025.findings-emnlp)

Challenge:	Multi-agent debates can improve the accuracy of Large Language Models by having multiple agents discuss solutions over several rounds of debate.
Approach:	a debate framework that uses uncertainty metrics to assess agent confidence is proposed . the framework uses textual prompts or a modified attention mechanism that adjusts token weights .
Outcome:	The proposed framework assesses agent confidence using uncertainty metrics . the framework is available at https://github.com/lukeyoffe/debunc.

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Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Souradip Chakraborty, Amrit Singh Bedi, Furong Huang

Reasoning

Challenge:	Existing approaches to selecting reliable responses from multiple LLMs often depend on external verifiers, human evaluators, or self-consistency techniques.
Approach:	They propose a calibrated log-likelihood-based selection framework to improve multi-LLM performance.
Outcome:	The proposed method outperforms majority voting and exceeds self-consistency performance when using a large number of model calls.

DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models (2026.acl-long)

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Yixin Bu, Runze Xia, Guanyun Zou, Yupeng Ji, Hongliang Dai, Haodong Liu, Piji Li

SUE: Sparsity-based Uncertainty Estimation via Sparse Dictionary Learning (2025.emnlp-main)

Challenge:	Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs).
Approach:	They propose a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions to capture model's internal fragility.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both uncertainty estimation and calibration while exhibiting superior cross-dataset generalization.

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Tamás Ficsor, Gábor Berend

Sentiment Analysis

Challenge:	Existing methods to estimate uncertainty use predictive confidence, structural characteristics of representation space, or stochastic variation in model outputs.
Approach:	They propose a new uncertainty estimation framework based on sparse dictionary learning by identifying dictionary atoms associated with misclassified samples.
Outcome:	The proposed framework outperforms or matches existing methods on several NLU benchmarks and sentiment analysis benchmarks.

Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs (2026.findings-acl)

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David Hartmann, Lena Pohlmann, Lelia Hanslik, Noah Gießing, Bettina Berendt, Pieter Delobelle