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Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

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SemEval-2016 Task 4: Sentiment Analysis in Twitter

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Annotation Artifacts in Natural Language Inference Data

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Generating Natural Language Adversarial Examples

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SemEval-2017 Task 4: Sentiment Analysis in Twitter

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HellaSwag: Can a Machine Really Finish Your Sentence?

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The Risk of Racial Bias in Hate Speech Detection

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SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

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Hypothesis Only Baselines in Natural Language Inference

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Adversarial NLI: A New Benchmark for Natural Language Understanding

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Universal Adversarial Triggers for Attacking and Analyzing NLP

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Adversarial Attacks on Deep-learning Models in Natural Language Processing

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Gender Bias in Coreference Resolution

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Stress Test Evaluation for Natural Language InferencePreprint

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Truth inference in crowdsourcing: is the problem solved?

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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

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Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

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Breaking NLI Systems with Sentences that Require Simple Lexical Inferences

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How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks

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Inherent Disagreements in Human Textual Inferences

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Towards Debiasing Fact Verification Models

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Is BERT Really Robust? Natural Language Attack on Text Classification and EntailmentPreprint

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How Can We Accelerate Progress Towards Human-like Linguistic Generalization?

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Understanding and Mitigating Worker Biases in the Crowdsourced Collection of Subjective Judgments

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Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension

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On our best behaviour

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Beat the ai: Investigating adversarial human annotation for reading comprehension

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Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and EntailmentPreprint

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Social Bias in Elicited Natural Language Inferences

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Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets

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Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference

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Analyzing Compositionality-Sensitivity of NLI Models

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Using Worker Self-Assessments for Competence-Based Pre-Selection in Crowdsourcing Microtasks

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Towards Ecologically Valid Research on Language User InterfacesPreprint

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Adversarial Attack on Sentiment Classification

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Dynabench: Rethinking Benchmarking in NLP

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

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Interactive Model Cards: A Human-Centered Approach to Model Documentation

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Generative language models exhibit social identity biases

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Mind the Gap: Assessing Temporal Generalization in Neural Language Models

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Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

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Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

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SituatedQA: Incorporating Extra-Linguistic Contexts into QA

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Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate

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Adaptive Testing and Debugging of NLP Models

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Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks

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An analysis of emotions and the prominence of positivity in #BlackLivesMatter tweets

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Pitfalls of Static Language Modelling.Preprint

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Human-Adversarial Visual Question AnsweringPreprint

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Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)

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Introducing MBIB - The First Media Bias Identification Benchmark Task and Dataset Collection

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On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study

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Everyone’s Voice Matters: Quantifying Annotation Disagreement Using Demographic Information

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A Close Look into the Calibration of Pre-trained Language Models

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How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?

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Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants

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Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation BenchmarkingPreprint

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SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

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What’s the Meaning of Superhuman Performance in Today’s NLU?

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Analyzing Dynamic Adversarial Training Data in the Limit

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Being Right for Whose Right Reasons?

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A hybrid framework for bot detection on twitter: Fusing digital DNA with BERT

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A comprehensive survey on sentiment analysis: Framework, techniques, and applications

Chandan, M. K., & Mandal, S. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100777 · Cited by 9 · see citing works

Clean or Annotate: How to Spend a Limited Data Collection Budget

Chen, D., & Bowman, Z. Y. A. S. R. (2022) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/2022.deeplo-1.17 · Cited by 9 · see citing works

Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

Mozes, M., Bartolo, M., Stenetorp, P., Kleinberg, B., & Griffin, L. D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.651 · Cited by 9 · see citing works

A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist

Bhatt, S., Jain, R., Dandapat, S., & Sitaram, S. (2021) · Cited by 8 · see citing works

ShellGPT: Generative Pre-trained Transformer Model for Shell Language Understanding

Shi, J., Jiang, S., Xu, B., Liang, J., Xiao, Y., & Wang, W. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00082 · Cited by 8 · see citing works

Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation

Scaboro, S., Portelli, B., Chersoni, E., Santus, E., & Serra, G. (2022) · Experimental Biology and Medicine · doi:10.1177/15353702221128577 · Cited by 6 · see citing works

Benchmarking Long-tail Generalization with Likelihood Splits

Godbole, A., & Jia, R. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.71 · Cited by 6 · see citing works

Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking

Tamari, R., Richardson, K., Kahlon, N., Sar-Shalom, A., Liu, N. F., Tsarfaty, R., & Shahaf, D. (2022) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2022.starsem-1.9 · Cited by 5 · see citing works

Rather a Nurse than a Physician - Contrastive Explanations under Investigation

Eberle, O., Chalkidis, I., Piqueras, L. C., & Brandl, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.427 · Cited by 4 · see citing works

Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

(2022) · doi:10.18653/v1/2022.deeplo-1 · Cited by 4 · see citing works

RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training

Kim, J., Mao, Y., Hou, R., Yu, H., Liang, D., Fung, P., Wang, Q., Feng, F., Huang, L., & Khabsa, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.223 · Cited by 3 · see citing works

Multimodal approach to public health interventions using EGG and mobile health technologies

Zhang, X., Liu, H., Sun, M., & Feng, S. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1520343 · Cited by 3 · see citing works

longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.

Kovatchev, V., Chatterjee, T., Govindarajan, V. S., Chen, J., Choi, E., Chronis, G., Das, A., Erk, K., Lease, M., Li, J. J., et al. (2022) · doi:10.18653/v1/2022.dadc-1.5 · Cited by 3 · see citing works

Knowledge Transfer Between Tasks and Languages in the Multi-task Encoder-agnostic Transformer-based Models

MIPT, Karpov, D., & Konovalov, V. (2023) · Computational Linguistics and Intellectual Technologies · doi:10.28995/2075-7182-2023-22-200-214 · Cited by 3 · see citing works

How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs?Preprint

Sen, I., Samory, M., Floeck, F., Wagner, C., & Augenstein, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07022 · Cited by 3 · see citing works

Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity ResolutionPreprint

Yin, W., Heinecke, S., Li, J., Keskar, N. S., Jones, M., Shi, S., Georgiev, S., Milich, K., Esposito, J., & Xiong, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10497 · Cited by 3 · see citing works

Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive LearningPreprint

Mohanty, I., Goyal, A., & Dotterweich, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01054 · Cited by 3 · see citing works

Neural Network-Based Sentiment Classification of Russian Sentences into Four Classes

Kosterin, M. A., & Paramonov, I. V. (2022) · Modeling and Analysis of Information Systems · doi:10.18255/1818-1015-2022-2-116-133 · Cited by 2 · see citing works

Neural Network Sentiment Classification of Russian Sentences into Four Classes

Kosterin, M. A., & Paramonov, I. V. (2023) · Automatic Control and Computer Sciences · doi:10.3103/s0146411623070052 · Cited by 2 · see citing works

tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework

Sileo, D. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2h4pkr685mkq · Cited by 2 · see citing works

Answering Clean Tech Questions with Large Language ModelsPreprint

Stagnol, L., Cherief, A., Farah, Z., Guenedal, T. L., Sakout, S., & Sekine, T. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4663447 · Cited by 2 · see citing works

A Large Language Model-Assisted Organizational Decision Support System Integrating Organizational Data with Management Frameworks

Tselepatiotis, M., Kariofyllas, C., Kontogianni, A., & Alepis, E. (2026) · Information · doi:10.3390/info17100971 · Cited by 1 · see citing works

Unimodal Intermediate Training for Multimodal Meme Sentiment Classification

Hazman, M., McKeever, S., & Griffith, J. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_055 · Cited by 1 · see citing works

BRoverbs - Measuring how much LLMs understand Portuguese proverbs

Almeida, T. S., Bonás, G. K., & Santos, J. G. A. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5797 · Cited by 1 · see citing works

Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory

Rodríguez, P., Htut, P. M., Lalor, J., & Sedoc, J. (2022) · Workshop on Insights from Negative Results in NLP (Insights) · doi:10.18653/v1/2022.insights-1.14 · Cited by 1 · see citing works

Learning with Noisy Labels by Targeted Relabeling.Preprint

Chen, D., Yu, Z., & Bowman, S. R. (2021) · arXiv (Cornell University) · Cited by 1 · see citing works

Proceedings of the First Workshop on Dynamic Adversarial Data Collection

(2022) · doi:10.18653/v1/2022.dadc-1 · Cited by 1 · see citing works

A Large Language Model Framework for Causal Reasoning and Performance Prediction in Multimodal Time-Series DataPreprint

Bian, Z., & Mou, L. (2025) · Preprints.org · doi:10.20944/preprints202511.0001.v1 · Cited by 1 · see citing works

Adaptive Contextual-Relational Network for Fine-Grained Gastrointestinal Disease ClassificationPreprint

Du, M., & Zeng, Y. (2025) · Preprints.org · doi:10.20944/preprints202512.0679.v1 · Cited by 1 · see citing works

Overconfidence in the Face of Ambiguity with Adversarial Data

Li, M., & Michael, J. (2022) · doi:10.18653/v1/2022.dadc-1.4

Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness

Madine, M. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-srw.39

Data-Driven Understanding of Dynamic Structural Restructuring in Perovskite Fluorides for Efficient Oxygen Evolution CatalysisPreprint

Qi, R., & Nian, T. (2025) · Preprints.org · doi:10.20944/preprints202510.2348.v1

Enhancing fairness in decision-making of natural language understanding systems: An intersectional bias debiasing model via information theory-based disentanglement

Li, Y., Hu, J., Song, R., & Hu, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130940

Entity-Aware Cross-Modal Fusion Network for Fine-Grained Entity Consistency Verification in Multimodal News Misinformation DetectionPreprint

Harris, M., Shaw, H., & Young, R. (2026) · Preprints.org · doi:10.20944/preprints202601.0691.v1

Sentiment Dynamics of Graduating Students-to-Employees Transitions: A Paired Sample LLM-Based Analysis of Expectations and Real-World Experiences

Mullick, M. A. O., Ahmed, R., Islam, M. M., Islam, M. N., Azim, A., Alkhamees, B. F., & Hassan, M. M. (2026) · IEEE Access · doi:10.1109/access.2026.3660365

Characterization of a Reliability Domain for Image Classifiers

Coz, A. L. (2024) · theses.fr (ABES) · doi:10.70675/b6f39077z9dc3z4edcz9e4cz073207f40c00

MSRA-CCL: Multi-Source Reliability-Aware Aggregation and Cross-Domain Consistency Learning for Weakly Supervised Sentiment Classification

Wang, J., Rong, B., Li, C., Xu, H., & Zhu, X. (2026) · Electronics · doi:10.3390/electronics15184230

Dynamic Context-Aware Sentiment Analysis for Evolving Social Media Conversations on Twitter

Naraveni, G. (2024) · ScholarSphere (Penn State Libraries) · doi:10.26207/5mf8-hx85

Dynamic Sentiment Analysis: A Low-Latency System for Social Media Monitoring

Agrahari, S., Das, A., Bhagat, K., Shah, V., Sharma, N., & Ramasamy, G. (2025) · International Conference on Research and Development in Information, Communication, and Computing Technologies · doi:10.5220/0013940500004919

Sentiment Classification for Dynamic Opinions using Natural Language Processing

Jayanag, B., & Rao, K. V. (2013)

RealSent Realtime Sentiment Analysis

Farooq, I. (2020) · Journal of Emerging Technologies and Innovative Research

The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis

Venkit, P. N., Srinath, M., Gautam, S., Venkatraman, S., Gupta, V. K., Passonneau, R. J., & Wilson, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.848 · Cited by 20 · see citing works