Measuring and Improving Consistency in Pretrained Language Models

Scikit-learn: Machine Learning in Python

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

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Language Models are Few-Shot Learners

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929

Transformers: State-of-the-Art Natural Language Processing

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741

Intriguing properties of neural networks

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SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208

VQA: Visual Question Answering

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Metamorphic Testing of Large Language Models for Natural Language Processing

Cho, S. B., Ruberto, S., & Terragni, V. (2025) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme64153.2025.00025 · Cited by 22

The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation

Belay, T. D., Tonja, A. L., Kolesnikova, O., Yimam, S. M., Ayele, A. A., Haile, S. B., Sidorov, G., & Gelbukh, A. F. (2022) · International Conference on Information and Communication Technology for Development for Africa (ICT4DA) · doi:10.1109/ict4da56482.2022.9971385 · Cited by 9

Thus Spake ChatGPT

Dutta, S., & Chakraborty, T. (2023) · Communications of the ACM · doi:10.1145/3616863 · Cited by 12

Accurate, yet inconsistent? Consistency Analysis on Language Understanding ModelsPreprint

Jang, M., Kwon, D. S., & Lukasiewicz, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06665 · Cited by 14

Understanding the Impact of Using Generative AI Tools in a Database Course

Osorio, V. R., Bernuy, A. Z., Simion, B., & Liut, M. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701785 · Cited by 8

Automated Validating and Fixing of Text-to-SQL Translation with Execution Consistency

Yang, Y., Wang, Z., Xia, Y., Wei, Z., Ding, H., Piskac, R., Chen, H., & Li, J. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725271 · Cited by 8

Learning with Instance Bundles for Reading Comprehension

Dua, D., Dasigi, P., Singh, S., & Gardner, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.584 · Cited by 9

Synonyms, Antonyms and Factual Knowledge in BERT Heads

Serina, L., Putelli, L., Gerevini, A., & Serina, I. (2023) · Future Internet · doi:10.3390/fi15070230 · Cited by 9

Self-Consistent Narrative Prompts on Abductive Natural Language Inference

Chan, C., Liu, X., Chan, T. H., Cheng, J., Song, Y., Wong, G. Y., & See, S. C. W. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.67 · Cited by 15

BERTnesia: Investigating the capture and forgetting of knowledge in BERTPreprint

Wallat, J., Singh, J., & Anand, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09313 · Cited by 18

S2AF : An action framework to self-check the Understanding Self-Consistency of Large Language Models

Shao, H., Wang, F., & Xie, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107365 · Cited by 7

Energy-Aware Multilingual Evaluation of Large Language Models

de Zarzà, I., Liz, M., de Curtò, J., & Calafate, C. T. (2026) · Electronics · doi:10.3390/electronics15071395 · Cited by 7

Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings

Wang, Y., & Zhang, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3337643 · Cited by 9

Translating Natural Language Queries to SQL Using the T5 Model

Wong, A., Pham, L., Lee, Y., Chan, S. T., Sadaya, R., Khmelevsky, Y., Clement, M., Cheng, F. W. Y., Mahony, J., & Ferri, M. (2024) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon61195.2024.10553509 · Cited by 8

Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answering

Chaturvedi, A., Bhar, S., Saha, S., Garain, U., & Asher, N. M. (2023) · Computational Linguistics · doi:10.1162/coli_a_00493 · Cited by 8

Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views

Margatina, K., Wang, S., Vyas, Y., John, N. A., Benajiba, Y., & Ballesteros, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.211 · Cited by 9

The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models

Hagström, L., Saynova, D., Norlund, T., Johansson, M., & Johansson, R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.332 · Cited by 6

Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions

Chen, M., Iyer, A., & Pavel, A. (2025) · Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3663547.3746393 · Cited by 6

ICR: A framework for resolving knowledge conflicts in retrieval-augmented generation

Xiong, Y., Chen, Y., & Zhang, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132139 · Cited by 6

Metamorphic Testing for Semantic Invariance in Large Language Models

de Curtò, J., & de Zarzà, I. (2025) · IEEE Access · doi:10.1109/access.2025.3646270 · Cited by 11

One mind, many tongues: A deep dive into language-agnostic knowledge neurons in large language models

Cao, P., Chen, Y., Jin, Z., Chen, Y., Liu, K., & Zhao, J. (2026) · Artificial Intelligence · doi:10.1016/j.artint.2026.104490 · Cited by 16

Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains

Queloz, M. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00864-x · Cited by 7

Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence

Jang, M., Mtumbuka, F., & Lukasiewicz, T. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.156 · Cited by 6

A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia

Monea, G., Peyrard, M., Josifoski, M., Chaudhary, V., Eisner, J. M., Kıcıman, E., Palangi, H., Patra, B., & West, R. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.369 · Cited by 19

Political Biases of ChatGPT in Different LanguagesPreprint

Retzlaff, N. (2024) · Preprints.org · doi:10.20944/preprints202406.1224.v1 · Cited by 8

Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality

Izquierdo-Domenech, J., Linares-Pellicer, J., & Ferri-Molla, I. (2023) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2023.09.002 · Cited by 8

Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision

Su, J., Yu, P., Zhang, J., & Bach, S. H. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386190 · Cited by 5

How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?

Srikanth, N. P., Carpuat, M. J., & Rudinger, R. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00692 · Cited by 5

Visually Grounded Commonsense Knowledge Acquisition

Yao, Y., Yu, T., Zhang, A., Li, M., Xie, R., Weber, C., Liu, Z., Zheng, H.-T., Wermter, S., Chua, T., et al. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25809 · Cited by 5

Evaluating the Knowledge Base Completion Potential of GPT

Veseli, B., Razniewski, S., Kalo, J.-C., & Weikum, G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.426 · Cited by 9

TempLM: Distilling Language Models into Template-Based Generators

Zhang, T., Lee, M., Li, X. L., Shen, E., & Hashimoto, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.124 · Cited by 6

Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

(2023) · doi:10.18653/v1/2023.c3nlp-1 · Cited by 5

SQUIRE: Interactive UI Authoring via Slot QUery Intermediate REpresentations

Leung, A., Cheng, R., Wu, J., Nichols, J., & Barik, T. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747672 · Cited by 10

Leveraging generative AI to transform statistical analysis plan authoring in clinical trials

Landman, R., Emir, B., Zhang, R., Manousaridis, G., Lee, Y. C., Jiang, L., Siu, G., Dai, F., Bell, G., Thiry, A., et al. (2026) · Clinical Trials · doi:10.1177/17407745261422365 · Cited by 4

Memory Injections: Correcting Multi-Hop Reasoning Failures During Inference in Transformer-Based Language Models

Sakarvadia, M., Ajith, A., Khan, A., Grzenda, D., Hudson, N., Bauer, A., Chard, K., & Foster, I. T. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.26 · Cited by 9

A comprehensive evaluation of semantic relation knowledge of pretrained language models and humans

Cao, Z., Yamada, H. S., Teufel, S., & Tokunaga, T. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09858-9 · Cited by 4

KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations

Jang, M., Majumder, B. P., McAuley, J., Lukasiewicz, T., & Camburu, O.-M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.47 · Cited by 4

Multi-Axial Analysis of Clinical Reasoning in Large Language Models: Inter-Verifier Disagreement and Its Implications for Automated Evaluation

Byun, H., Lee, D., Jung, M., & Jang, B. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02440-y · Cited by 3

Can BERT eat RuCoLA? Topological Data Analysis to Explain

Proskurina, I., Artemova, E., & Piontkovskaya, I. (2023) · Workshop on Slavic Natural Language Processing (SlavicNLP) · doi:10.18653/v1/2023.bsnlp-1.15 · Cited by 3

Consistent Text Categorization using Data Augmentation in e-Commerce

Avigdor-Elgrabli, N., Horowitz, G., Raviv, A., & Daye, S. Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.30 · Cited by 3

Seeking Clozure: Robust Hypernym extraction from BERT with Anchored Prompts

Liu, C., Cohn, T., & Frermann, L. (2023) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2023.starsem-1.18 · Cited by 3

Knowledge Base Completion for Long-Tail Entities

Chen, L., Razniewski, S., & Weikum, G. (2023) · doi:10.18653/v1/2023.matching-1.8 · Cited by 6

Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases

Mitra, M., de Vos, M. G., Cortinovis, N., & Ometto, D. (2024) · IEEE International Conference on eScience · doi:10.1109/e-science62913.2024.10678704 · Cited by 4

Knowledge Acquired by Foundation Models

Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_4 · Cited by 3

Measuring Consistency in Text-based Financial Forecasting Models

Yang, L., Ma, Y., & Zhang, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.769 · Cited by 5

Self-Training Meets Consistency: Improving LLMs’ Reasoning with Consistency-Driven Rationale Evaluation

Lee, J., Sakaguchi, K., & Bak, J. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.528 · Cited by 2

Enhancing the RAG Pipeline Through Advanced Optimization Techniques

Ilyas, Q. M., & Aziz, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6255-6.ch003 · Cited by 2

Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence

Queloz, M. (2025) · Minds and Machines · doi:10.1007/s11023-025-09738-9 · Cited by 3

Toward a Robust Detection of PowerShell Malware against Code Mixing and Obfuscation by Using Sentence Transformer and Similarity Learning

Fu, Z., Song, L., Ding, S. H. H., Alaca, F., & Acharya, S. (2025) · ACM Transactions on Privacy and Security · doi:10.1145/3771542 · Cited by 2

VeriFastScore: Speeding up long-form factuality evaluation

Rajendhran, R., Zadeh, A., Sarte, M., Li, C., & Iyyer, M. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.491 · Cited by 7

MoCS: Modular configuration synthesis via large language models and graph neural network-augmented recommendation

Dai, Y., Zhang, H., Wang, J., & Liao, J. (2026) · Computer Communications · doi:10.1016/j.comcom.2026.108428 · Cited by 2

S2CR: A self-supervised self-consistency reasoning framework coupled to retrieval-augmented generation

Shao, H., Wang, F., Zhang, S., & Xie, Z. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104701 · Cited by 2

Yapay Zekâ, Dilin Doğuştanlığını Yıkabilir mi? Büyük Dil Modelleri Karşısında Uyaran Eksikliği Argümanı

Kerimoğlu, C. (2026) · Dil Araştırmaları · doi:10.54316/dilarastirmalari.1879242 · Cited by 2

Would a large language model pay extra for a view? Inferring willingness to pay from subjective choices

Reusens, M., Goethals, S., Calders, T., & Martens, D. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133279 · Cited by 2

Explainable AI for Deep Visual Recognition: Evaluation, Methods, and Open Challenges

Alharbi, K. N. (2026) · Electronics · doi:10.3390/electronics15143222 · Cited by 2

Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain

Eichel, A., Schlipf, H., & Walde, S. S. I. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.104 · Cited by 3

Who Are All The Stochastic Parrots Imitating? They Should Tell Us!

Shaier, S., Hunter, L., & Kann, K. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-short.13 · Cited by 2

Continuous Entailment Patterns for Lexical Inference in Context

Schmitt, M., & Schütze, H. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.556 · Cited by 2

Judgment aggregation, discursive dilemma and reflective equilibrium: Neural language models as self-improving doxastic agents

Betz, G., & Richardson, K. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.900943 · Cited by 2

Learning to Improve Out-of-Distribution Generalization via Self-Adaptive Language Masking

Jiang, S., Pan, Y., Chen, Q., Xiang, Y., & Wu, X. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3394774 · Cited by 2

Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency

Shen, L., Tan, W., Zheng, B., & Khashabi, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.523 · Cited by 3

Do as We Do, Not as You Think: the Conformity of Large Language Models

Weng, Z., Chen, G., & Wang, W. (2025) · arXiv · doi:10.48550/arxiv.2501.13381 · Cited by 1

ConvoMap: Interactive Visualizations for Exploring Complex Conversations in Multi-Agent Systems

Zhang, G., Bursztyn, V. S., Chan, G. Y.-Y., Guo, S., Koh, E., Oney, S., & Hoffswell, J. (2025) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl-hcc65237.2025.00043 · Cited by 2

Generative AI as a Strategic ESG Consultant: A Comparative Analysis of Priorities in E‐Commerce Circular Logistics

Wasilewski, A., & Kołaczek, G. (2026) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.70562 · Cited by 1

On the Underspecification of Situations in Open-domain Conversational Datasets

Otani, N., Araki, J., Kim, H., & Hovy, E. H. (2023) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2023.nlp4convai-1.2 · Cited by 2

Unveiling the Evolution and Impact of Pretrained Language Models in Natural Language Processing

Bian, H. (2024) · International Conference on Computer, Artificial Intelligence and Control Engineering (CAICE) · doi:10.1145/3672758.3672854 · Cited by 1

K-Bloom: unleashing the power of pre-trained language models in extracting knowledge graph with predefined relations

Vo, T., Luu, S. T., & Nguyen, L.-M. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02345-1 · Cited by 1

Robots That Perform Norm-Based Reference Resolution

Abrams, M., Thierauf, C., & Scheutz, M. J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-3519-1_11 · Cited by 1

Exploring Conversational Adaptability: Assessing the Proficiency of Large Language Models in Dynamic Alignment with Updated User Intent

Chen, Y.-C., & Huang, H. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i22.34534 · Cited by 1

AN APPROACH TO ASSESSING THE QUALITY OF PENTEST SCENARIO GENERATION USING LARGE LANGUAGE MODELS

Konev, A., & Игоревна, П. Т. (2025) · Voprosy kiberbezopasnosti · doi:10.21681/2311-3456-2025-6-147-157 · Cited by 1

Applications of multi-agent systems in computational materials science

Ying, W., Zhong-Yi, L., & Zefeng, G. A. O. (2026) · Acta Physica Sinica · doi:10.7498/aps.75.20251687 · Cited by 1

A multi-scenario approach to continuously learn and understand norm violations

dos Santos, T. F., Osman, N., & Schorlemmer, M. (2023) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-023-09619-4 · Cited by 1

Improved Convolutional Neural Network based Approach for Analyzing the ChatGPT and Different Fields Impact

Nayak, I., Kanagaraj, K., Shrimali, M., Kumar, D., Ghamande, M. V., & Kumar, C. S. (2023) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs58579.2023.10404259 · Cited by 1

On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation

Patel, G., Choshen, L., & Abend, O. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.14 · Cited by 1

Unnatural language processing: How do language models handle machine-generated prompts?

Kervadec, C., Franzon, F., & Baroni, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.959 · Cited by 1

Does the Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models?

Cao, B., Tang, Q., Lin, H., Han, X., & Sun, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.143 · Cited by 2

Continuous Entailment Patterns for Lexical Inference in ContextPreprint

Schmitt, M., & Schütze, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03695 · Cited by 2

AI Behavioral SciencePreprint

Jackson, M. O., Mei, Q., Wang, S., Xie, Y., Yuan, W., Benzell, S. G., Brynjolfsson, E., Evans, J. A., Jabarian, B., Kleinberg, J. M., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5395006 · Cited by 2

Interviewer-Candidate Role Play: Towards Developing Real-World NLP SystemsPreprint

Varshney, N., Mishra, S. R., & Baral, C. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00315

Accelerating Multi-Exit BERT Inference via Curriculum Learning and Knowledge Distillation

Gu, S., Luo, X., Wang, X., & Guo, Y. (2023) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194023500018

Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023)

(2023) · doi:10.18653/v1/2023.matching-1

Probing Power by Prompting: Harnessing Pre-trained Language Models for Power Connotation Framing

Khanehzar, S., Cohn, T., Mikołajczak, G., & Frermann, L. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.61

How Much Consistency Is Your Accuracy Worth?

Johnson, J. K., & Marasović, A. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.19

Harnessing Black-Box Control to Boost Commonsense in LM’s Generation

Tian, Y., Zhang, F., & Peng, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.329 · Cited by 1

When RAG and LLM Conflict: Who Will AI Listen to?

Fang, Q. (2024) · doi:10.59350/7gfzv-88m36

iKnowiSee: AR Glasses with Language Learning Translation System and Identity Recognition System Built Based on Large Pre-trained Models of Language and Vision and Internet of Things Technology

Liang, Q., Chen, Y., Li, W., Lai, M., Ni, W., & Qiu, H. X. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-3948-6_2

Reasoning in Transformers – Mitigating Spurious Correlations and Reasoning Shortcuts

Enström, D., Kjellberg, V., & Johansson, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71170-1_18

KLoB: a Benchmark for Assessing Knowledge Localization Methods in Language Models

Ju, Y., Ma, H., Xing, X., & Zeng, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0119-6_36

Predicting ChatGPT’s Ability to Solve Complex Programming Challenges

Ho, N., May, J., Ngo, B., Formato, J., Ngo, L., Ho, V. L., & Bui, H. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825139

A Conceptual Framework for Efficient and Sustainable Pruning Techniques in Deep Learning Models

Smarts, N., Rajalakshmi, S., & Kuthadi, V. M. (2025) · Journal of Computer Science · doi:10.3844/jcssp.2025.1113.1128

Digital Banking Transformation: LLMs in Customer Service and Fraud Detection

Manoj, R. J., Boobesh, K., Rajan, A. P., & Saravanan, T. P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78940-3_20

Assessing the Reliability of Large Language Models in Generating Synthetic and Representative Datasets for International Study Program Recommendations

Zonjić, R., Alfirević, L., Bašić, K., Džanija, I., & Vranić, M. (2025) · International Colloquium on Smart Grid Metrology (SMAGRIMET) · doi:10.1109/smagrimet66590.2025.11030048

Probing Fine-Grained Hierarchical Concept Comprehension and Generation in Large Language Models

Sun, K., Bai, Y., Tu, S., Li, J., & Hou, L. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3592339

Initializing and Retrofitting Key-Value Adaptors for Traceable Model Editing

Zhu, H., Lan, Y., Li, X., & Qian, W. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.152

Generative AI for Mental Health Education: A Comparison of ChatGPT and DeepSeek

Mu, Y., & Li, Q. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-0880-8_34

An Investigation of Comparative Correlative Constructions in Auto-regressive Large Language Models: From Construction Grammar to Computational UnderstandingPreprint

Li, J., & Liu, Y. (2025) · Research Square · doi:10.21203/rs.3.rs-6702743/v1 · Cited by 2

Applied with Caution: Extreme-Scenario Testing Reveals Significant Risks in Using LLMs for Humanities and Social Sciences Paper Evaluation

Liu, H., Dai, L., & Jiang, H. (2025) · Applied Sciences · doi:10.3390/app151910696

EXAMINING THE ROLE OF TEXT ANALYSIS IN SOYBEAN OIL FUTURES PRICE PREDICTION

Gao, Q., & Yu, H. (2025) · Financial and credit activity problems of theory and practice · doi:10.55643/fcaptp.5.64.2025.4823

Three Tiers of Computation in Transformers and in Brain Architectures

Graham, E., & Granger, R. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3844612

Research and Practice on the Construction of an Agent Paradigm for Vocational Education Based on RAG Technology

Gao, H., Chen, Z., Wang, J., & Peng, T. (2025) · International Symposium on Artificial Intelligence for Education (ISAIE) · doi:10.1145/3775073.3775234

Accurate, yet inconsistent? Consistency Analysis on Language Understanding ModelsPreprint

Jang, M., Kwon, D. S., & Lukasiewicz, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06665 · Cited by 14

Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary

Jang, M., & Lukasiewicz, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.527 · Cited by 4

Consistency and completeness of knowledge acquired by language models

Kassner, N. (2025) · Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München) · doi:10.5282/edoc.36004

How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?

Srikanth, N. P., Carpuat, M. J., & Rudinger, R. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00692 · Cited by 5

Measuring Reliability of Large Language Models through Semantic ConsistencyPreprint

Raj, H., Rosati, D., & Majumdar, S. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.05853 · Cited by 36