Designing and Interpreting Probes with Control Tasks

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

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

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071

Untitled work

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Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

Building a Large Annotated Corpus of English: The Penn Treebank

Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405

Understanding deep learning requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838

Generating Typed Dependency Parses from Phrase Structure Parses

de Marneffe, M.-C., MacCartney, B., & Manning, C. D. (2006) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3wtkaidk6ea8 · Cited by 2,289

Visual Probing: Cognitive Framework for Explaining Self-Supervised Image Representations

Oleszkiewicz, W., Basaj, D., Sieradzki, I., Górszczak, M., Rychalska, B., Lewandowska, K., Trzcinski, T. P., & Zieliński, B. (2023) · IEEE Access · doi:10.1109/access.2023.3242982 · Cited by 3

Probing Cross-Modal Representations in Multi-Step Relational Reasoning

Parfenova, I., Elliott, D., Fernández, R., & Pezzelle, S. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.16 · Cited by 2

What can Neural Referential Form Selectors Learn?

Chen, G., Same, F., & van Deemter, K. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.15 · Cited by 4

AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models

Ryb, S., Giulianelli, M., Sinclair, A., & Fernández, R. (2022) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2022.starsem-1.5 · Cited by 1

On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers

Samardżić, T., Gutierrez-Vasques, X., van der Goot, R., Müller-Eberstein, M., Pelloni, O., & Plank, B. (2022) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2022.conll-1.18 · Cited by 1

Intrinsic Knowledge Evaluation on Chinese Language ModelsPreprint

Wang, Z., & Hu, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.14277 · Cited by 1

Learning Chess Blindfolded: Evaluating Language Models on State Tracking.Preprint

Toshniwal, S., Wiseman, S., Livescu, K., & Gimpel, K. (2021) · arXiv (Cornell University) · Cited by 1

What if This Modified That? Syntactic Interventions via Counterfactual EmbeddingsPreprint

Tucker, M., Qian, P., & Lévy, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.14002 · Cited by 7

Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal ImagesPreprint

Blumer, K., Venugopalan, S., Brenner, M. P., & Kleinberg, J. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11468 · Cited by 1

Training dynamics of neural language models

Saphra, N. (2021) · ERA · doi:10.7488/era/1421 · Cited by 1

Transformer with a Mixture of Gaussian Keys.Preprint

Nguyen, T., Nguyen, T. M., Le, D. D., Nguyen, K. C. D., Tran, A. N., Baraniuk, R. G., Ho, N., & Osher, S. (2021) · arXiv (Cornell University) · Cited by 1

Probing as Quantifying the Inductive Bias of Pre-trained Representations.Preprint

Immer, A., Hennigen, L. T., Fortuin, V., & Cotterell, R. (2021) · arXiv (Cornell University) · Cited by 1

Exploring Variable Synergy in Multi-Task Deep Learning for Hydrological ModelingPreprint

Ouyang, W., Gu, X., Ye, L., Liu, X., & Zhang, C. (2023) · doi:10.22541/essoar.170066948.83679544/v1 · Cited by 1

Evaluation and Integration of Structural Knowledge for Pre-trained Language Models

Lovón-Melgarejo, J. (2024) · theses.fr (ABES) · doi:10.70675/9b2d6bf6za13az4dfcza45bzca75ad5b25a6 · Cited by 1

From Human Cognition to Neural Activations: Probing the Computational Primitives of Spatial Reasoning in LLMs

An, J., Yang, L., Wang, M., Lu, L., An, W., & Yang, E. (2026) · arXiv · doi:10.48550/arxiv.2603.26323

Language Models Use Monotonicity to Assess NPI Licensing

Jumelet, J., Denić, M., Szymanik, J., Hupkes, D., & Steinert‐Threlkeld, S. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.439 · Cited by 21

Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question AnsweringPreprint

Liang, Z., Zhao, Y., & Surdeanu, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10791

diagNNose: A Library for Neural Activation AnalysisPreprint

Jumelet, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06819

Bird's Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic ApproachPreprint

Hou, Y., & Sachan, M. (2021) · arXiv (Cornell University) · doi:10.3929/ethz-b-000519233

Fine-grained Interpretation and Causation Analysis in Deep NLP ModelsPreprint

Sajjad, H., Kokhlikyan, N., Dalvi, F., & Durrani, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08039

What can Neural Referential Form Selectors Learn?Preprint

Chen, G., Same, F., & van Deemter, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06806

Conditional probing: measuring usable information beyond a baselinePreprint

Hewitt, J., Ethayarajh, K., Liang, P., & Manning, C. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09234 · Cited by 8

Adversarial Scrubbing of Demographic Information for Text ClassificationPreprint

Chowdhury, S. B. R., Ghosh, S., Li, Y., Oliva, J. B., Srivastava, S., & Chaturvedi, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08613 · Cited by 4

Connecting Attributions and QA Model Behavior on Realistic CounterfactualsPreprint

Ye, X., Nair, R., & Durrett, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04515 · Cited by 9

Controlled tasks for model analysis: Retrieving discrete information from sequences

Sorodoc, I.-T., Boleda, G., & Baroni, M. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.37

How much pretraining data do language models need to learn syntax?Preprint

Pérez-Mayos, L., Ballesteros, M., & Wanner, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03160 · Cited by 5

Supplement on CC1

Lees, T., Reece, S., Kratzert, F., et al. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-2021-566-cc2

Comment on hess-2021-566

Lees, T., Reece, S., Kratzert, F., et al. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-2021-566-rc1

Comment on hess-2021-566

Lees, T., Reece, S., Kratzert, F., et al. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2021-566-rc2

Reply on RC2

Lees, T. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2021-566-ac3

Reply on RC1

Lees, T. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2021-566-ac2

Reply on CC1

Lees, T. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-2021-566-ac1

Visualizing the Relationship Between Encoded Linguistic Information and Task Performance

Xiang, J., Li, H., Lian, D., Huang, G., Watanabe, T., & Liu, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.35

Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language

(2021) · doi:10.18653/v1/2021.unimplicit-1

How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge?

Sun, S., Dillon, B., & Iyyer, M. (2022) · Workshop on Insights from Negative Results in NLP (Insights) · doi:10.18653/v1/2022.insights-1.6

Incorporating Typological Information into Pretrained Word Representations for Improved Performance

Subramaniam, V. (2022) · International Conference on Control, Robotics and Intelligent System (CCRIS) · doi:10.1145/3562007.3562026

Probing BERT on Syntactic Representation Paradigms: Dependency vs Constituency TreesPreprint

Alzetta, C., Miaschi, A., Dell’Orletta⋄, F., Venturi⋄, G., & Montemagni⋄, S. (2022) · Research Square · doi:10.21203/rs.3.rs-2202147/v1

Trust, but Verify: Using Self-supervised Probing to Improve Trustworthiness

Deng, A., Shen, L., Xiong, M., Chen, Z., & Hooi, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_21

Evaluer, diagnostiquer et analyser la traduction automatique neuronale

Yvon, F. (2022) · FORUM Revue internationale d’interprétation et de traduction / International Journal of Interpretation and Translation · doi:10.1075/forum.00023.yvo · Cited by 1

Meta-learning based instance manipulation for implicit discourse relation recognition

Zeng, J., Xie, B., Wu, C., Yin, Y., Zeng, H., & Su, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110457

The Inexplicable Efficacy of Language Models

Aiyappa, R., & Kachwala, Z. (2023) · XRDS Crossroads The ACM Magazine for Students · doi:10.1145/3589654

Syntax and Geometry of Information

Bailly, R., Leblond, L., & Gábor, K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.590

The Hidden Folk: Linguistic Properties Encoded in Multilingual Contextual Character Representations

Agirrezabal, M., Boldsen, S., & Hollenstein, N. (2023) · doi:10.18653/v1/2023.cawl-1.2

Improving Syntactic Probing Correctness and Robustness with Control Tasks

Ma, W., Wang, B. Z., Zhang, H., Wang, L., Coto‐Solano, R., Hassanpour, S., & Vosoughi, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.35

Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)

(2022) · doi:10.18653/v1/2022.umios-1

Universal and Independent: Multilingual Probing Framework for Exhaustive Model Interpretation and Evaluation

Serikov, O., Protasov, V., Волошина, Е. В., Knyazkova, V., & Shavrina, T. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.37 · Cited by 2

Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures

Prange, J., & Chersoni, E. (2023) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2023.starsem-1.40

Is German secretly a Slavic language? What BERT probing can tell us about language groups

Mysiak, A., & Cyranka, J. (2023) · Workshop on Slavic Natural Language Processing (SlavicNLP) · doi:10.18653/v1/2023.bsnlp-1.11

Decoding the Encoded – Linguistic Secrets of Language Models: A Systematic Literature Review

Avetisyan, H., & Broneske, D. (2023) · International Conference on Machine Learning Techniques and NLP (MLNLP) · doi:10.5121/csit.2023.131606

Representational Structure

Beinborn, L., & Hollenstein, N. (2023) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-43260-6_5

Systematic Generalization by Finetuning? Analyzing Pretrained Language Models Using Constituency Tests

Chakraborty, A., Cheung, J. C., & O’Donnell, T. J. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.27

METAPROBE: A Representation- and Task-Agnostic Probe

Zhou, Y., & Srikumar, V. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.18

Disentangling the Linguistic Competence of Privacy-Preserving BERT

Arnold, S., Kemmerzell, N., & Schreiner, A. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.5

Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution

Jumelet, J., & Zuidema, W. H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.288 · Cited by 1

Using Artificial French Data to Understand the Emergence of Gender Bias in Transformer Language Models

Conti, L., & Wisniewski, G. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.641

Emergence of Abstract State Representations in Embodied Sequence Modeling

Yun, T., Zeng, Z., Handa, K., Thapliyal, A., Pang, B., Pavlick, E., & Sun, C. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.749 · Cited by 6

Rethinking the Construction of Effective Metrics for Understanding the Mechanisms of Pretrained Language Models

Li, Y., Yin, J., & Lin, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.894

A State-Vector Framework for Dataset Effects

Sahak, E., Zhu, Z., & Rudzicz, F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.942

How to Do Maths with Words: Neural Machine Learning Applications to Mathematics and Their Philosophical Significance

Gastaldi, J. L. (2023) · Springer eBooks · doi:10.1007/978-3-030-19071-2_142-1

Serial Comptroller NetworksPreprint

Spasokukotskiy, K. (2024) · doi:10.22541/au.172081298.83306356/v1

Topic aware probing: From sentence length prediction to idiom identification how reliant are neural language models on topic?

Nedumpozhimana, V., & Kelleher, J. D. (2024) · Natural language processing. · doi:10.1017/nlp.2024.43 · Cited by 1

Probing the Pitfalls: Understanding SVD’s Shortcomings in Language Model Compression

Pletenev, S. (2024) · Journal of language and Education · doi:10.17323/jle.2024.22368

Enhancing Interaction Graph of Data Schema and Syntactic Structure with Pre-trained Language Model for Text-to-SQL

Zhao, W., Zhao, L., Wu, F., Zheng, Z., Jin, H., & Gu, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1024-2_12

Hidden Pieces: An Analysis of Linear Probes for GPT Representation Edits

Davis, A., & Sukthankar, G. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00073 · Cited by 2

Parallel Trees: a novel resource with aligned dependency and constituency syntactic representations

Alzetta, C., Miaschi, A., Dell’Orletta⋄, F., Venturi⋄, G., & Montemagni⋄, S. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09826-3

Property-driven localization and characterization in deep molecular representations

Cintas, C., Das, P., Ross, J., Belgodere, B. M., Tadesse, G. A., Chenthamarakshan, V., Born, J., & Speakman, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-09717-1

DeepCodeProbe: Evaluating Code Representation Quality in Models Trained on Code

Majdinasab, V., Nikanjam, A., & Khomh, F. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10731-0 · Cited by 3

Interpreting the Structure of Multi-object Representations in Vision Encoders

Khajuria, T., Dias, B. O., Domnich, M., & Aru, J. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08324-1_16 · Cited by 2

A Constitutional Alignment Framework for AI Governance: Transplanting Corporate Veil-Piercing with Semantic Reachability MappingPreprint

Zhang, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5741382

What Do Biological Foundation Models Compute? Sparse Autoencoders from Feature Recovery to Mechanistic InterpretabilityPreprint

Orlov, A. V., Makus, Y. V., Ashniev, G. A., Orlova, N. N., & Nikitin, P. I. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.04.709491 · Cited by 2

Contextualization and Generalization in Entity and Relation Extraction

Taillé, B. (2022) · theses.fr (ABES) · doi:10.70675/72fea173z5549z4bd5z8332zf81f26d3c2b9

Linear Lens: A human-centered, non-interventional mechanistic approach to explainable AI

Danish, M. U., Aziz, M., Rehman, U., & Grolinger, K. (2026) · Machine Learning with Applications · doi:10.1016/j.mlwa.2026.100897

How Can We Make Language Models Better at Handling the Diversity and Variability of Natural Languages ?

Müller, B. (2022) · theses.fr (ABES) · doi:10.70675/6984dc63z4731z4eabzb3fazaf5d012fe143

Aligned Probing: Relating Toxic Behavior and Model Internals

Waldis, A., Gautam, V., Lauscher, A., Klakow, D., & Gurevych, I. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.613 · Cited by 4

On the Status of Word Embeddings as Implementations of the Distributional Hypothesis

Mickus, T. (2022) · theses.fr (ABES) · doi:10.70675/cfc2b3e0zdc1dz4f02za2cezd8116a4bd01e

Word Meaning Representation in Neural Language Models : Lexical Polysemy and Semantic Relationships

Soler, A. G. (2021) · theses.fr (ABES) · doi:10.70675/99a10411zc05ez4135z8f31z13573e0fd4d1

Neural Approaches to Historical Words Reconstruction

Fourrier, C. (2022) · theses.fr (ABES) · doi:10.70675/647b8a58zeea0z48cdz8229zf1d7ee8b0742 · Cited by 2

What do you know, BERT ? Exploring the linguistic competencies of Transformer-based contextual word embeddings

Metheniti, E. (2023) · theses.fr (ABES) · doi:10.70675/2f81071bz4724z44ecz999dz1acee85d8951 · Cited by 2

Study of the abstraction capabilities of neural language models

Li, B. (2023) · theses.fr (ABES) · doi:10.70675/0236e084zee1ez4013z8f5bz6eebee3f0e4e

Data-Based Natural Language Generation : Evaluation and Explainability

Faille, J. (2023) · theses.fr (ABES) · doi:10.70675/3e73dd43z7aa6z4405za5cez27c78b408a3c

Study of the multimodal understanding of vision-language transformer models

Salin, E. (2023) · theses.fr (ABES) · doi:10.70675/2beca704z5d3bz421dzbf61z61e00610aaeb

Probing Content and Channel in Speaker Verification Models

Sergidou, E.-K., Bikker, D., Pouw, C., Rohdin, J., Geradts, Z., & Worring, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463177

Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention

Zhou, W., Zou, Y., Gu, C., Dong, R., Xie, H., & Zhang, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37627-5_20

53A survey on latent linguistic patterns in bidirectional transformer-based language models

Kugler, K., & Rettinger, A. (2026) · De Gruyter eBooks · doi:10.1515/9783111199740-003

Dimension-Wise Probing of Music Foundation Models for Chinese and Western Music: Timbre, Technique, Key, and Mode

Bai, X., Hui, N., Li, Y., & Han, X. (2026) · Electronics · doi:10.3390/electronics15153421

Cross-Lingual Transfer in Multilingual Foundation Models: Mechanisms and Optimization

James, G. M. (2026) · Eduschool Journal of Artificial Intelligence Research (EJAIR) · doi:10.63090/ejair/3139.7336.0001

JURY: A Comprehensive Training-Free to Fully-Supervised Framework for Evaluating Chemical Language ModelsPreprint

Khan, D. U., Aamer, N., Sajjad, M., Vollmer, S., Dengel, A., & Asim, M. N. (2026) · Research Square · doi:10.21203/rs.3.rs-10420771/v1

REFLEX: Finding and Defending Refusal Mechanisms in LLMs using Activation GuidancePreprint

Vishwakarma, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7360401

Human-Level Accuracy, Non-human Strategies: Revealing Model–Human Divergence in Video Physical Reasoning

Li, F., Zheng, S., Yin, Z., Cui, J., Ji, L., & Liu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37132-4_5

TDSR-VLA: Transition-Aware Denoising Sequence Representations for Vision-Language-Action

Kim, D., Song, K., Lee, S., Ju, H., Cha, E. S., & Kim, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37013-6_27

What Do Process Embeddings Learn? A Probing Investigation

Köllschen, F., Grohs, M., & Rehse, J.-R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37777-7_28

From Saliency Maps to Circuits: An Integrative Survey of Explainable AI and Mechanistic Interpretability

Dugăeșescu, A., & Florea, A. M. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8100315

Recovering Predefined Door-Placement Rules from BIM Geometry: A Controlled Benchmark of Graph Transformers over Neural Signed Distance Function Embeddings

Du, S., Hou, L., & Zhang, G. (2026) · Buildings · doi:10.3390/buildings16214194

Depressive Belief Rigidity in Language Models: Representation and Limits of Activation-Based Control

Xie, S. (2026) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap71869.2026.11727911

Improving Syntactic Probing Correctness and Robustness with Control Tasks

Ma, W., Wang, B. Z., Zhang, H., Wang, L., Coto‐Solano, R., Hassanpour, S., & Vosoughi, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.35

Embodied Knowledge in Natural Language Processing: A Prolog-Based Approach to Selected bAbI Tasks

Cuartero-Sánchez, J. M. (2025) · doi:10.13140/rg.2.2.29837.65763

A study on Inference Control in Natural Language Processing(自然言語処理における推論の制御に関する研究)

宮田, 高. (1996) · doi:10.11501/3139771

The integration of language and content: action research on a theory of task design

Jones, A., & Sin, S. (2007) · Journal of Applied Linguistics · doi:10.1558/japl.v1i1.95 · Cited by 2

Natural Language Interface Put in Perspective: Interaction of Search Method and Task Complexity

Wang, Q., Hu, J., & Nass, C. (2005) · Natural Language Processing and Cognitive Science · doi:10.5220/0002566000030012