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

(2014) · Cited by 33,419

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

Holmes ⌕ A Benchmark to Assess the Linguistic Competence of Language Models

Waldis, A., Waldis, A., Perlitz, Y., Choshen, L., Choshen, L., Hou, Y., & Gurevych, I. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00718 · Cited by 23

Morph Call: Probing Morphosyntactic Content of Multilingual Transformers

Mikhailov, V., Serikov, O., & Artemova, E. (2021) · doi:10.18653/v1/2021.sigtyp-1.10 · Cited by 9

Screening Gender Transfer in Neural Machine Translation

Wisniewski, G., Zhu, L., Bailler, N., & Yvon, F. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.24 · Cited by 6

Nucleus Composition in Transition-based Dependency Parsing

Nivre, J., Basirat, A., Dürlich, L., & Moss, A. (2022) · Computational Linguistics · doi:10.1162/coli_a_00450 · Cited by 6

SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

Lauscher, A., Bianchi, F., Bowman, S. R., & Hovy, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.539 · Cited by 7

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

Do Transformers Parse while Predicting the Masked Word?

Zhao, H., Panigrahi, A., Ge, R., & Arora, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1029 · Cited by 39

Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained Models

Wu, Z., Liu, N. F., & Potts, C. (2022) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2022.repl4nlp-1.11 · Cited by 6

Naturalistic Causal Probing for Morpho-Syntax

Amini, A., Pimentel, T., Meister, C., & Cotterell, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00554 · Cited by 14

Lexical Semantics with Large Language Models: A Case Study of English “break”

Petersen, E., & Potts, C. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.36 · Cited by 10

Can BERT Reason? Logically Equivalent Probes for Evaluating the Inference Capabilities of Language Models.Preprint

Zhou, P., Khanna, R., Lin, B. Y., Ho, D. E., Ren, X., & Pujara, J. (2020) · arXiv (Cornell University) · Cited by 6

BERT's output layer recognizes all hidden layers? Some Intriguing Phenomena and a simple way to boost BERTPreprint

Kao, W.-T., Wu, T.-H., Chi, P.-H., Hsieh, C.-C., & Lee, H.-Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09309 · Cited by 19

Approximating How Single Head Attention LearnsPreprint

Snell, C., Zhong, R., Klein, D., & Steinhardt, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07601 · Cited by 9

Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)

(2022) · doi:10.18653/v1/2022.trustnlp-1 · Cited by 6

Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau

Zhu, Z., Tang, X., Zhang, J., Tang, Y., Liu, L., Gao, C., Zhang, S., Liu, Y., Jin, J., Liu, C., et al. (2025) · Water Resources Research · doi:10.1029/2024wr038515 · Cited by 8

From voice to safety: Language AI powered pilot-ATC communication understanding for airport surface movement collision risk assessment

Pang, Y., Kendall, A. P., Porcayo, A., Barsotti, M., Jain, A., & Clarke, J.-P. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105540 · Cited by 11

Fine-grained Interpretation and Causation Analysis in Deep NLP Models

Sajjad, H., Kokhlikyan, N., Dalvi, F., & Durrani, N. (2021) · doi:10.18653/v1/2021.naacl-tutorials.2 · Cited by 5

Measuring Memorization Effect in Word-Level Neural Networks Probing

Rosa, R., Musil, T., & Mareček, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58323-1_19 · Cited by 5

Is Continuous Prompt a Combination of Discrete Prompts? Towards a Novel View for Interpreting Continuous Prompts

Ju, T., Zheng, Y., Wang, H., Zhao, H., & Liu, G. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.494 · Cited by 4

Decomposing Natural Logic Inferences for Neural NLI

Rozanova, J., Ferreira, D. M., Thayaparan, M., Valentino, M., & Freitas, A. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.33 · Cited by 5

Untitled work

(2022) · doi:10.18653/v1/2022.conll-1.8 · Cited by 5

Toward Neurosymbolic Program Comprehension

Velasco, A., Garryyeva, A., Palacio, D. N., Mastropaolo, A., & Poshyvanyk, D. (2025) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00047 · Cited by 5

Quantifying the Contextualization of Word Representations with Semantic Class Probing

Zhao, M., Dufter, P., Yaghoobzadeh, Y., & Schütze, H. (2020) · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · doi:10.5282/ubm/epub.74039 · Cited by 5

Exploring Neural Entity Representations for Semantic Information

Runge, A., & Hovy, E. H. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.20 · Cited by 7

Exploring Pair-Wise NMT for Indian LanguagesPreprint

Akella, K., Allu, S. H., Ragupathi, S., Singhal, A., Khan, Z., Namboodiri, V. P., & Jawahar, C. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.05786 · Cited by 5

Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

(2021) · doi:10.18653/v1/2021.sigtyp-1 · Cited by 5

Sort by Structure: Language Model Ranking as Dependency Probing

Müller-Eberstein, M., Goot, R., & Plank, B. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.93 · Cited by 4

Neural referential form selection: Generalisability and interpretability

Chen, G., Same, F., & van Deemter, K. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101466 · Cited by 5

A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys

Nguyen, T. M., Nguyen, T., Bui, L., Do, H., Nguyen, D. K., Le, D. D., Tran-The, H., Ho, N., Osher, S., & Baraniuk, R. G. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096107 · Cited by 5

On the Transformation of Latent Space in Fine-Tuned NLP Models

Durrani, N., Sajjad, H., Dalvi, F., & Alam, F. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.97 · Cited by 10

Probing Script Knowledge from Pre-Trained Models

Jin, Z., Zhang, X., Yu, M., & Huang, L. (2022) · doi:10.18653/v1/2022.umios-1.10 · Cited by 8

Emergence of Syntax Needs Minimal Supervision

Bailly, R., & Gábor, K. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.46 · Cited by 4

The Rediscovery Hypothesis: Language Models Need to Meet Linguistics

Nikoulina, V., Tezekbayev, M., Kozhakhmet, N., Babazhanova, M., Gallé, M., & Assylbekov, Z. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12788 · Cited by 4

Does My Representation Capture X? Probe-Ably

Ferreira, D. M., Rozanova, J., Thayaparan, M., Valentino, M., & Freitas, A. (2021) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2021.acl-demo.23 · Cited by 5

On the data requirements of probing

Zhu, Z., Wang, J., Li, B., & Rudzicz, F. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.326 · Cited by 5

Probing LLMs for Joint Encoding of Linguistic Categories

Starace, G., Papakostas, K., Choenni, R., Panagiotopoulos, A., Rosati, M., Leidinger, A., & Shutova, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.476 · Cited by 15

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Davis, F., & van Schijndel, M. (2021) · doi:10.18653/v1/2021.acl-long.93 · Cited by 7

Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties

Miaschi, A., Sarti, G., Brunato⋄, D., Dell’Orletta⋄, F., & Venturi⋄, G. (2022) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.965 · Cited by 4

Evaluating Transformer’s Ability to Learn Mildly Context-Sensitive Languages

Wang, S., & Steinert‐Threlkeld, S. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.21 · Cited by 4

Probing Across Time: What Does RoBERTa Know and When?Preprint

Liu, L. Z., Wang, Y., Kasai, J., Hajishirzi, H., & Smith, N. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07885 · Cited by 4

Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance LevelPreprint

Zhong, R., Ghosh, D., Klein, D., & Steinhardt, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06020 · Cited by 7

Influence Patterns for Explaining Information Flow in BERTPreprint

Lu, K., Wang, Z., Mardziel, P., & Datta, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00740 · Cited by 4

Improving neural machine translation of languages with little data and rich morphology

Dhar, P. (2024) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.859674171 · Cited by 3

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

Gastaldi, J. L. (2024) · Springer eBooks · doi:10.1007/978-3-031-40846-5_142 · Cited by 3

Unboxing the Black Box: A Survey on Mechanistic Interpretability for Algorithmic Understanding of Neural Networks

Kowalska, B., & Kwaśnicka, H. (2026) · Machine Learning · doi:10.1007/s10994-026-07129-4 · Cited by 11

On Robustness and Sensitivity of a Neural Language Model: A Case Study on Italian L1 Learner Errors

Miaschi, A., Brunato⋄, D., Dell’Orletta⋄, F., & Venturi⋄, G. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3226333 · Cited by 3

Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces

Levy, T. J., Goldman, O., & Tsarfaty, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.348 · Cited by 4

Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets

Zhang, L., Prokhorov, V., & Shareghi, E. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.14 · Cited by 3

Probe-Less Probing of BERT’s Layer-Wise Linguistic Knowledge with Masked Word Prediction

Aoyama, T., & Schneider, N. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.25 · Cited by 3

The Architectural Bottleneck Principle

Pimentel, T., Valvoda, J., Stoehr, N., & Cotterell, R. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.788 · Cited by 7

BERT Shows Garden Path Effects

Irwin, T., Wilson, K., & Marantz, A. P. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.235 · Cited by 2

Interventional Probing in High Dimensions: An NLI Case Study

Rozanova, J., Valentino, M., Cordeiro, L. C., & Freitas, A. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.188 · Cited by 3

Assessment of Pre-Trained Models Across Languages and Grammars

Muñoz-Ortiz, A., Vilares, D., & Gómez‐Rodríguez, C. (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.23 · Cited by 5

Joint Embedding Learning and Latent Subspace Probing for Cross-Domain Few-Shot Keyword Spotting

Özay, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446764 · Cited by 3

Controlling the Imprint of Passivization and Negation in Contextualized Representations

Çelikkanat, H., Virpioja, S., Tiedemann, J., & Apidianaki, M. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.13 · Cited by 7

Is neural language acquisition similar to natural? A chronological probing study

Волошина, Е. В., Serikov, O., Shavrina, T., & AIRI, S. M. (2022) · Computational Linguistics and Intellectual Technologies · doi:10.28995/2075-7182-2022-21-550-563 · Cited by 4

NeuroX Library for Neuron Analysis of Deep NLP Models

Dalvi, F., Sajjad, H., & Durrani, N. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.21 · Cited by 5

Probing with Noise: Unpicking the Warp and Weft of Embeddings

Klubička, F., & Kelleher, J. D. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.34 · Cited by 3

Natural language guided object retrieval in images

Ostovar, A., Bensch, S., & Hellström, T. (2021) · Acta Informatica · doi:10.1007/s00236-021-00400-2 · Cited by 3

Intrinsic Probing through Dimension Selection

Hennigen, L. T., Williams, A., & Cotterell, R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.15 · Cited by 41

Understanding Game-Playing Agents with Natural Language Annotations

Tomlin, N., He, A., & Klein, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.90 · Cited by 5

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning

Xie, S., Qiu, J., Pasad, A., Du, L., Qu, Q., & Mei, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.422 · Cited by 12

Heads-up! Unsupervised Constituency Parsing via Self-Attention HeadsPreprint

Li, B., Kim, T., Amplayo, R. K., & Keller, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09517 · Cited by 3

Do Language Embeddings Capture Scales?Preprint

Zhang, X., Ramachandran, D., Tenney, I., Elazar, Y., & Roth, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05345 · Cited by 24

Finding structure during incremental speech comprehensionPreprint

Lyu, B., Marslen‐Wilson, W. D., Fang, Y., & TYLER, L. K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.25.465687 · Cited by 3

A unifying computational account of temporal context effects in language across the human cortexPreprint

Vo, V. A., Jain, S., Beckage, N., Chien, H.-Y. S., Obinwa, C., & Huth, A. G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.03.551886 · Cited by 5

From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step

Deng, Y., Choi, Y., & Shieber, S. (2024) · arXiv · doi:10.48550/arxiv.2405.14838 · Cited by 3

Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training

Müller-Eberstein, M., van der Goot, R., Plank, B., & Titov, I. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.879 · Cited by 7

Multilingual prediction of semantic norms with language models: a study on English and Chinese

Peng, B., Hsu, Y.-Y., Chersoni, E., Le, Q., & Huang, C. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09866-9 · Cited by 3

Partial-label metric ceilings for evaluating gene regulatory networks inferred from single-cell foundation models

Kendiukhov, I. (2026) · Biosystems · doi:10.1016/j.biosystems.2026.105864 · Cited by 4

Bridging Perception, Memory, and Inference through Semantic Relations

Björklund, J., Lindström, A. D., & Drewes, F. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.719 · Cited by 3

Testing the Effectiveness of the Diagnostic Probing Paradigm on Italian Treebanks

Miaschi, A., Alzetta, C., Brunato⋄, D., Dell’Orletta⋄, F., & Venturi⋄, G. (2023) · Information · doi:10.3390/info14030144 · Cited by 2

Spectral Probing

Müller-Eberstein, M., van der Goot, R., & Plank, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.527 · Cited by 2

Using Roark-Hollingshead Distance to Probe BERT’s Syntactic Competence

Niu, J., Lu, W., Corlett, E., & Penn, G. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.27 · Cited by 3

Understanding the Inner-workings of Language Models Through Representation Dissimilarity

Brown, D., Godfrey, C., Konz, N., Tu, J. H., & Kvinge, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.403 · Cited by 2

Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models

Hou, Y., Li, J., Fei, Y., Stolfo, A., Zhou, W., Zeng, G., Bosselut, A., & Sachan, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.299 · Cited by 42

Using the Hammer only on Nails: A Hybrid Method for Representation-Based Evidence Retrieval for Question Answering

Liang, Z., Zhao, Y., & Surdeanu, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_22 · Cited by 3

Probing Tasks Under Pressure

Miaschi, A., Alzetta, C., Brunato⋄, D., Dell’Orletta⋄, F., & Venturi⋄, G. (2022) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.10729 · Cited by 2

Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models

Yi, D., Bruno, J. V., Han, J., Zukerman, P., & Steinert‐Threlkeld, S. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.12 · Cited by 3

Biomedical Interpretable Entity Representations

García-Olano, D., Onoe, Y., Baldini, I., Ghosh, J., Wallace, B., & Varshney, K. R. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.311 · Cited by 2

Test Harder than You Train: Probing with Extrapolation Splits

Kunz, J., & Kuhlmann, M. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.2 · Cited by 3

Post-hoc analysis of Arabic transformer models

Abdelalí, A., Durrani, N., Dalvi, F., & Sajjad, H. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.8 · Cited by 8

What Has Been Enhanced in my Knowledge-Enhanced Language Model?

Hou, Y., Fu, G., & Sachan, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.102 · Cited by 6

BERTering RAMS: What and How Much does BERT Already Know About Event Arguments? - A Study on the RAMS Dataset

Gangal, V., & Hovy, E. H. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.1 · Cited by 3

Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer

Kashyap, A. R., Mehnaz, L., Malik, B., Waheed, A., Hazarika, D., Kan, M., & Shah, R. R. (2021) · National University of Singapore · Cited by 2

Representation and Pre-Activation of Lexical-Semantic Knowledge in Neural Language Models

Derby, S., Devereux, B. J., & Miller, P. C. (2021) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2021.cmcl-1.25 · Cited by 5

Explaining the Road Not TakenPreprint

Shen, H., & Huang, T.-H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14973 · Cited by 4

Probing Word Translations in the Transformer and Trading Decoder for Encoder LayersPreprint

Xu, H., van Genabith, J., Liu, Q., & Xiong, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09586 · Cited by 2

Analyzing Gender Translation Errors to Identify Information Flows between the Encoder and Decoder of a NMT System

Wisniewski, G., Zhu, L., Ballier, N., & Yvon, F. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.13 · Cited by 1

Representation of Lexical Stylistic Features in Language Models’ Embedding Space

Lyu, Q., Apidianaki, M., & Callison-Burch, C. (2023) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2023.starsem-1.32 · Cited by 4

Counterfactually Probing Language Identity in Multilingual Models

Srinivasan, A., Govindarajan, V. S., & Mahowald, K. (2023) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2023.mrl-1.3 · Cited by 2

Can LLMs Facilitate Interpretation of Pre-trained Language Models?

Mousi, B., Durrani, N., & Dalvi, F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.196 · Cited by 8

Implications of Annotation Artifacts in Edge Probing Test Datasets

Choudhury, S. R., & Kalra, J. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.39 · Cited by 1

Textual Identifiability and Anchored Semantics for Evaluating World-Model Claims in Large Language Models

Lan, Z., & Yang, J. (2026) · IEEE Access · doi:10.1109/access.2026.3740087 · Cited by 1

Augmenting BERT with CNN for Multiple Choice Question Answering

Roy, S., Ehtesham, N., Islam, M. S., & Marium-E-Jannat (2021) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit54785.2021.9689877 · Cited by 2

Intermediate Entity-based Sparse Interpretable Representation Learning

García-Olano, D., Onoe, Y., Ghosh, J., & Wallace, B. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.17 · Cited by 3

On Quick Kisses and How to Make Them Count: A Study on Event Construal in Light Verb Constructions with BERT

Liu, C., & Chersoni, E. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.28 · Cited by 1

Understanding Probe Behaviors Through Variational Bounds of Mutual Information

Choi, K., Jung, J.-W., & Watanabe, S. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448065 · Cited by 3

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

Exploring Span Representations in Neural Coreference Resolution

Kahardipraja, P., Vyshnevska, O., & Loáiciga, S. (2020) · Workshop on Computational Approaches to Discourse (CODI) · doi:10.18653/v1/2020.codi-1.4 · Cited by 1

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