---
title: "Who cites Naomi Saphra — 445 papers of our library"
description: "445 papers of the Elamur Research library cite the works of Naomi Saphra, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/naomi-saphra/cited-by/"
---
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# Who cites Naomi Saphra

470 citations of their works from 445 papers of our library (of the 149,439 whose bibliographies we read) · 88 with a page here, the most cited first · [← Naomi Saphra](https://md.research.elamur.ai/free/en/authors/naomi-saphra/)

1. [**PyTorch: An Imperative Style, High-Performance Deep Learning Library arXiv:1912.01703**Adam Paszke, Sam Gross, Francisco Massa, et al.*2019 · 18,645 citations*](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/)
2. [**AllenNLP: A Deep Semantic Natural Language Processing Platform arXiv:1803.07640**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
3. **[A Structural Probe for Finding Syntax in Word Representations](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**John Hewitt, Christopher D. Manning*2019 · 1,254 citations*
4. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
5. [**Marian: Fast Neural Machine Translation in C++ arXiv:1804.00344**Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, et al.*2018 · 507 citations*](https://md.research.elamur.ai/free/en/papers/junczys-dowmunt-2018-marian-fast-neural-machine-translation/)
6. **[Multilingual and Multi-Aspect Hate Speech Analysis](https://md.research.elamur.ai/free/en/papers/ousidhoum-2019-multilingual-multi-aspect-hate-speech/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, et al.*2019 · 411 citations*
7. [**Editing Models with Task Arithmetic arXiv:2212.04089**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
8. **[Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space](https://md.research.elamur.ai/free/en/papers/geva-2022-transformer-feed-forward-layers-build/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mor Geva, Avi Caciularu, Kevin Ro Wang, et al.*2022 · 346 citations*
9. **[Information-Theoretic Probing with Minimum Description Length](https://md.research.elamur.ai/free/en/papers/voita-2020-information-theoretic-probing-minimum-description/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Elena Voita, Ivan Titov*2020 · 345 citations*
10. **[Search-based Neural Structured Learning for Sequential Question Answering](https://md.research.elamur.ai/free/en/papers/iyyer-2017-search-based-neural-structured-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang*2017 · 330 citations*
11. **[A Minimal Span-Based Neural Constituency Parser](https://md.research.elamur.ai/free/en/papers/stern-2017-minimal-span-based-neural-constituency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mitchell Stern, Jacob Andreas, Dan Klein*2017 · 311 citations*
12. [**Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow arXiv:1805.08949**Pengcheng Yin, Bowen Deng, Edgar Chen, et al.*2018 · 308 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-learning-mine-aligned-code-natural/)
13. **[Information-Theoretic Probing for Linguistic Structure](https://md.research.elamur.ai/free/en/papers/pimentel-2020-information-theoretic-probing-linguistic-structure/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, et al.*2020 · 298 citations*
14. **[Selective Attention for Context-aware Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/maruf-2019-selective-attention-context-aware-neural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Sameen Maruf, André F. T. Martins, Gholamreza Haffari*2019 · 263 citations*
15. **[Tied Multitask Learning for Neural Speech Translation](https://md.research.elamur.ai/free/en/papers/anastasopoulos-2018-tied-multitask-learning-neural-speech/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Antonios Anastasopoulos, David Chiang*2018 · 253 citations*
16. **[Document Context Neural Machine Translation with Memory Networks](https://md.research.elamur.ai/free/en/papers/maruf-2018-document-context-neural-machine-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sameen Maruf, Gholamreza Haffari*2018 · 238 citations*
17. **[Strong Baselines for Neural Semi-Supervised Learning under Domain Shift](https://md.research.elamur.ai/free/en/papers/ruder-2018-strong-baselines-neural-semi-supervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sebastian Ruder, Barbara Plank*2018 · 229 citations*
18. **[MTNT: A Testbed for Machine Translation of Noisy Text](https://md.research.elamur.ai/free/en/papers/michel-2018-mtnt-testbed-machine-translation-noisy/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Paul Michel, Graham Neubig*2018 · 227 citations*
19. **[The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives](https://md.research.elamur.ai/free/en/papers/voita-2019-bottom-up-evolution-representations-transformer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Elena Voita, Rico Sennrich, Ivan Titov*2019 · 225 citations*
20. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 220 citations*
21. **[LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better](https://md.research.elamur.ai/free/en/papers/kuncoro-2018-lstms-can-learn-syntax-sensitive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Adhiguna Kuncoro, Chris Dyer, John Hale, et al.*2018 · 208 citations*
22. **[Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 190 citations*
23. **[Learning Language Representations for Typology Prediction](https://md.research.elamur.ai/free/en/papers/malaviya-2017-learning-language-representations-typology-prediction/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chaitanya Malaviya, Graham Neubig, Patrick Littell*2017 · 187 citations*
24. **[Neural Discourse Structure for Text Categorization](https://md.research.elamur.ai/free/en/papers/ji-2017-neural-discourse-structure-text-categorization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yangfeng Ji, Noah A. Smith*2017 · 181 citations*
25. **[Investigating Multilingual NMT Representations at Scale](https://md.research.elamur.ai/free/en/papers/kudugunta-2019-investigating-multilingual-nmt-representations-scale/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sneha Kudugunta, Ankur Bapna, Isaac Caswell, et al.*2019 · 176 citations*
26. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
27. **[Stronger Baselines for Trustable Results in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/denkowski-2017-stronger-baselines-trustable-results-neural/) [Workshop on Neural Machine Translation](https://md.research.elamur.ai/free/en/venues/workshop-on-neural-machine-translation/)**Michael Denkowski, Graham Neubig*2017 · 165 citations*
28. **[Extreme Adaptation for Personalized Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/michel-2018-extreme-adaptation-personalized-neural-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Michel, Graham Neubig*2018 · 161 citations*
29. **[Argument Mining with Structured SVMs and RNNs](https://md.research.elamur.ai/free/en/papers/niculae-2017-argument-mining-structured-svms-rnns/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vlad Niculae, Joonsuk Park, Claire Cardie*2017 · 160 citations*
30. [**Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold arXiv:1706.09528**Swabha Swayamdipta, Sam Thomson, Chris Dyer, et al.*2017 · 160 citations*](https://md.research.elamur.ai/free/en/papers/swayamdipta-2017-frame-semantic-parsing-softmax-margin/)
31. [**Generative Verifiers: Reward Modeling as Next-Token Prediction arXiv:2408.15240**Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.*2024 · 152 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-generative-verifiers-reward-modeling-as/)
32. **[Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.*2023 · 148 citations*
33. **[Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://md.research.elamur.ai/free/en/papers/ravichander-2021-probing-probing-paradigm-does-probing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy*2021 · 144 citations*
34. **[Dynamic Entity Representations in Neural Language Models](https://md.research.elamur.ai/free/en/papers/ji-2017-dynamic-entity-representations-neural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yangfeng Ji, Chenhao Tan, Sebastian Martschat, et al.*2017 · 144 citations*
35. [**Understanding the Effects of RLHF on LLM Generalisation and Diversity arXiv:2310.06452**Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/)
36. **[Diverse Demonstrations Improve In-context Compositional Generalization](https://md.research.elamur.ai/free/en/papers/levy-2023-diverse-demonstrations-improve-context-compositional/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Itay Levy, Ben Bogin, Jonathan Berant*2023 · 141 citations*
37. **[All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality](https://md.research.elamur.ai/free/en/papers/timkey-2021-all-bark-no-bite-rogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**William Timkey, Marten van Schijndel*2021 · 139 citations*
38. **[Pushing the Limits of Low-Resource Morphological Inflection](https://md.research.elamur.ai/free/en/papers/anastasopoulos-2019-pushing-limits-low-resource-morphological/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Antonios Anastasopoulos, Graham Neubig*2019 · 137 citations*
39. **[Scheduled Multi-Task Learning: From Syntax to Translation](https://md.research.elamur.ai/free/en/papers/kiperwasser-2018-scheduled-multi-task-learning-syntax/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Eliyahu Kiperwasser, Miguel Ballesteros*2018 · 130 citations*
40. **[Data Augmentation for Morphological Reinflection](https://md.research.elamur.ai/free/en/papers/silfverberg-2017-data-augmentation-morphological-reinflection/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Miikka Silfverberg, Adam Wiemerslage, Ling Liu, et al.*2017 · 122 citations*
41. [**Emergent Linear Representations in World Models of Self-Supervised Sequence Models arXiv:2309.00941**Neel Nanda, Andrew Lee, Martin Wattenberg*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-emergent-linear-representations-world-models/)
42. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
43. [**minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models arXiv:2203.13112**Kanishka Misra*2022 · 102 citations*](https://md.research.elamur.ai/free/en/papers/misra-2022-minicons-enabling-flexible-behavioral-representational/)
44. **[Probing Across Time: What Does RoBERTa Know and When?](https://md.research.elamur.ai/free/en/papers/liu-2021-probing-across-time-what-does/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Leo Z. Liu, Yizhong Wang, Jungo Kasai, et al.*2021 · 96 citations*
45. **[Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/qi-2023-cross-lingual-consistency-factual-knowledge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jirui Qi, Raquel Fernández, Arianna Bisazza*2023 · 90 citations*
46. **[Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://md.research.elamur.ai/free/en/papers/rame-2023-rewarded-soups-towards-pareto-optimal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.*2023 · 90 citations*
47. [**Deep Reinforcement Learning at the Edge of the Statistical Precipice arXiv:2108.13264**Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, et al.*2021 · 90 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2021-deep-reinforcement-learning-edge-statistical/)
48. **[LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://md.research.elamur.ai/free/en/papers/hashemi-2024-llm-rubric-multidimensional-calibrated-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Helia Hashemi, Jason Eisner, Corby Rosset, et al.*2024 · 88 citations*
49. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
50. **[The Geometry of Multilingual Language Model Representations](https://md.research.elamur.ai/free/en/papers/chang-2022-geometry-multilingual-language-model-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tyler A. Chang, Zhuowen Tu, Benjamin K. Bergen*2022 · 84 citations*
51. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.*2023 · 81 citations*
52. **[ReFT: Representation Finetuning for Language Models](https://md.research.elamur.ai/free/en/papers/wu-2024-reft-representation-finetuning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Aryaman Arora, Zheng Wang, et al.*2024 · 74 citations*
53. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
54. [**Rho-1: Not All Tokens Are What You Need arXiv:2404.07965**Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/)
55. [**A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models arXiv:2407.02646**Daking Rai, Yilun Zhou, Shi Feng, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/rai-2024-practical-review-mechanistic-interpretability-transformer/)
56. **[What Do Self-Supervised Speech Models Know About Words?](https://md.research.elamur.ai/free/en/papers/pasad-2024-what-do-self-supervised-speech/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ankita Pasad, Chung-Ming Chien, Shane Settle, et al.*2024 · 51 citations*
57. **[Deep Model Fusion: A Survey](https://md.research.elamur.ai/free/en/papers/li-2023-deep-model-fusion-survey/) [IEEE Transactions on Neural Networks and Learning Systems](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-neural-networks-and-learning-systems/)**Weishi Li, Yong Peng, Miao Zhang, et al.*2023 · 49 citations*
58. [**Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis**Mario Giulianelli, Iris Luden, Raquel Fernandez, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2023-definition-generation-senses/)
59. **[CausalGym: Benchmarking causal interpretability methods on linguistic tasks](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aryaman Arora, Dan Jurafsky, Christopher Potts*2024 · 38 citations*
60. **[Neural Lattice Language Models](https://md.research.elamur.ai/free/en/papers/buckman-2018-neural-lattice-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jacob Buckman, Graham Neubig*2018 · 35 citations*
61. [**The Life Cycle of Knowledge in Big Language Models: A Survey arXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2023 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-life-cycle-knowledge-big-language/)
62. **[How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure](https://md.research.elamur.ai/free/en/papers/wilson-2023-how-abstract-is-linguistic-generalization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Michael Wilson, Jackson Petty, Robert Frank*2023 · 29 citations*
63. [**Soft Merging of Experts with Adaptive Routing arXiv:2306.03745**Mohammed Muqeeth, Haokun Liu, Colin Raffel*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/muqeeth-2023-soft-merging-experts-adaptive-routing/)
64. [**Language Models as Models of Language arXiv:2408.07144**Raphael Milliere*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/milliere-2024-language-models-models-language/)
65. **[Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language Model](https://md.research.elamur.ai/free/en/papers/chintam-2023-identifying-adapting-transformer-components-responsible/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Abhijith Chintam, Rahel Beloch, Willem Zuidema, et al.*2023 · 19 citations*
66. **[Naturalistic Causal Probing for Morpho-Syntax](https://md.research.elamur.ai/free/en/papers/amini-2023-naturalistic-causal-probing-morpho-syntax/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Afra Amini, Tiago Pimentel, Clara Meister, et al.*2023 · 19 citations*
67. [**A Distributional and Orthographic Aggregation Model for English Derivational Morphology**Daniel Deutsch, John Hewitt, Dan Roth*2018 · 19 citations*](https://md.research.elamur.ai/free/en/papers/deutsch-2018-distributional-orthographic-aggregation-model-english/)
68. [**Assessing the Cross-linguistic Utility of Abstract Meaning Representation**Shira Wein, Nathan Schneider*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/wein-2024-assessing-cross-linguistic-utility-abstract/)
69. [**Can Large Reasoning Models Self-Train? arXiv:2505.21444**Sheikh Shafayat, Fahim Tajwar, Ruslan Salakhutdinov, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/shafayat-2025-large-reasoning-models-self-train/)
70. [**Mastering Board Games by External and Internal Planning with Language Models arXiv:2412.12119**John Schultz, Jakub Adamek, Matej Jusup, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/schultz-2024-mastering-board-games-external-internal/)
71. **[Natural Language Processing RELIES on Linguistics](https://md.research.elamur.ai/free/en/papers/opitz-2025-natural-language-processing-relies-linguistics/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Juri Opitz, Shira Wein, Nathan Schneider*2025 · 13 citations*
72. **[From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency](https://md.research.elamur.ai/free/en/papers/ohmer-2024-form-s-meaning-probing-semantic/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Xenia Ohmer, Elia Bruni, Dieuwke Hupkes*2024 · 11 citations*
73. **[Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases](https://md.research.elamur.ai/free/en/papers/hu-2025-between-circuits-chomsky-pre-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michael Y. Hu, Jackson Petty, Chuan Shi, et al.*2025 · 9 citations*
74. **[Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons](https://md.research.elamur.ai/free/en/papers/leng-2025-understanding-multi-task-learning-generalization/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yongqi Leng, Deyi Xiong*2025 · 9 citations*
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78. [**Fast and Accurate Non-Projective Dependency Tree Linearization**Xiang Yu, Simon Tannert, Ngoc Thang Vu, et al.*2020 · 7 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-fast-accurate-non-projective-dependency/)
79. [**A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration**Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, et al.*2020 · 6 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2020-novel-challenge-set-hebrew-morphological/)
80. [**HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment**Anssi Yli-Jyrä, Josi Purhonen, Matti Liljeqvist, et al.*2020 · 6 citations*](https://md.research.elamur.ai/free/en/papers/jyra-2020-helfi-hebrew-greek-finnish-parallel/)
81. [**Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning**Xiang Yu, Ngoc Thang Vu, Jonas Kuhn*2018 · 5 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-approximate-dynamic-oracle-dependency-parsing/)
82. [**Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation**Shira Wein, Zhuxin Wang, Nathan Schneider*2023 · 4 citations*](https://md.research.elamur.ai/free/en/papers/wein-2023-measuring-fine-grained-semantic-equivalence/)
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86. **[Investigating More Explainable and Partition-Free Compositionality Estimation for LLMs: A Rule-Generation Perspective](https://md.research.elamur.ai/free/en/papers/xu-2026-investigating-more-explainable-partition-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyao Xu, Cong Wang, Houfeng Wang*2026*
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88. [**Recursive LSTM Tree Representation for Arc-Standard Transition-Based Dependency Parsing**Mohab Elkaref, Bernd Bohnet*2019*](https://md.research.elamur.ai/free/en/papers/elkaref-2019-recursive-lstm-tree-representation-arc/)

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