---
title: "Who cites Hector J. Levesque — page 2 — 757 citing papers"
description: "757 papers of the Elamur Research library cite the works of Hector J. Among them: Debiasing Pre-trained Contextualised Embeddings…"
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# Who cites Hector J. Levesque

757 citations of their works from 757 papers of our library (of the 149,439 whose bibliographies we read) · 206 with a page here, the most cited first · page 2 of 3 · 101–200 · [← Hector J. Levesque](https://md.research.elamur.ai/free/en/authors/hector-j-levesque/)

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101. **[Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masahiro Kaneko, Danushka Bollegala*2021 · 175 citations*
102. **[Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation](https://md.research.elamur.ai/free/en/papers/wang-2019-does-it-make-sense-why/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cunxiang Wang, Shuailong Liang, Yue Zhang, et al.*2019 · 174 citations*
103. **[BabyBERTa: Learning More Grammar With Small-Scale Child-Directed Language](https://md.research.elamur.ai/free/en/papers/huebner-2021-babyberta-learning-more-grammar-small/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Philip A. Huebner, Elior Sulem, Fisher Cynthia, et al.*2021 · 164 citations*
104. **[SemEval-2020 Task 4: Commonsense Validation and Explanation](https://md.research.elamur.ai/free/en/papers/wang-2020-semeval-2020-task-4-commonsense/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Cunxiang Wang, Shuailong Liang, Yili Jin, et al.*2020 · 158 citations*
105. **[INFOTABS: Inference on Tables as Semi-structured Data](https://md.research.elamur.ai/free/en/papers/gupta-2020-infotabs-inference-tables-as-semi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, et al.*2020 · 150 citations*
106. **[Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/dou-2019-investigating-meta-learning-algorithms-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos*2019 · 150 citations*
107. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing arXiv:2406.08464**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 148 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/)
108. **[Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?](https://md.research.elamur.ai/free/en/papers/pham-2021-out-order-how-important-is/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Thang Pham, Trung Bui, Long Mai, et al.*2021 · 148 citations*
109. [**Efficient Training of Language Models to Fill in the Middle arXiv:2207.14255**Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
110. **[Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://md.research.elamur.ai/free/en/papers/lv-2024-full-parameter-fine-tuning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Lv, Yuqing Yang, Tengxiao Liu, et al.*2024 · 143 citations*
111. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches arXiv:1904.01172**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 135 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
112. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models arXiv:2308.08493**Shahriar Golchin, Mihai Surdeanu*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
113. **[Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cao-2020-gender-inclusive-coreference-resolution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Trista Cao, Hal Daumé III*2020 · 127 citations*
114. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
115. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
116. **[I-BERT: Integer-only BERT Quantization](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Sehoon Kim, Amir Gholami, Zhewei Yao, et al.*2021 · 122 citations*
117. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
118. **[A Surprisingly Robust Trick for the Winograd Schema Challenge](https://md.research.elamur.ai/free/en/papers/kocijan-2019-surprisingly-robust-trick-winograd-schema/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, et al.*2019 · 121 citations*
119. **[Solving Hard Coreference Problems](https://md.research.elamur.ai/free/en/papers/peng-2015-solving-hard-coreference-problems/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Haoruo Peng, Daniel Khashabi, Dan Roth*2015 · 118 citations*
120. **[Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://md.research.elamur.ai/free/en/papers/yoo-2022-ground-truth-labels-matter-deeper/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.*2022 · 114 citations*
121. **[Testing the Ability of Language Models to Interpret Figurative Language](https://md.research.elamur.ai/free/en/papers/liu-2022-testing-ability-language-models-interpret/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Emmy Liu, Chenxuan Cui, Kenneth Zheng, et al.*2022 · 114 citations*
122. **[Learning from Task Descriptions](https://md.research.elamur.ai/free/en/papers/weller-2020-learning-task-descriptions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Orion Weller, Nicholas Lourie, Matt Gardner, et al.*2020 · 112 citations*
123. [**Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework**Aaron Steven White, Pushpendre Rastogi, Kevin Duh, et al.*2017 · 110 citations*](https://md.research.elamur.ai/free/en/papers/white-2017-inference-is-everything-recasting-semantic/)
124. **[The Repair of Speech Act Misunderstandings by Abductive Inference](https://md.research.elamur.ai/free/en/papers/mcroy-1995-repair-speech-act-misunderstandings-abductive/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Susan W. McRoy, Graeme Hirst*1995 · 109 citations*
125. **[ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://md.research.elamur.ai/free/en/papers/bian-2024-chatgpt-is-knowledgeable-but-inexperienced/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ning Bian, Xianpei Han, Le Sun, et al.*2024 · 106 citations*
126. **[Learning to Retrieve In-Context Examples for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Liang Wang, Nan Yang, Furu Wei*2024 · 106 citations*
127. **[Tokenizer Choice For LLM Training: Negligible or Crucial?](https://md.research.elamur.ai/free/en/papers/ali-2024-tokenizer-choice-llm-training-negligible/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.*2024 · 104 citations*
128. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jane Pan, Tianyu Gao, Howard Chen, et al.*2023 · 102 citations*
129. **[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*
130. **[LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/parmar-2024-logicbench-systematic-evaluation-logical-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mihir Parmar, Nisarg Patel, Neeraj Varshney, et al.*2024 · 93 citations*
131. **[LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://md.research.elamur.ai/free/en/papers/dou-2024-loramoe-alleviating-world-knowledge-forgetting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Enyu Zhou, Yan Liu, et al.*2024 · 93 citations*
132. **[Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation](https://md.research.elamur.ai/free/en/papers/levy-2021-collecting-large-scale-gender-bias/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shahar Levy, Koren Lazar, Gabriel Stanovsky*2021 · 82 citations*
133. **[D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 81 citations*
134. **[Mitigating Label Biases for In-context Learning](https://md.research.elamur.ai/free/en/papers/fei-2023-mitigating-label-biases-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Fei, Yifan Hou, Zeming Chen, et al.*2023 · 80 citations*
135. [**The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution**Ali Emami, Paul Trichelair, Adam Trischler, et al.*2019 · 73 citations*](https://md.research.elamur.ai/free/en/papers/emami-2019-knowref-coreference-corpus-removing-gender/)
136. **[Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yang Trista Cao, Hal Daumé III*2021 · 72 citations*
137. **[Tokenization Is More Than Compression](https://md.research.elamur.ai/free/en/papers/schmidt-2024-tokenization-is-more-than-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Craig W Schmidt, Varshini Reddy, Haoran Zhang, et al.*2024 · 70 citations*
138. **[It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/tikhonov-2021-it-s-all-heads-attention/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexey Tikhonov, Max Ryabinin*2021 · 69 citations*
139. **[AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs](https://md.research.elamur.ai/free/en/papers/mousi-2025-aradice-benchmarks-dialectal-cultural-capabilities/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Basel Mousi, Nadir Durrani, Fatema Ahmad, et al.*2025 · 68 citations*
140. [**Exploring the Benefits of Training Expert Language Models over Instruction Tuning arXiv:2302.03202**Joel Jang, Seungone Kim, Seonghyeon Ye, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/)
141. [**On Generalization in Coreference Resolution**Shubham Toshniwal, Patrick Xia, Sam Wiseman, et al.*2021 · 62 citations*](https://md.research.elamur.ai/free/en/papers/toshniwal-2021-generalization-coreference-resolution/)
142. **[Automatic Story Generation: Challenges and Attempts](https://md.research.elamur.ai/free/en/papers/alabdulkarim-2021-automatic-story-generation-challenges-attempts/) [Workshop on Narrative Understanding](https://md.research.elamur.ai/free/en/venues/nuse/)**Amal Alabdulkarim, Siyan Li, Xiangyu Peng*2021 · 61 citations*
143. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
144. [**Incremental Neural Coreference Resolution in Constant Memory**Patrick Xia, João Sedoc, Benjamin Van Durme*2020 · 55 citations*](https://md.research.elamur.ai/free/en/papers/xia-2020-incremental-neural-coreference-resolution-constant/)
145. [**FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark arXiv:2107.07498**Liang Xu, Xiaojing Lu, Chenyang Yuan, et al.*2021 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-fewclue-chinese-few-shot/)
146. **[IndoCulture: Exploring Geographically Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces](https://md.research.elamur.ai/free/en/papers/koto-2024-indoculture-exploring-geographically-influenced-cultural/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Fajri Koto, Rahmad Mahendra, Nurul Aisyah, et al.*2024 · 53 citations*
147. **[Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?](https://md.research.elamur.ai/free/en/papers/balepur-2024-artifacts-or-abduction-how-do/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger*2024 · 52 citations*
148. [**A Neural Entity Coreference Resolution Review Expert Systems with Applications, Volume 168, 15 April 2021, 114,466**Nikolaos Stylianou, Ioannis Vlahavas*2019 · 48 citations*](https://md.research.elamur.ai/free/en/papers/stylianou-2019-neural-entity-coreference-resolution-review/)
149. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
150. **[WikiCREM: A Large Unsupervised Corpus for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/kocijan-2019-wikicrem-large-unsupervised-corpus-coreference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vid Kocijan, Oana-Maria Camburu, Ana-Maria Cretu, et al.*2019 · 41 citations*
151. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations*
152. **[Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese](https://md.research.elamur.ai/free/en/papers/putri-2024-can-llm-generate-culturally-relevant/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rifki Afina Putri, Faiz Ghifari Haznitrama, Dea Adhista, et al.*2024 · 38 citations*
153. **[A Practical Nonmonotonic Theory for Reasoning about Speech Acts](https://md.research.elamur.ai/free/en/papers/appelt-1988-practical-nonmonotonic-theory-reasoning-about/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Douglas Appelt, Kurt Konolige*1988 · 37 citations*
154. [**GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning**Zilong Zheng, Shuwen Qiu, Lifeng Fan, et al.*2021 · 36 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2021-grice-grammar-based-dataset-recovering/)
155. **[Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://md.research.elamur.ai/free/en/papers/emelin-2021-wino-x-multilingual-winograd-schemas/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Denis Emelin, Rico Sennrich*2021 · 35 citations*
156. **[Sub-Character Tokenization for Chinese Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/si-2023-sub-character-tokenization-chinese-pretrained/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chenglei Si, Zhengyan Zhang, Yingfa Chen, et al.*2023 · 29 citations*
157. **[Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://md.research.elamur.ai/free/en/papers/balepur-2025-which-these-best-describes-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber*2025 · 28 citations*
158. **[New Protocols and Negative Results for Textual Entailment Data Collection](https://md.research.elamur.ai/free/en/papers/bowman-2020-new-protocols-negative-results-textual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, et al.*2020 · 27 citations*
159. [**Are Large Language Models Robust Coreference Resolvers? arXiv:2305.14489**Nghia T. Le, Alan Ritter*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/le-2023-large-language-models-robust-coreference/)
160. **[Sensitivity as a Complexity Measure for Sequence Classification Tasks](https://md.research.elamur.ai/free/en/papers/hahn-2021-sensitivity-as-complexity-measure-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Michael Hahn, Dan Jurafsky, Richard Futrell*2021 · 26 citations*
161. **[Assessing the Capabilities of Large Language Models in Coreference: An Evaluation](https://md.research.elamur.ai/free/en/papers/gan-2024-assessing-capabilities-large-language-models/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yujian Gan, Massimo Poesio, Juntao Yu*2024 · 24 citations*
162. **[AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite](https://md.research.elamur.ai/free/en/papers/groschwitz-2023-amr-parsing-is-far-solved/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonas Groschwitz, Shay B. Cohen, Lucia Donatelli, et al.*2023 · 20 citations*
163. **[Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*
164. [**Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution**Connor Baumler, Rachel Rudinger*2022 · 19 citations*](https://md.research.elamur.ai/free/en/papers/baumler-2022-recognition-they-them-as-singular/)
165. [**Orion-14B: Open-source Multilingual Large Language Models arXiv:2401.12246**Du Chen, Yi Huang, Xiaopu Li, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-orion-14b-open-source/)
166. **[Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum](https://md.research.elamur.ai/free/en/papers/pouransari-2024-dataset-decomposition-faster-llm-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, et al.*2024 · 16 citations*
167. **[Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/konno-2021-pseudo-zero-pronoun-resolution-improves/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi, et al.*2021 · 14 citations*
168. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/)
169. **[Dual Cache for Long Document Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/guo-2023-dual-cache-long-document-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qipeng Guo, Xiangkun Hu, Yue Zhang, et al.*2023 · 12 citations*
170. **[MEraser: An Effective Fingerprint Erasure Approach for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-meraser-effective-fingerprint-erasure-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingxuan Zhang, Zhenhua Xu, Rui Hu, et al.*2025 · 11 citations*
171. [**Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?**Michal Novák, Miloslav Konopik, Anna Nedoluzhko, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/novak-2025-findings-fourth-shared-task-multilingual/)
172. [**Debiasing Algorithm through Model Adaptation arXiv:2310.18913**Tomasz Limisiewicz, David Mareček, Tomaš Musil*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/limisiewicz-2023-debiasing-algorithm-through-model-adaptation/)
173. **[This One or That One? A Study on Accessibility via Demonstratives with Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-this-one-or-that-one/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yu Wang, Emmanuele Chersoni, Chu-Ren Huang*2026 · 7 citations*
174. **[Multilingual coreference resolution as text generation](https://md.research.elamur.ai/free/en/papers/skachkova-2024-multilingual-coreference-resolution-as-text/) [Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC)](https://md.research.elamur.ai/free/en/venues/crac/)**Natalia Skachkova*2024 · 6 citations*
175. [**Developing a Corpus of Indirect Speech Act Schemas**Antonio Roque, Alexander Tsuetaki, Vasanth Sarathy, et al.*2020 · 6 citations*](https://md.research.elamur.ai/free/en/papers/roque-2020-developing-corpus-indirect-speech-act/)
176. **[CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor](https://md.research.elamur.ai/free/en/papers/xu-2025-ctcc-robust-stealthy-fingerprinting-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhenhua Xu, Xixiang Zhao, Xubin Yue, et al.*2025 · 4 citations*
177. [**A Benchmark Arabic Dataset for Commonsense Explanation arXiv:2012.10251**Saja AL-Tawalbeh, Mohammad AL-Smadi*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/al-tawalbeh-2020-benchmark-arabic-dataset-commonsense-explanation/)
178. [**A guide to the dataset explosion in QA, NLI, and commonsense reasoning**Anna Rogers, Anna Rumshisky*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/)
179. **[Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation](https://md.research.elamur.ai/free/en/papers/ojastu-2026-estonian-winogrande-dataset-comparative-analysis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, et al.*2026 · 3 citations*
180. **[Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference Context](https://md.research.elamur.ai/free/en/papers/bartl-2025-adapting-psycholinguistic-research-llms-gender/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Marion Bartl, Thomas Brendan Murphy, Susan Leavy*2025 · 3 citations*
181. [**PCR4ALL: A Comprehensive Evaluation Benchmark for Pronoun Coreference Resolution in English**Xinran Zhao, Hongming Zhang, Yangqiu Song*2022 · 3 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2022-pcr4all-comprehensive-evaluation-benchmark-pronoun/)
182. **[FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation](https://md.research.elamur.ai/free/en/papers/otmakhova-2026-fluke-linguistically-driven-task-agnostic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yulia Otmakhova, Thinh Hung Truong, Rahmad Mahendra, et al.*2026 · 1 citation*
183. **[Serbian SuperGLUE: Towards an Evaluation Benchmark for South Slavic Language Models](https://md.research.elamur.ai/free/en/papers/perovic-2026-serbian-superglue-evaluation-benchmark-south/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Mitar Perovic, Teodora Mihajlov*2026 · 1 citation*
184. **[The Thin Line Between Comprehension and Persuasion in LLMs](https://md.research.elamur.ai/free/en/papers/wynter-2026-thin-line-between-comprehension-persuasion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrian de Wynter, Tangming Yuan*2026 · 1 citation*
185. **[Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling Tasks](https://md.research.elamur.ai/free/en/papers/mellado-2026-diagnostic-predictive-evaluation-methodology-sequence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Elena Alvarez-Mellado, Julio Gonzalo*2026 · 1 citation*
186. [**Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine Translation arXiv:2502.19104**Michelle Kappl*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kappl-2025-all-spanish-doctors-male-evaluating/)
187. [**ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning arXiv:2602.17054**Hussein S. Al-Olimat, Ahmad Alshareef*2026*](https://md.research.elamur.ai/free/en/papers/al-olimat-2026-alps-diagnostic-challenge-set-arabic/)
188. **[Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility](https://md.research.elamur.ai/free/en/papers/palta-2026-arguments-that-alter-minds-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shramay Palta, Peter A. Rankel, Sarah Wiegreffe, et al.*2026*
189. **[Can a Remedy Find a Researcher? Exploring the Development of Semantic Knowledge in Italian BabyLMs](https://md.research.elamur.ai/free/en/papers/suozzi-2026-can-remedy-find-researcher-exploring/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Alice Suozzi, Luca Capone, Gianluca Lebani, et al.*2026*
190. [**CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models arXiv:2603.09993**Jon Chun, Hannah Sussman, Adrian Mangine, et al.*2026*](https://md.research.elamur.ai/free/en/papers/chun-2026-cei-benchmark-evaluating-pragmatic-reasoning/)
191. **[Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry](https://md.research.elamur.ai/free/en/papers/vintar-2026-charting-european-llm-benchmarking-landscape/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Spela Vintar, Mojca Brglez, Taja Kuzman Pungeršek, et al.*2026*
192. **[Compositional Meaning Representations in LLMs: a Critical Review of Probing Studies](https://md.research.elamur.ai/free/en/papers/marro-2026-compositional-meaning-representations-llms-critical/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Rémy Marro*2026*
193. **[E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral-benchmarks/) [IEEE Access](https://md.research.elamur.ai/free/en/venues/ieee-access/)**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*
194. [**E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding arXiv:2609.33530**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral/)
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196. **[Gender bias in machine translation: an evaluation on the English–Portuguese language pair (Viés de gênero na tradução automática: uma avaliação no par linguístico inglês-português)](https://md.research.elamur.ai/free/en/papers/soares-2026-vies-de-genero-na-traducao/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Tayane A. Soares, Yohan B. Gumiel, Rafael Junqueira, et al.*2026*
197. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026*
198. **[ltzGLUE: Luxembourgish General Language Understanding Evaluation](https://md.research.elamur.ai/free/en/papers/plum-2026-ltzglue-luxembourgish-general-language-understanding/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alistair Plum, Felicia Körner, Anne-Marie Lutgen, et al.*2026*
199. **[Making Synthetic Questions More Child-Directed: Prompting and Sampling Effects](https://md.research.elamur.ai/free/en/papers/poh-2026-making-synthetic-questions-more-child/) [Workshop on Computational Developmental Linguistics (CDL)](https://md.research.elamur.ai/free/en/venues/cdl/)**Whitney Poh, Michael Tombolini, Libby Barak*2026*
200. **[Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement](https://md.research.elamur.ai/free/en/papers/yan-2026-paraphrasing-as-zero-shot-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyue Yan, Hongying Zan, Xinglin Lyu, et al.*2026*

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