---
title: "Who cites Marco Marelli — page 2"
description: "1,722 papers of the Elamur Research library cite the works of Marco Marelli, found in their bibliographies · page 2 of 2 · 101–193; the most cited first."
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---
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# Who cites Marco Marelli

1,765 citations of their works from 1,722 papers of our library (of the 140,884 whose bibliographies we read) · 193 with a page here, the most cited first · page 2 of 2 · 101–193 · [← Marco Marelli](https://md.research.elamur.ai/free/en/authors/marco-marelli/)

[← prev](https://md.research.elamur.ai/free/en/authors/marco-marelli/cited-by/) [1](https://md.research.elamur.ai/free/en/authors/marco-marelli/cited-by/) **2**

101. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
102. [**How Can We Accelerate Progress Towards Human-like Linguistic Generalization?**Tal Linzen*2020 · 257 citations*](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/)
103. [**Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions**Pouya Pezeshkpour, Estevam Hruschka*2023 · 256 citations*](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/)
104. [**Generating Datasets with Pretrained Language Models**Timo Schick, Hinrich Schutze*2021 · 250 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/)
105. [**DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/)
106. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 245 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
107. [**NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark**Oscar Sainz, Jon Campos, Iker García-Ferrero, et al.*2023 · 245 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/)
108. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 237 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
109. [**On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/)
110. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
111. [**Self-Guided Contrastive Learning for BERT Sentence Representations**Taeuk Kim, Kang Min Yoo, Sang-goo Lee*2021 · 234 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-self-guided-contrastive-learning-bert/)
112. [**Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision**Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.*2023 · 232 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-principle-driven-self-alignment-language/)
113. [**FOLIO: Natural Language Reasoning with First-Order Logic**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 217 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
114. [**Probing for semantic evidence of composition by means of simple classification tasks**Allyson Ettinger, Ahmed Elgohary, Philip Resnik*2016 · 217 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2016-probing-semantic-evidence-composition-means/)
115. [**Distilling Reasoning Capabilities into Smaller Language Models**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
116. [**An Unsupervised Sentence Embedding Method by Mutual Information Maximization**Yan Zhang, Ruidan He, Zuozhu Liu, et al.*2020 · 213 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-unsupervised-sentence-embedding-method-mutual/)
117. [**GAIA: a benchmark for General AI Assistants**Gregoire Mialon, Clementine Fourrier, Craig Swift, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/)
118. [**AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors**Weize Chen, Yusheng Su, Jingwei Zuo, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-agentverse-facilitating-multi-agent/)
119. [**Reinforced Self-Training (ReST) for Language Modeling**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
120. [**PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/)
121. [**Prompting GPT-3 To Be Reliable**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
122. [**Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 202 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/)
123. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
124. [**Aligning Large Language Models with Human: A Survey**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
125. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
126. [**From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline**Tianle Li, Wei-Lin Chiang, Evan Frick, et al.*2024 · 191 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-crowdsourced-data-high-quality-benchmarks/)
127. [**One Embedder, Any Task: Instruction-Finetuned Text Embeddings**Hongjin Su, Weijia Shi, Jungo Kasai, et al.*2022 · 191 citations*](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
128. [**LawBench: Benchmarking Legal Knowledge of Large Language Models**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 184 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
129. [**On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/)
130. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
131. [**LILA: A Unified Benchmark for Mathematical Reasoning**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/)
132. [**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
133. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
134. [**Generative Representational Instruction Tuning**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 165 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
135. [**Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition**Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/)
136. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
137. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
138. [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/)
139. [**Testing the Predictions of Surprisal Theory in 11 Languages**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/)
140. [**DataComp-LM: In search of the next generation of training sets for language models**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 156 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/)
141. [**ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/)
142. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 145 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
143. [**Self-Evaluation Guided Beam Search for Reasoning**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/)
144. [**Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration**Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/)
145. [**A Roadmap to Pluralistic Alignment**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
146. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
147. [**InFoBench: Evaluating Instruction Following Ability in Large Language Models**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/)
148. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
149. [**FLUTE: Figurative Language Understanding through Textual Explanations**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/)
150. [**Faithful Logical Reasoning via Symbolic Chain-of-Thought**Jundong Xu, Hao Fei, Liangming Pan, et al.*2024 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-faithful-logical-reasoning-symbolic-chain/)
151. [**Deductive Verification of Chain-of-Thought Reasoning**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/)
152. [**ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding**Uri Shaham, Maor Ivgi, Avia Efrat, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/shaham-2023-zeroscrolls-zero-shot-benchmark-long/)
153. [**Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment**Masatoshi Tsuchiya*2018 · 125 citations*](https://md.research.elamur.ai/free/en/papers/tsuchiya-2018-performance-impact-caused-hidden-bias/)
154. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
155. [**Large Language Model Alignment: A Survey**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
156. [**EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, et al.*2019 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/)
157. [**Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 121 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/)
158. [**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/)
159. [**Evaluating the Performance of Large Language Models on GAOKAO Benchmark**Xiaotian Zhang, Chunyang Li, Yi Zong, et al.*2023 · 107 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-evaluating-performance-large-language/)
160. [**End-to-End Bias Mitigation by Modelling Biases in Corpora**Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/)
161. [**Investigating Reasons for Disagreement in Natural Language Inference**Nan-Jiang Jiang, Marie-Catherine de Marneffe*2022 · 104 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-investigating-reasons-disagreement-natural-language/)
162. [**Inverse Scaling: When Bigger Isn't Better**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
163. [**MAmmoTH2: Scaling Instructions from the Web**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/)
164. [**JGLUE: Japanese General Language Understanding Evaluation**Kentaro Kurihara, Daisuke Kawahara, Tomohide Shibata*2022 · 90 citations*](https://md.research.elamur.ai/free/en/papers/kurihara-2022-jglue-japanese-general-language-understanding/)
165. [**NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/)
166. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
167. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
168. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
169. [**Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity**Juri Opitz, Angel Daza, Anette Frank*2021 · 47 citations*](https://md.research.elamur.ai/free/en/papers/opitz-2021-weisfeiler-leman-bamboo-novel-amr/)
170. [**Transcending Scaling Laws with 0.1% Extra Compute**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 40 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
171. [**Language acquisition: do children and language models follow similar learning stages?**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
172. [**SemEval-2025 Task 1: AdMIRe - Advancing Multimodal Idiomaticity Representation**Thomas Pickard, Aline Villavicencio, Maggie Mi, et al.*2025 · 30 citations*](https://md.research.elamur.ai/free/en/papers/pickard-2025-semeval-2025-task-1-admire/)
173. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
174. [**Prediction of Arabic Legal Rulings using Large Language Models**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/)
175. [**The Quantization Model of Neural Scaling**Eric J. Michaud, Ziming Liu, Uzay Girit, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-neural-scaling/)
176. [**Do Large Language Models Understand Word Senses?**Domenico Meconi, Simone Stirpe, Federico Martelli*2025 · 18 citations*](https://md.research.elamur.ai/free/en/papers/meconi-2025-llms-word-senses/)
177. [**Construction Grammar Provides Unique Insight into Neural Language Models**Leonie Weissweiler, Taiqi He, Naoki Otani, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-construction-grammar-provides-unique-insight/)
178. [**The First Shared Task on Discourse Representation Structure Parsing**Lasha Abzianidze, Rik van Noord, Hessel Haagsma, et al.*2019 · 16 citations*](https://md.research.elamur.ai/free/en/papers/abzianidze-2019-first-shared-task-discourse-representation/)
179. [**Combining Event Semantics and Degree Semantics for Natural Language Inference**Izumi Haruta, Koji Mineshima, Daisuke Bekki*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/haruta-2020-combining-event-semantics-degree-semantics/)
180. [**Dialogue Discourse Parsing as Generation: A Sequence-to-Sequence LLM-based Approach**Chuyuan Li, Yuwei Yin, Giuseppe Carenini*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-dialogue-discourse-parsing-as-generation/)
181. [**What can we learn from Semantic Tagging?**Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, et al.*2018 · 11 citations*](https://md.research.elamur.ai/free/en/papers/abdou-2018-what-can-we-learn-semantic/)
182. [**ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection**Khloud Al Jallad, Nada Ghneim*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/jallad-2022-arnli-arabic-natural-language-inference/)
183. [**Challenging the Transformer-based models with a Classical Arabic dataset: Quran and Hadith**Shatha Altammami, Eric Atwell*2022 · 6 citations*](https://md.research.elamur.ai/free/en/papers/altammami-2022-challenging-transformer-based-models-classical/)
184. [**Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders**Yuwei Zhang, Siffi Singh, Sailik Sengupta, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-can-your-model-tell-negation/)
185. [**Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/)
186. [**E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets**Khloud AL Jallad, Nada Ghneim, Ghaida Rebdawi*2026*](https://md.research.elamur.ai/free/en/papers/jallad-2026-e-conan-entailment-contradition-neutral-benchmarks/)
187. [**EduBench: A Portuguese Benchmark for Open-Ended Discursive Question Answering**Pedro Henrique Paiola, Luís Gabriel Damiati Mendes, Bruno de Oliveira Monchelato, et al.*2026*](https://md.research.elamur.ai/free/en/papers/paiola-2026-edubench-portuguese-benchmark-open-ended/)
188. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
189. [**Persona-Aware Evaluation of Cognitive Bias in LLMs: From Benchmark to Applied Decision-Making**Katsumasa Yoshikawa, Junya Takayama, Takato Yamazaki*2026*](https://md.research.elamur.ai/free/en/papers/yoshikawa-2026-persona-aware-evaluation-cognitive-bias/)
190. [**Sentiment Analysis and Language Models for Kwanyama**Ndapa Nakashole*2026*](https://md.research.elamur.ai/free/en/papers/nakashole-2026-sentiment-analysis-language-models-kwanyama/)
191. [**ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts**Nikta Gohari Sadr, Sangmitra Madhusudan, Arash Asgari, et al.*2026*](https://md.research.elamur.ai/free/en/papers/sadr-2026-zip-quantifying-which-words-matter/)
192. [**Can generative AI recognise textual entailment? (生成 AI は含意関係認識ができるのか)**荒沢康平, 狩野芳伸*2024*](https://md.research.elamur.ai/free/en/papers/arasawa-2024-can-generative-ai-recognise/)
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