---
title: "Who cites Ishani Mondal — page 2 — 603 papers of our library"
description: "603 papers of the Elamur Research library cite the works of Ishani Mondal, found in their bibliographies · page 2 of 2 · 101–166; the most cited first."
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---
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# Who cites Ishani Mondal

603 citations of their works from 603 papers of our library (of the 149,439 whose bibliographies we read) · 166 with a page here, the most cited first · page 2 of 2 · 101–166 · [← Ishani Mondal](https://md.research.elamur.ai/free/en/authors/ishani-mondal/)

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

101. **[Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://md.research.elamur.ai/free/en/papers/beck-2024-sensitivity-performance-robustness-deconstructing-effect/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Tilman Beck, Hendrik Schuff, Anne Lauscher, et al.*2024 · 69 citations*
102. [**Learning by Distilling Context arXiv:2209.15189**Charlie Snell, Dan Klein, Ruiqi Zhong*2022 · 67 citations*](https://md.research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/)
103. [**Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models arXiv:2402.13064**Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/)
104. [**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/)
105. [**Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases arXiv:2303.14742**Yunjie Ji, Yong Deng, Yan Gong, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-exploring-impact-instruction-data/)
106. **[One-Shot Learning as Instruction Data Prospector for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-one-shot-learning-as-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.*2024 · 61 citations*
107. [**In-context Pretraining: Language Modeling Beyond Document Boundaries arXiv:2310.10638**Weijia Shi, Sewon Min, Maria Lomeli, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-context-pretraining-language-modeling-document/)
108. [**Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning arXiv:2402.00530**Ming Li, Yong Zhang, Shwai He, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/)
109. **[TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://md.research.elamur.ai/free/en/papers/kadaoui-2023-tarjamat-evaluation-bard-chatgpt-machine/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Karima Kadaoui, Samar M. Magdy, Abdul Waheed, et al.*2023 · 60 citations*
110. **[Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.*2024 · 59 citations*
111. **[ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zihan Liu, Wei Ping, Rajarshi Roy, et al.*2024 · 58 citations*
112. [**DARWIN Series: Domain Specific Large Language Models for Natural Science arXiv:2308.13565**Tong Xie, Yuwei Wan, Wei Huang, et al.*2023 · 55 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-darwin-series-domain-specific-large/)
113. [**TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks arXiv:2310.00752**Dongfu Jiang, Yishan Li, Ge Zhang, et al.*2023 · 50 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-tigerscore-towards-building-explainable-metric/)
114. **[Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-rethinking-kullback-leibler-divergence-knowledge/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Taiqiang Wu, Chaofan Tao, Jiahao Wang, et al.*2025 · 47 citations*
115. [**HyperCLOVA X Technical Report arXiv:2404.01954**Kang Min Yoo, Jaegeun Han, Sookyo In, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2024-hyperclova-x-technical-report/)
116. [**Goal Driven Discovery of Distributional Differences via Language Descriptions arXiv:2302.14233**Ruiqi Zhong, Peter Zhang, Steve Li, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2023-goal-driven-discovery-distributional-differences/)
117. [**SALMON: Self-Alignment with Instructable Reward Models arXiv:2310.05910**Zhiqing Sun, Yikang Shen, Hongxin Zhang, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-salmon-self-alignment-instructable-reward/)
118. [**The MiniPile Challenge for Data-Efficient Language Models arXiv:2304.08442**Jean Kaddour*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-minipile-challenge-data-efficient-language/)
119. **[FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://md.research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Orion Weller, Benjamin Chang, Sean MacAvaney, et al.*2025 · 40 citations*
120. **[Dual-Space Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-dual-space-knowledge-distillation-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Songming Zhang, Xue Zhang, Zengkui Sun, et al.*2024 · 39 citations*
121. [**Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing arXiv:2305.16635**Jaehun Jung, Peter West, Liwei Jiang, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/jung-2023-impossible-distillation-low-quality-model/)
122. [**M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models arXiv:2305.10263**Chuang Liu, Renren Jin, Yuqi Ren, et al.*2023 · 34 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-m3ke-massive-multi-level/)
123. **[Evaluating Language Models as Synthetic Data Generators](https://md.research.elamur.ai/free/en/papers/kim-2025-evaluating-language-models-as-synthetic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Seungone Kim, Juyoung Suk, Xiang Yue, et al.*2025 · 32 citations*
124. **[CIDAR: Culturally Relevant Instruction Dataset For Arabic](https://md.research.elamur.ai/free/en/papers/alyafeai-2024-cidar-culturally-relevant-instruction-dataset/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, et al.*2024 · 30 citations*
125. **[Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance](https://md.research.elamur.ai/free/en/papers/nahum-2025-are-llms-better-than-reported/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Omer Nahum, Nitay Calderon, Orgad Keller, et al.*2025 · 27 citations*
126. **[SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://md.research.elamur.ai/free/en/papers/wadden-2025-sciriff-resource-enhance-language-model/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**David Wadden, Kejian Shi, Jacob Morrison, et al.*2025 · 27 citations*
127. [**From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning arXiv:2304.07995**Qian Liu, Fan Zhou, Zhengbao Jiang, et al.*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-zero-hero-examining-power-symbolic/)
128. [**COLLIE: Systematic Construction of Constrained Text Generation Tasks arXiv:2307.08689**Shunyu Yao, Howard Chen, Austin W. Hanjie, et al.*2023 · 23 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-collie-systematic-construction-constrained-text/)
129. **[DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://md.research.elamur.ai/free/en/papers/zhang-2023-dialogstudio-towards-richest-most-diverse/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianguo Zhang, Kun Qian, Zhiwei Liu, et al.*2023 · 21 citations*
130. **[The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design](https://md.research.elamur.ai/free/en/papers/snegirev-2025-russian-focused-embedders-exploration-rumteb/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artem Snegirev, Maria Tikhonova, Maksimova Anna, et al.*2025 · 19 citations*
131. **[Suri: Multi-constraint Instruction Following in Long-form Text Generation](https://md.research.elamur.ai/free/en/papers/pham-2024-suri-multi-constraint-instruction-following/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chau Minh Pham, Simeng Sun, Mohit Iyyer*2024 · 19 citations*
132. [**Quality-Diversity through AI Feedback arXiv:2310.13032**Herbie Bradley, Andrew Dai, Hannah Teufel, et al.*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/bradley-2023-quality-diversity-through-ai-feedback/)
133. [**Jais 2: A Family of Arabic-Centric Open Large Language Models arXiv:2608.13580**Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim, et al.*2026 · 14 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2026-jais-2-family-arabic-centric/)
134. **[BALSAM: A Platform for Benchmarking Arabic Large Language Models](https://md.research.elamur.ai/free/en/papers/matham-2025-balsam-platform-benchmarking-arabic-large/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed, et al.*2025 · 14 citations*
135. **[EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework](https://md.research.elamur.ai/free/en/papers/shi-2025-educationq-evaluating-llms-teaching-capabilities/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yao Shi, Rongkeng Liang, Yong Xu*2025 · 14 citations*
136. **[ToDi: Token-wise Distillation via Fine-Grained Divergence Control](https://md.research.elamur.ai/free/en/papers/jung-2025-todi-token-wise-distillation-fine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seongryong Jung, Suwan Yoon, DongGeon Kim, et al.*2025 · 12 citations*
137. **[Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/yang-2025-beyond-boundaries-learning-universal-entity/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yuming Yang, Wantong Zhao, Caishuang Huang, et al.*2025 · 11 citations*
138. [**EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models arXiv:2402.11430**Jun Gao, Huan Zhao, Wei Wang, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-eventrl-enhancing-event-extraction-outcome/)
139. **[FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models](https://md.research.elamur.ai/free/en/papers/fan-2025-fedmkt-federated-mutual-knowledge-transfer/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Tao Fan, Guoqiang Ma, Yan Kang, et al.*2025 · 10 citations*
140. **[WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback](https://md.research.elamur.ai/free/en/papers/shi-2026-wildfeedback-aligning-llms-situ-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taiwei Shi, Zhuoer Wang, Longqi Yang, et al.*2026 · 9 citations*
141. [**Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps arXiv:2510.13430**Ahmed Alzubaidi, Shaikha Alsuwaidi, Basma El Amel Boussaha, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/alzubaidi-2025-evaluating-arabic-large-language-models/)
142. **[Beyond Logits: Aligning Feature Dynamics for Effective Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/gong-2025-beyond-logits-aligning-feature-dynamics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoqiang Gong, Jiaxing Wang, Jin Xu, et al.*2025 · 7 citations*
143. **[QUDSELECT: Selective Decoding for Questions Under Discussion Parsing](https://md.research.elamur.ai/free/en/papers/suvarna-2024-qudselect-selective-decoding-questions-under/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ashima Suvarna, Xiao Liu, Tanmay Parekh, et al.*2024 · 7 citations*
144. [**LLM-Oriented Token-Adaptive Knowledge Distillation arXiv:2510.11615**Xurong Xie, Zhucun Xue, Jiafu Wu, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/xie-2025-llm-oriented-token-adaptive-knowledge/)
145. **[Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs](https://md.research.elamur.ai/free/en/papers/anshumann-2025-sparse-logit-sampling-accelerating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anshumann, Mohd Abbas Zaidi, Akhil Kedia, et al.*2025 · 6 citations*
146. **[CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning](https://md.research.elamur.ai/free/en/papers/onyame-2026-cure-med-curriculum-informed-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eric Onyame, Akash Ghosh, Subhadip Baidya, et al.*2026 · 5 citations*
147. [**Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence arXiv:2503.14749**Sophia Hager; David Mueller; Kevin Duh;, Nicholas Andrews*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/hager-2025-uncertainty-distillation-teaching-language-models/)
148. [**Less is More: Selective Reflection for Compatible and Efficient Knowledge Distillation in Large Language Models arXiv:2508.06135**Lingyuan Liu, Mengxiang Zhang*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-less-more-selective-reflection-compatible/)
149. **[Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following](https://md.research.elamur.ai/free/en/papers/ren-2026-instructions-are-all-you-need/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Ren, Qianyu He, Powei Chang, et al.*2026 · 3 citations*
150. **[Multi-Granularity Semantic Revision for Large Language Model Distillation](https://md.research.elamur.ai/free/en/papers/liu-2026-multi-granularity-semantic-revision-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Liu, Yun Zhang, Wei Li, et al.*2026 · 2 citations*
151. **[PromptLab: A Collaborative Platform for Prompt Engineering and Dataset Curation](https://md.research.elamur.ai/free/en/papers/shaibani-2026-promptlab-collaborative-platform-prompt-engineering/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Maged S. Al-shaibani, Zaid Alyafeai, Dania Refai, et al.*2026 · 1 citation*
152. [**HAMSA: Hijacking Aligned Compact Models via Stealthy Automation arXiv:2508.16484**Alexey Krylov, Iskander Vagizov, Dmitrii Korzh, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/krylov-2025-hamsa-hijacking-aligned-compact-models/)
153. [**HeBID: Detecting Social Identities in Hebrew-language Political Text**Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/lan-2025-hebid-detecting-social-identities-hebrew/)
154. **[A Herd of Language Models Makes a Better Zero-shot Annotator for Clinical Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/shimizu-2026-herd-language-models-makes-better/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seiji Shimizu, Shoko Wakamiya, Eiji Aramaki*2026*
155. **[Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography](https://md.research.elamur.ai/free/en/papers/yan-2026-anchored-sliding-window-robust-imperceptible/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruiyi Yan, Shiao Meng, Yugo Murawaki*2026*
156. **[Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs](https://md.research.elamur.ai/free/en/papers/zheng-2026-are-all-prompt-components-value/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yujia Zheng, Tianhao Li, Haotian Huang, et al.*2026*
157. **[Calibrated Progressive Distillation: Co-Designing Curriculum and Target Mixing for Knowledge Distillation of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-calibrated-progressive-distillation-co-designing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mengxiang Zhang, Lingyuan Liu*2026*
158. **[CaPEdit: Capability-Preserving Lifelong Knowledge Editing For Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-capedit-capability-preserving-lifelong-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Song-Li Wu, Zhaocheng Du, Xianquan Wang, et al.*2026*
159. **[From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication](https://md.research.elamur.ai/free/en/papers/mondal-2026-fluent-useful-generative-ai-that/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ishani Mondal*2026*
160. **[Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMs](https://md.research.elamur.ai/free/en/papers/moriyama-2026-generation-instruction-preference-dataset-improving/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kei Moriyama, Takashi Kodama, Kouta Nakayama*2026*
161. [**How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline arXiv:2604.04204**Mir Tafseer Nayeem, Davood Rafiei*2026*](https://md.research.elamur.ai/free/en/papers/nayeem-2026-english-us-become-default-triangulating/)
162. **[InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*
163. **[Learning from Evolving Training Dynamics: An Entropy-Maximizing Data Curation Strategy for LLM Supervised Post-Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-learning-evolving-training-dynamics-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengxiang Zhang, Lingyuan Liu*2026*
164. **[Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-shuttle-between-symbolic-instructions-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wangtao Sun, Haotian Xu, Huanxuan Liao, et al.*2026*
165. [**The Validity of Coreference-based Evaluations of Natural Language Understanding arXiv:2602.16200**Ian Porada*2026*](https://md.research.elamur.ai/free/en/papers/porada-2026-validity-coreference-based-evaluations-natural/)
166. **[We Are What We Repeatedly Do: Improving Long Context Instruction Following](https://md.research.elamur.ai/free/en/papers/robinette-2026-we-are-what-we-repeatedly/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Preston K Robinette, Andrew Hard, Swaroop Ramaswamy, et al.*2026*

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