---
title: "Who cites Adam Tauman Kalai — page 2"
description: "1931 papers of the Elamur Research library cite the works of Adam Tauman Kalai, found in their bibliographies · page 2 of 2 · 101–140; the most cited…"
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# Who cites Adam Tauman Kalai

1,943 citations of their works from 1,931 papers of our library (of the 140,884 whose bibliographies we read) · 140 with a page here, the most cited first · page 2 of 2 · 101–140 · [← Adam Tauman Kalai](https://md.research.elamur.ai/free/en/authors/adam-tauman-kalai/)

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

101. [**DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition**Z. Z. Ren, Zhihong Shao, Junxiao Song, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/ren-2025-deepseek-prover-v2-advancing-formal/)
102. [**Fine-tuning Language Models for Factuality**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
103. [**L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning**Pranjal Aggarwal, Sean Welleck*2025 · 119 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-l1-controlling-long-reasoning-model/)
104. [**Intrinsic Bias Metrics Do Not Correlate with Application Bias**Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, et al.*2021 · 116 citations*](https://md.research.elamur.ai/free/en/papers/tarrant-2021-intrinsic-bias-metrics-do-not/)
105. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/)
106. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
107. [**Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs**Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh, et al.*2025 · 110 citations*](https://md.research.elamur.ai/free/en/papers/gandhi-2025-cognitive-behaviors-that-enable-self/)
108. [**ExpertQA: Expert-Curated Questions and Attributed Answers**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/)
109. [**TokenSkip: Controllable Chain-of-Thought Compression in LLMs**Heming Xia, Chak Tou Leong, Wenjie Wang, et al.*2025 · 105 citations*](https://md.research.elamur.ai/free/en/papers/xia-2025-tokenskip-controllable-chain-thought-compression/)
110. [**UI-TARS: Pioneering Automated GUI Interaction with Native Agents**Yujia Qin, Yining Ye, Junjie Fang, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/qin-2025-ui-tars-pioneering-automated/)
111. [**WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models**Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz*2022 · 90 citations*](https://md.research.elamur.ai/free/en/papers/minixhofer-2022-wechsel-effective-initialization-subword-embeddings/)
112. [**Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/)
113. [**Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle***Yang Trista Cao, Hal Daumé III*2021 · 71 citations*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/)
114. [**VALUE: Understanding Dialect Disparity in NLU**Caleb Ziems, Jiaao Chen, Camille Harris, et al.*2022 · 65 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2022-value-understanding-dialect-disparity-nlu/)
115. [**The Moral Foundations Reddit Corpus**Jackson P. Trager, Alireza S. Ziabari, Elnaz Rahmati, et al.*2026 · 22 citations*](https://md.research.elamur.ai/free/en/papers/trager-2026-moral-foundations-reddit-corpus/)
116. [**The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses**Bashar Alhafni, Nizar Habash, Houda Bouamor*2022 · 21 citations*](https://md.research.elamur.ai/free/en/papers/alhafni-2022-arabic-parallel-gender-corpus-2/)
117. [**New Trends for Modern Machine Translation with Large Reasoning Models**Sinuo Liu, Chenyang Lyu, Minghao Wu, et al.*2026 · 16 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-new-trends-modern-machine-translation/)
118. [**Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language**Xi Chen, Shuo Wang*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-pragmatic-inference-chain-pic-improving/)
119. [**The User-Aware Arabic Gender Rewriter**Bashar Alhafni, Ossama Obeid, Nizar Habash*2023 · 5 citations*](https://md.research.elamur.ai/free/en/papers/alhafni-2023-user-aware-arabic-gender-rewriter/)
120. [**ARB: A Comprehensive Arabic Multimodal Reasoning Benchmark**Sara Ghaboura, Shubham Patle, Ketan More, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/ghaboura-2026-arb-comprehensive-arabic-multimodal-reasoning/)
121. [**Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures**Chiara Manna, Hosein Mohebbi, Afra Alishahi, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/manna-2026-gender-disambiguation-machine-translation-diagnostic/)
122. [**Roadblocks in Gender Bias Measurement for Diachronic Corpora**Saied Alshahrani, Esma Wali, Abdullah R Alshamsan, et al.*2022 · 2 citations*](https://md.research.elamur.ai/free/en/papers/alshahrani-2022-roadblocks-gender-bias-measurement-diachronic/)
123. [**Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation Task**Dawson Petersen, Abhishek Purushothama, Nathan Schneider*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/petersen-2026-sense-sensitivity-reasoning-models-are/)
124. [**Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages**Nick McKenna, Xinnuo Xu, Jack Williams, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mckenna-2026-synthetic-function-demonstrations-improve-generation/)
125. [**Uncertainty Quantification for Evaluating Gender Bias in Machine Translation**Ieva Staliunaite, Julius Cheng, Andreas Vlachos*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/staliunaite-2026-uncertainty-quantification-evaluating-gender-bias/)
126. [**A framework for analyzing concept representations in neural models**Burin Naowarat, Hao Tang, Sharon Goldwater*2026*](https://md.research.elamur.ai/free/en/papers/naowarat-2026-framework-analyzing-concept-representations-neural/)
127. [**Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety**Paolo Di Natale, Elena Chiocchetti, Marlies Alber, et al.*2026*](https://md.research.elamur.ai/free/en/papers/natale-2026-beyond-simple-term-injection-reasoning/)
128. [**CANVAS: A Multimodal Dataset of Chinese Textbook Images for Bias and Representation Analysis**Haotian Zhu, Kefan Yu, Min Li*2026*](https://md.research.elamur.ai/free/en/papers/zhu-2026-canvas-multimodal-dataset-chinese-textbook/)
129. [**Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark**Yu Wu, Ke Shu, Jonas Fischer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wu-2026-detecting-latin-historical-books-large/)
130. [**Extending Creativity: Large Language Models and the Practice of Poetry Translation**Natalia Resende, James Hadley*2026*](https://md.research.elamur.ai/free/en/papers/resende-2026-extending-creativity-large-language-models/)
131. [**How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment**Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thaler-2026-how-far-can-bias-go/)
132. [**Investigating How LLMs Propagate Female Stereotypes: Comparing What Models Say via Prompts with What They Represent in Their Embeddings**Andrea Valderrey Nuñez, Jelke Bloem*2026*](https://md.research.elamur.ai/free/en/papers/nunez-2026-investigating-how-llms-propagate-female/)
133. [**mSCoRe: A Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning**Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen*2026*](https://md.research.elamur.ai/free/en/papers/ngo-2026-mscore-multilingual-scalable-benchmark-skill/)
134. [**Multimodal LLMs Do Not Compose Skills Optimally across Modalities**Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune*2026*](https://md.research.elamur.ai/free/en/papers/ontalvilla-2026-multimodal-llms-do-not-compose/)
135. [**NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language Inference**Matheus Westhelle, Viviane Moreira*2026*](https://md.research.elamur.ai/free/en/papers/westhelle-2026-negnli-br-brazilian-portuguese-benchmark/)
136. [**Rating-based property axes: Agentivity and split intransitivity**Eva Neu, Brian Dillon, Katrin Erk*2026*](https://md.research.elamur.ai/free/en/papers/neu-2026-rating-based-property-axes-agentivity/)
137. [**Rethinking Role-Playing Evaluation: Anonymous Benchmarking and A Systematic Study of Personality Effects**Ji-Lun Peng, Yun-Nung Chen*2026*](https://md.research.elamur.ai/free/en/papers/peng-2026-rethinking-role-playing-evaluation-anonymous/)
138. [**SciCiteVal: A Multi-Domain Dataset for Scientific Citation Verification**Qinyue Liu, Yongxin Zhou, Cyril Labbe*2026*](https://md.research.elamur.ai/free/en/papers/liu-2026-sciciteval-multi-domain-dataset-scientific/)
139. [**Towards Reliable AI Fairness: Challenges in Steering Features within Bias-Implicated Neurons**Ismael Garrido-Munoz, Arturo Montejo-Raez, Fernando Martínez-Santiago*2026*](https://md.research.elamur.ai/free/en/papers/munoz-2026-reliable-ai-fairness-challenges-steering/)
140. [**Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation**Shenbin Qian, Yves Scherrer*2026*](https://md.research.elamur.ai/free/en/papers/qian-2026-why-do-large-language-models/)

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