---
title: "Who cites Erkut Erdem — page 3 — 1,278 papers of our library"
description: "1,278 papers of the Elamur Research library cite the works of Erkut Erdem, found in their bibliographies · page 3 of 4 · 201–300; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Erkut Erdem](https://md.research.elamur.ai/free/en/authors/erkut-erdem/) / [cited by](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/) / page 3

# Who cites Erkut Erdem

1,279 citations of their works from 1,278 papers of our library (of the 149,439 whose bibliographies we read) · 386 with a page here, the most cited first · page 3 of 4 · 201–300 · [← Erkut Erdem](https://md.research.elamur.ai/free/en/authors/erkut-erdem/)

[← prev](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/4/)

201. [**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/)
202. [**Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks arXiv:2305.14201**Tiedong Liu, Bryan Kian Hsiang Low*2023 · 65 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-goat-fine-tuned-llama-outperforms/)
203. **[Why think step by step? Reasoning emerges from the locality of experience](https://md.research.elamur.ai/free/en/papers/prystawski-2023-why-think-step-step-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ben Prystawski, Michael Y. Li, Noah D. Goodman*2023 · 65 citations*
204. **[A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, et al.*2024 · 64 citations*
205. [**CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility arXiv:2307.09705**Guohai Xu, Jiayi Liu, Ming Yan, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-cvalues-measuring-values-chinese/)
206. **[Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://md.research.elamur.ai/free/en/papers/li-2024-leveraging-large-language-models-nlg/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Li, Xiaohan Xu, Tao Shen, et al.*2024 · 63 citations*
207. [**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/)
208. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/)
209. [**LAB-Bench: Measuring Capabilities of Language Models for Biology Research arXiv:2407.10362**Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/laurent-2024-lab-bench-measuring-capabilities/)
210. [**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/)
211. [**Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves arXiv:2311.04205**Yihe Deng, Weitong Zhang, Zixiang Chen, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-rephrase-respond-let-large-language/)
212. **[SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://md.research.elamur.ai/free/en/papers/ye-2023-satlm-satisfiability-aided-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Qiaochu Chen, Isil Dillig, et al.*2023 · 62 citations*
213. [**Teaching Large Language Models to Reason with Reinforcement Learning arXiv:2403.04642**Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-teaching-large-language-models-reason/)
214. [**Large Language Models Are Not Strong Abstract Reasoners arXiv:2305.19555**Gael Gendron, Qiming Bao, Michael Witbrock, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/gendron-2023-large-language-models-strong-abstract/)
215. **[BIG-Bench Extra Hard](https://md.research.elamur.ai/free/en/papers/kazemi-2025-big-bench-extra-hard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.*2025 · 58 citations*
216. **[SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs](https://md.research.elamur.ai/free/en/papers/xu-2025-softcot-soft-chain-thought-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yige Xu, Xu Guo, Zhiwei Zeng, et al.*2025 · 58 citations*
217. [**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/)
218. [**SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark arXiv:2307.15020**Liang Xu, Anqi Li, Lei Zhu, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-superclue-comprehensive-chinese-large/)
219. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/)
220. **[The Art of Saying No: Contextual Noncompliance in Language Models](https://md.research.elamur.ai/free/en/papers/brahman-2024-art-saying-no-contextual-noncompliance/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.*2024 · 56 citations*
221. **[A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains](https://md.research.elamur.ai/free/en/papers/jacovi-2024-chain-thought-is-as-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.*2024 · 54 citations*
222. [**CMB: A Comprehensive Medical Benchmark in Chinese arXiv:2308.08833**Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cmb-comprehensive-medical-benchmark/)
223. [**English Machine Reading Comprehension Datasets: A Survey**Daria Dzendzik, Jennifer Foster, Carl Vogel*2021 · 54 citations*](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
224. [**Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? arXiv:2406.13121**Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-long-context-language-models-subsume/)
225. [**RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style arXiv:2410.16184**Yantao Liu, Zijun Yao, Rui Min, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/)
226. [**Language acquisition: do children and language models follow similar learning stages? arXiv:2306.03586**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
227. [**On the application of Large Language Models for language teaching and assessment technology arXiv:2307.08393**Andrew Caines, Luca Benedetto, Shiva Taslimipoor, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/caines-2023-application-large-language-models-language/)
228. [**How to Train Data-Efficient LLMs arXiv:2402.09668**Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/sachdeva-2024-train-data-efficient-llms/)
229. **[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hamish Ivison, Yizhong Wang, Jiacheng Liu, et al.*2024 · 50 citations*
230. **[The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation](https://md.research.elamur.ai/free/en/papers/pavlovic-2024-effectiveness-llms-annotators-comparative-overview/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Maja Pavlovic, Massimo Poesio*2024 · 49 citations*
231. [**Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models arXiv:2406.02061**Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, et al.*2024 · 48 citations*](https://md.research.elamur.ai/free/en/papers/nezhurina-2024-alice-wonderland-simple-tasks-showing/)
232. [**Linguistic ambiguity analysis in ChatGPT arXiv:2302.06426**Miguel Ortega-Martin, Oscar Garcia-Sierra, Alfonso Ardoiz, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/ortega-martin-2023-linguistic-ambiguity-analysis-chatgpt/)
233. **[Metaphor Understanding Challenge Dataset for LLMs](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*
234. [**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/)
235. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
236. [**GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements arXiv:2402.10963**Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/)
237. [**Lost in Translation: Large Language Models in Non-English Content Analysis arXiv:2306.07377**Gabriel Nicholas, Aliya Bhatia*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/nicholas-2023-lost-translation-large-language-models/)
238. [**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/)
239. [**Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery arXiv:2304.13714**Debadutta Dash, Rahul Thapa, Juan M. Banda, et al.*2023 · 44 citations*](https://md.research.elamur.ai/free/en/papers/dash-2023-evaluation-gpt-3-5-gpt/)
240. [**On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks arXiv:2402.08115**Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/stechly-2024-self-verification-limitations-large-language/)
241. [**Transcending Scaling Laws with 0.1% Extra Compute arXiv:2210.11399**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
242. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
243. [**Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration arXiv:2310.00280**Qiushi Sun, Zhangyue Yin, Xiang Li, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-corex-pushing-boundaries-complex-reasoning/)
244. [**GPT Can Solve Mathematical Problems Without a Calculator arXiv:2309.03241**Zhen Yang, Ming Ding, Qingsong Lv, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-gpt-solve-mathematical-problems-calculator/)
245. [**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/)
246. **[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*
247. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
248. [**Aligning Language Models with Preferences through f-divergence Minimization arXiv:2302.08215**Dongyoung Go, Tomasz Korbak, German Kruszewski, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/)
249. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
250. **[Aligning Large Language Models with Human Preferences through Representation Engineering](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-large-language-models-human/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhao Liu, Xiaohua Wang, Muling Wu, et al.*2024 · 37 citations*
251. **[CITE: A Corpus of Image-Text Discourse Relations](https://md.research.elamur.ai/free/en/papers/alikhani-2019-cite-corpus-image-text-discourse/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Malihe Alikhani, Sreyasi Nag Chowdhury, Gerard de Melo, et al.*2019 · 37 citations*
252. **[Measuring AI Ability to Complete Long Software Tasks](https://md.research.elamur.ai/free/en/papers/kwa-2025-measuring-ai-ability-complete/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Thomas Kwa, Ben West, Joel Becker, et al.*2025 · 36 citations*
253. [**The Surprising Effectiveness of Test-Time Training for Few-Shot Learning arXiv:2411.07279**Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2024-surprising-effectiveness-test-time-training/)
254. **[AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions](https://md.research.elamur.ai/free/en/papers/kirichenko-2025-abstentionbench-reasoning-llms-fail-unanswerable/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, et al.*2025 · 35 citations*
255. **[LAiW: A Chinese Legal Large Language Models Benchmark](https://md.research.elamur.ai/free/en/papers/dai-2025-laiw-chinese-legal-large-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yongfu Dai, Duanyu Feng, Jimin Huang, et al.*2025 · 35 citations*
256. [**Through the Lens of Core Competency: Survey on Evaluation of Large Language Models arXiv:2308.07902**Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-through-lens-core-competency-survey/)
257. [**Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models arXiv:2411.07140**Yancheng He, Shilong Li, Jiaheng Liu, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-chinese-simpleqa-chinese-factuality/)
258. **[ChessGPT: Bridging Policy Learning and Language Modeling](https://md.research.elamur.ai/free/en/papers/feng-2023-chessgpt-bridging-policy-learning-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xidong Feng, Yicheng Luo, Ziyan Wang, et al.*2023 · 34 citations*
259. [**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/)
260. [**Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation arXiv:2306.05783**Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, et al.*2023 · 34 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-xiezhi-ever-updating-benchmark/)
261. [**SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety arXiv:2404.05399**Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2024-safetyprompts-systematic-review-open-datasets/)
262. [**BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models arXiv:2309.06085**Wei Qi Leong, Jian Gang Ngui, Yosephine Susanto, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/leong-2023-bhasa-holistic-southeast-asian-linguistic/)
263. [**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/)
264. **[Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/wang-2025-benchmark-self-evolving-multi-agent/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Siyuan Wang, Zhuohan Long, Zhihao Fan, et al.*2025 · 30 citations*
265. [**DsDm: Model-Aware Dataset Selection with Datamodels arXiv:2401.12926**Logan Engstrom, Axel Feldmann, Aleksander Madry*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/engstrom-2024-dsdm-model-aware-dataset-selection/)
266. [**The Quantization Model of Neural Scaling arXiv:2303.13506**Eric J. Michaud, Ziming Liu, Uzay Girit, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-neural-scaling/)
267. **[Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling](https://md.research.elamur.ai/free/en/papers/wan-2025-reasoning-aware-self-consistency-leveraging/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guangya Wan, Yuqi Wu, Jie Chen, et al.*2025 · 29 citations*
268. [**One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support arXiv:2306.09237**Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, et al.*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/stern-2023-one-law-many-languages-benchmarking/)
269. **[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*
270. **[The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents](https://md.research.elamur.ai/free/en/papers/jia-2025-task-shield-enforcing-task-alignment/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Feiran Jia, Tong Wu, Xin Qin, et al.*2025 · 27 citations*
271. **[Towards the Law of Capacity Gap in Distilling Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-law-capacity-gap-distilling-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Zhang, Qiuchi Li, Dawei Song, et al.*2025 · 27 citations*
272. **[MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/das-2024-mathsensei-tool-augmented-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Debrup Das, Debopriyo Banerjee, Somak Aditya, et al.*2024 · 27 citations*
273. **[Prediction of Arabic Legal Rulings using Large Language Models](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/) [Electronics](https://md.research.elamur.ai/free/en/venues/electronics/)**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*
274. [**Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models arXiv:2310.00836**Man Luo, Shrinidhi Kumbhar, Ming shen, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-towards-logiglue-brief-survey-benchmark/)
275. [**Feedback Loops With Language Models Drive In-Context Reward Hacking arXiv:2402.06627**Alexander Pan, Erik Jones, Meena Jagadeesan, et al.*2024 · 26 citations*](https://md.research.elamur.ai/free/en/papers/pan-2024-feedback-loops-language-models-drive/)
276. **[Construction Grammar Provides Unique Insight into Neural Language Models](https://md.research.elamur.ai/free/en/papers/weissweiler-2023-construction-grammar-provides-unique-insight/) [International Workshop on Construction Grammars and NLP](https://md.research.elamur.ai/free/en/venues/cxgsnlp/)**Leonie Weissweiler, Taiqi He, Naoki Otani, et al.*2023 · 26 citations*
277. [**Logical Reasoning in Large Language Models: A Survey arXiv:2502.09100**Hanmeng Liu, Zhizhang Fu, Mengru Ding, et al.*2025 · 25 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-logical-reasoning-large-language-models/)
278. **[IberoBench: A Benchmark for LLM Evaluation in Iberian Languages](https://md.research.elamur.ai/free/en/papers/baucells-2025-iberobench-benchmark-llm-evaluation-iberian/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, et al.*2025 · 24 citations*
279. [**MiMo-Audio: Audio Language Models are Few-Shot Learners arXiv:2512.23808**Xiaomi LLM-Core Team, Dong Zhang, Gang Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-llm-mimo-audio-audio-language/)
280. [**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/)
281. **[Symbolic Regression with a Learned Concept Library](https://md.research.elamur.ai/free/en/papers/grayeli-2024-symbolic-regression-learned-concept-library/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, et al.*2024 · 23 citations*
282. [**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/)
283. **[Joint Prompt Optimization of Stacked LLMs using Variational Inference](https://md.research.elamur.ai/free/en/papers/sordoni-2023-joint-prompt-optimization-stacked-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Cote, et al.*2023 · 23 citations*
284. [**CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction arXiv:2502.07316**Junlong Li, Daya Guo, Dejian Yang, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-codei-o-condensing-reasoning/)
285. [**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/)
286. [**Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning arXiv:2310.03249**Mohamed Aghzal, Erion Plaku, Ziyu Yao*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/aghzal-2023-large-language-models-good-path/)
287. **[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*
288. **[MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2025-magicore-multi-agent-iterative-coarse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Justin Chen, Archiki Prasad, Swarnadeep Saha, et al.*2025 · 20 citations*
289. [**The Art of Scaling Reinforcement Learning Compute for LLMs arXiv:2510.13786**Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.*2025 · 20 citations*](https://md.research.elamur.ai/free/en/papers/khatri-2025-art-scaling-reinforcement-learning-compute/)
290. [**The Vector Grounding Problem arXiv:2304.01481**Dimitri Coelho Mollo, Raphael Milliere*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/mollo-2023-vector-grounding-problem/)
291. [**How predictable is language model benchmark performance? arXiv:2401.04757**David Owen*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/owen-2024-predictable-language-model-benchmark-performance/)
292. **[Ecologically Valid Explanations for Label Variation in NLI](https://md.research.elamur.ai/free/en/papers/jiang-2023-ecologically-valid-explanations-label-variation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe*2023 · 19 citations*
293. **[RECKONING: Reasoning through Dynamic Knowledge Encoding](https://md.research.elamur.ai/free/en/papers/chen-2023-reckoning-reasoning-through-dynamic-knowledge/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zeming Chen, Gail Weiss, Eric Mitchell, et al.*2023 · 19 citations*
294. [**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/)
295. [**Aviary: training language agents on challenging scientific tasks arXiv:2412.21154**Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2024-aviary-training-language-agents/)
296. [**Large Language Model Programs arXiv:2305.05364**Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/schlag-2023-large-language-model-programs/)
297. [**Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs arXiv:2507.09477**Yangning Li, Weizhi Zhang, Yuyao Yang, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-towards-agentic-rag-deep-reasoning/)
298. **[ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events](https://md.research.elamur.ai/free/en/papers/islakoglu-2025-chronosense-exploring-temporal-understanding-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Duygu Sezen Islakoglu, Jan-Christoph Kalo*2025 · 16 citations*
299. **[Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges](https://md.research.elamur.ai/free/en/papers/samuel-2025-data-contamination-detection-modern-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Vinay Samuel, Yue Zhou, Henry Peng Zou*2025 · 16 citations*
300. **[The "LLM World of Words" English free association norms generated by large language models](https://md.research.elamur.ai/free/en/papers/abramski-2024-llm-world-words-english-free/) [Scientific Data](https://md.research.elamur.ai/free/en/venues/scientific-data/)**Katherine Abramski, Riccardo Improta, Giulio Rossetti, et al.*2024 · 16 citations*

[← prev](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/authors/erkut-erdem/cited-by/page/4/)

- [← back to](https://md.research.elamur.ai/free/en/authors/erkut-erdem/) — Erkut Erdem