---
title: "Who cites Qian Huang — page 3 — 1,115 papers of our library"
description: "1,115 papers of the Elamur Research library cite the works of Qian Huang, found in their bibliographies · page 3 of 4 · 201–300; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/qian-huang/cited-by/page/3/"
---
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# Who cites Qian Huang

1,126 citations of their works from 1,115 papers of our library (of the 149,439 whose bibliographies we read) · 320 with a page here, the most cited first · page 3 of 4 · 201–300 · [← Qian Huang](https://md.research.elamur.ai/free/en/authors/qian-huang/)

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201. [**Can Language Models Solve Olympiad Programming? arXiv:2404.10952**Quan Shi, Michael Tang, Karthik Narasimhan, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-language-models-solve-olympiad-programming/)
202. **[Can Large Language Models Unlock Novel Scientific Research Ideas?](https://md.research.elamur.ai/free/en/papers/kumar-2025-can-large-language-models-unlock/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sandeep Kumar, Tirthankar Ghosal, Vinayak Goyal, et al.*2025 · 27 citations*
203. **[L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://md.research.elamur.ai/free/en/papers/ni-2024-l2ceval-evaluating-language-code-generation/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ansong Ni, Pengcheng Yin, Yilun Zhao, et al.*2024 · 27 citations*
204. **[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*
205. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations*
206. [**LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation arXiv:2401.17244**Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/chiang-2024-llamp-large-language-model-made/)
207. [**ResearchTown: Simulator of Human Research Community arXiv:2412.17767**Haofei Yu, Zhaochen Hong, Zirui Cheng, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/yu-2024-researchtown-simulator-human-research-community/)
208. [**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/)
209. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
210. **[SURVEYFORGE : On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing](https://md.research.elamur.ai/free/en/papers/yan-2025-surveyforge-outline-heuristics-memory-driven/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiangchao Yan, Shiyang Feng, Jiakang Yuan, et al.*2025 · 20 citations*
211. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
212. **[ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability](https://md.research.elamur.ai/free/en/papers/liu-2026-reasonrank-empowering-passage-ranking-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhan Liu, Xinyu Ma, Weiwei Sun, et al.*2026 · 18 citations*
213. [**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/)
214. [**Elaborative Simplification as Implicit Questions Under Discussion**Yating Wu, William Sheffield, Kyle Mahowald, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-elaborative-simplification-as-implicit-questions/)
215. [**Evaluating statistical language models as pragmatic reasoners arXiv:2305.01020**Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/)
216. **[DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://md.research.elamur.ai/free/en/papers/zhang-2024-darg-dynamic-evaluation-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhehao Zhang, Jiaao Chen, Diyi Yang*2024 · 17 citations*
217. [**MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models Proceedings of the 2nd GenBench Workshop on Generalisation (Benchmarking) in NLP**Dojun Park, Jiwoo Lee, Seohyun Park, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-multiprageval-multilingual-pragmatic-evaluation-large/)
218. **[Evaluating the Consistency of LLM Evaluators](https://md.research.elamur.ai/free/en/papers/lee-2025-evaluating-consistency-llm-evaluators/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Noah Lee, Jiwoo Hong, James Thorne*2025 · 16 citations*
219. **[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*
220. **[Translation in the Hands of Many: Centering Lay Users in Machine Translation Interactions](https://md.research.elamur.ai/free/en/papers/savoldi-2025-translation-hands-many-centering-lay/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Beatrice Savoldi, Alan Ramponi, Matteo Negri, et al.*2025 · 16 citations*
221. [**Data-driven Discovery with Large Generative Models arXiv:2402.13610**Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2024-data-driven-discovery-large-generative/)
222. [**From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging arXiv:2410.01215**Yuling Shi, Songsong Wang, Chengcheng Wan, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-code-correctness-closing-last-mile/)
223. **[When One LLM Drools, Multi-LLM Collaboration Rules](https://md.research.elamur.ai/free/en/papers/feng-2026-when-one-llm-drools-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shangbin Feng, Wenxuan Ding, Alisa Liu, et al.*2026 · 14 citations*
224. **[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*
225. **[CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/leang-2025-comat-chain-mathematically-annotated-thought/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joshua Ong Jun Leang, Aryo Pradipta Gema, Shay B. Cohen*2025 · 14 citations*
226. **[Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://md.research.elamur.ai/free/en/papers/shin-2025-mind-blind-spots-focus-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, et al.*2025 · 14 citations*
227. **[Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic](https://md.research.elamur.ai/free/en/papers/boughorbel-2023-analyzing-multilingual-competency-llms-multi/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sabri Boughorbel, Majd Hawasly*2023 · 14 citations*
228. [**Constituency Parsing using LLMs arXiv:2310.19462**Xuefeng Bai, Jialong Wu, Yulong Chen, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-constituency-parsing-using-llms/)
229. **[DataSciBench: An LLM Agent Benchmark for Data Science](https://md.research.elamur.ai/free/en/papers/zhang-2026-datascibench-llm-agent-benchmark-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dan Zhang, Sining Zhoubian, Min Cai, et al.*2026 · 13 citations*
230. [**CodeR: Issue Resolving with Multi-Agent and Task Graphs arXiv:2406.01304**Dong Chen, Shaoxin Lin, Muhan Zeng, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-coder-issue-resolving-multi-agent/)
231. **[Literature Meets Data: A Synergistic Approach to Hypothesis Generation](https://md.research.elamur.ai/free/en/papers/liu-2025-literature-meets-data-synergistic-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, et al.*2025 · 12 citations*
232. **[REARANK: Reasoning Re-ranking Agent via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zhang-2025-rearank-reasoning-re-ranking-agent/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Le Zhang, Bo Wang, Xipeng Qiu, et al.*2025 · 12 citations*
233. **[Benchmarking LLaMA-3 on Arabic Language Generation Tasks](https://md.research.elamur.ai/free/en/papers/khondaker-2024-benchmarking-llama-3-arabic-language/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Md Tawkat Islam Khondaker, Numaan Naeem, Fatimah Khan, et al.*2024 · 12 citations*
234. [**The ICL Consistency Test arXiv:2312.04945**Lucas Weber, Elia Bruni, Dieuwke Hupkes*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/weber-2023-icl-consistency-test/)
235. **[Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers](https://md.research.elamur.ai/free/en/papers/xu-2025-can-llms-identify-critical-limitations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhijian Xu, Yilun Zhao, Manasi Patwardhan, et al.*2025 · 11 citations*
236. [**E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models arXiv:2401.15927**Jinchang Hou, Chang Ao, Haihong Wu, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/hou-2024-e-eval-comprehensive-chinese/)
237. **[Pragmatic Competence Evaluation of Large Language Models for the Korean Language](https://md.research.elamur.ai/free/en/papers/park-2024-pragmatic-competence-evaluation-large-language/) [Pacific Asia Conference on Language, Information and Computation (PACLIC)](https://md.research.elamur.ai/free/en/venues/paclic/)**Dojun Park, Jiwoo Lee, Hyeyun Jeong, et al.*2024 · 11 citations*
238. [**CLEVA: Chinese Language Models EVAluation Platform arXiv:2308.04813**Yanyang Li, Jianqiao Zhao, Duo Zheng, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cleva-chinese-language-models/)
239. **[RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation](https://md.research.elamur.ai/free/en/papers/li-2026-rubrichub-comprehensive-highly-discriminative-rubric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sunzhu Li, Jiale Zhao, Huimin Ren, et al.*2026 · 9 citations*
240. **[A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI](https://md.research.elamur.ai/free/en/papers/chen-2025-rose-any-other-name-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beiduo Chen, Siyao Peng, Anna Korhonen, et al.*2025 · 9 citations*
241. **[ChatBench: From Static Benchmarks to Human-AI Evaluation](https://md.research.elamur.ai/free/en/papers/chang-2025-chatbench-static-benchmarks-human-ai/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Serina Chang, Ashton Anderson, Jake M. Hofman*2025 · 9 citations*
242. **[DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture](https://md.research.elamur.ai/free/en/papers/maji-2025-drishtikon-multimodal-multilingual-benchmark-testing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Arijit Maji, Raghvendra Kumar, Akash Ghosh, et al.*2025 · 9 citations*
243. **[Theorem Prover as a Judge for Synthetic Data Generation](https://md.research.elamur.ai/free/en/papers/leang-2025-theorem-prover-as-judge-synthetic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joshua Ong Jun Leang, Giwon Hong, Wenda Li, et al.*2025 · 9 citations*
244. [**CodeS: Natural Language to Code Repository via Multi-Layer Sketch arXiv:2403.16443**Daoguang Zan, Ailun Yu, Wei Liu, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zan-2024-codes-natural-language-code-repository/)
245. **[TurkBench: A Benchmark for Evaluating Turkish Large Language Models](https://md.research.elamur.ai/free/en/papers/toraman-2026-turkbench-benchmark-evaluating-turkish-large/) [Workshop Natural Language Processing for Turkic Languages](https://md.research.elamur.ai/free/en/venues/sigturk/)**Cagri Toraman, Ahmet Kaan Sever, Ayşe Aysu Cengiz, et al.*2026 · 8 citations*
246. **[Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/) [Workshop on the Role of Resources in the Age of Large Language Models](https://md.research.elamur.ai/free/en/venues/resourceful/)**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*
247. **[Red Teaming Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/chen-2026-red-teaming-large-reasoning-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiawei Chen, Yang Yang, Chao Yu, et al.*2026 · 7 citations*
248. **[SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature](https://md.research.elamur.ai/free/en/papers/ding-2026-scirag-adaptive-citation-aware-outline/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hang Ding, Yilun Zhao, Tiansheng Hu, et al.*2026 · 7 citations*
249. **[ICLEval: Evaluating In-Context Learning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-icleval-evaluating-context-learning-ability/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Wentong Chen, Yankai Lin, ZhenHao Zhou, et al.*2025 · 7 citations*
250. **[TLUE: A Tibetan Language Understanding Evaluation Benchmark](https://md.research.elamur.ai/free/en/papers/gao-2025-tlue-tibetan-language-understanding-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fan Gao, Cheng Huang, Yutong Liu, et al.*2025 · 7 citations*
251. [**HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew**Tzuf Paz-Argaman, Itai Mondshine, Asaf Achi Mordechai, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/argaman-2024-hesum-novel-dataset-abstractive-text/)
252. [**Shared Imagination: LLMs Hallucinate Alike arXiv:2407.16604**Yilun Zhou, Caiming Xiong, Silvio Savarese, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-shared-imagination-llms-hallucinate-alike/)
253. **[ThatiAR: Subjectivity Detection in Arabic News Sentences](https://md.research.elamur.ai/free/en/papers/suwaileh-2024-thatiar-subjectivity-detection-arabic-news/) [Proceedings of the International AAAI Conference on Web and Social Media](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-international-aaai-conference-on-web-and-social-media/)**Reem Suwaileh, Maram Hasanain, Fatema Hubail, et al.*2024 · 7 citations*
254. **[Detecting Training Data of Large Language Models via Expectation Maximization](https://md.research.elamur.ai/free/en/papers/kim-2026-detecting-training-data-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.*2026 · 5 citations*
255. **[SubLIME: Subset Selection via Rank Correlation Prediction for Data-Efficient LLM Evaluation](https://md.research.elamur.ai/free/en/papers/saranathan-2025-sublime-subset-selection-rank-correlation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gayathri Saranathan, Cong Xu, Mahammad Parwez Alam, et al.*2025 · 5 citations*
256. **[Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://md.research.elamur.ai/free/en/papers/oh-2026-uncertainty-quantification-llm-agents-foundations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Changdae Oh, Seongheon Park, To Eun Kim, et al.*2026 · 4 citations*
257. [**GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning arXiv:2407.02147**Hasna Chouikhi, Manel Aloui, Cyrine Ben Hammou, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/chouikhi-2024-gemmar-enhancing-llms-through-arabic/)
258. **[How Quantization Shapes Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/marcuzzi-2026-how-quantization-shapes-bias-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Federico Marcuzzi, Xuefei Ning, Roy Schwartz, et al.*2026 · 3 citations*
259. **[IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages](https://md.research.elamur.ai/free/en/papers/pattnayak-2026-indicjr-judge-free-benchmark-jailbreak/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Priyaranjan Pattnayak, Sanchari Chowdhuri*2026 · 3 citations*
260. **[SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://md.research.elamur.ai/free/en/papers/seo-2026-spio-ensemble-selective-strategies-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wonduk Seo, Juhyeon Lee, Yanjun Shao, et al.*2026 · 3 citations*
261. **[Trans-EnV: A Framework for Evaluating the Linguistic Robustness of LLMs Against English Varieties](https://md.research.elamur.ai/free/en/papers/lee-2025-trans-env-framework-evaluating-linguistic/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiyoung Lee, Seungho Kim, Jieun Han, et al.*2025 · 3 citations*
262. **[Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization](https://md.research.elamur.ai/free/en/papers/xing-2026-are-llms-reliable-rankers-rank/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tiancheng Xing, Jerry Li, Yixuan Du, et al.*2026 · 2 citations*
263. **[Can We Predict Before Executing Machine Learning Agents?](https://md.research.elamur.ai/free/en/papers/zheng-2026-can-we-predict-before-executing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingsheng Zheng, Jintian Zhang, Yujie Luo, et al.*2026 · 2 citations*
264. **[Learning to Ideate for Machine Learning Engineering Agents](https://md.research.elamur.ai/free/en/papers/zhang-2026-learning-ideate-machine-learning-engineering/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yunxiang Zhang, Kang Zhou, Zhichao Xu, et al.*2026 · 2 citations*
265. **[LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models](https://md.research.elamur.ai/free/en/papers/mazor-2026-lvlm-aware-multimodal-retrieval-rag/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nir Mazor, Tom Hope*2026 · 2 citations*
266. **[Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation](https://md.research.elamur.ai/free/en/papers/shen-2026-preconditioned-test-time-adaptation-out/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanwen Shen, Ting Ying, Jiajie Lu, et al.*2026 · 2 citations*
267. [**WASIL: In-the-Wild Arabic Spoken Interactions with LLMs arXiv:2605.16364**Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/ali-2026-wasil-wild-arabic-spoken-interactions/)
268. **[AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-agenticeval-agentic-self-evolving-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixu Wang, Xin Wang, Yang Yao, et al.*2026 · 1 citation*
269. **[Aligned Probing: Relating Toxic Behavior and Model Internals](https://md.research.elamur.ai/free/en/papers/waldis-2026-aligned-probing-relating-toxic-behavior/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Andreas Waldis, Vagrant Gautam, Anne Lauscher, et al.*2026 · 1 citation*
270. **[Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists](https://md.research.elamur.ai/free/en/papers/pietruszka-2026-can-models-help-us-create/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Michał Pietruszka, Łukasz Borchmann, Aleksander Jędrosz, et al.*2026 · 1 citation*
271. **[Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models](https://md.research.elamur.ai/free/en/papers/yin-2026-decentralized-arena-democratic-scalable-automatic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanbin Yin, Kun Zhou, Zhen Wang, et al.*2026 · 1 citation*
272. **[Evaluating Linguistic Knowledge of LLMs in Tamil: The ILAKKANAM Benchmark](https://md.research.elamur.ai/free/en/papers/varsha-2026-evaluating-linguistic-knowledge-llms-tamil/) [workshop on Challenges in Processing South Asian Languages](https://md.research.elamur.ai/free/en/venues/chipsal/)**Jeyarajalingam Varsha, Menan Velayuthan, Sumirtha Karunakaran, et al.*2026 · 1 citation*
273. [**MÖVE: A Holistic LLM Benchmark for the German Public Sector arXiv:2606.13111**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-m-ove-holistic-llm-benchmark/)
274. **[ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting](https://md.research.elamur.ai/free/en/papers/tian-2026-reattn-improving-attention-based-re/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxing Tian, Fengran Mo, Weixu Zhang, et al.*2026 · 1 citation*
275. **[Rethinking Scale: Deployment Trade-offs of Small Language Models under Agent Paradigms](https://md.research.elamur.ai/free/en/papers/wang-2026-rethinking-scale-deployment-trade-offs/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinlin Wang*2026 · 1 citation*
276. **[The Mighty ToRR: A Benchmark for Table Reasoning and Robustness in LLMs](https://md.research.elamur.ai/free/en/papers/tahan-2026-mighty-torr-benchmark-table-reasoning/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Shir Ashury-Tahan, Yifan Mai, Rajmohan C, et al.*2026 · 1 citation*
277. **[User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios](https://md.research.elamur.ai/free/en/papers/wu-2026-user-perceptions-vs-proxy-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyuan Wu, Roshni Kaushik, Wenkai Li, et al.*2026 · 1 citation*
278. [**Learning from Convenience Samples: A Case Study on Fine-Tuning LLMs for Survey Non-response in the German Longitudinal Election Study arXiv:2509.25063**Tobias Holtdirk, Dennis Assenmacher, Arnim Bleier, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/holtdirk-2025-learning-convenience-samples-case-study/)
279. [**Aquila2 Technical Report arXiv:2408.07410**Bo-Wen Zhang, Liangdong Wang, Jijie Li, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zhang-2024-aquila2-technical-report/)
280. **[Accelerating LLM Fine-Tuning via Embedding Knowledge Transfer](https://md.research.elamur.ai/free/en/papers/peng-2026-accelerating-llm-fine-tuning-embedding/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Meishu Peng, Ziyue Zhang, Yi Zhang, et al.*2026*
281. **[Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider](https://md.research.elamur.ai/free/en/papers/sauter-2026-actionable-interpretability-churn-classification-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Adrian Sauter, Vera Neplenbroek, Georgios Vlassopoulos, et al.*2026*
282. **[AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis](https://md.research.elamur.ai/free/en/papers/she-2026-aica-bench-holistically-examining-capabilities/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dong She, Xianrong Yao, Liqun Chen, et al.*2026*
283. [**ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models arXiv:2602.18776**Anas Alhumud, Abdulaziz Alhammadi, Muhammad Badruddin Khan*2026*](https://md.research.elamur.ai/free/en/papers/alhumud-2026-arabicnumbench-evaluating-arabic-number-reading/)
284. **[Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented SFT and DAPO RL](https://md.research.elamur.ai/free/en/papers/lorandi-2026-automatic-paper-analysis-categorisation-systematic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Michela Lorandi, Anya Belz, Simon Mille, et al.*2026*
285. **[Benchmark Data Contamination in Underrepresented Languages: A Comprehensive Analysis Using Brazilian Data](https://md.research.elamur.ai/free/en/papers/vilar-2026-benchmark-data-contamination-underrepresented-languages/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Iriedson Souto Maior de Moraes Vilar, David Candeia Maia, João Brunet, et al.*2026*
286. **[BenchNavigator: A Discovery Interface for Comparing LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/sokol-2026-benchnavigator-discovery-interface-comparing-llm/) [Workshop on Evaluating Evaluations (EvalEval)](https://md.research.elamur.ai/free/en/venues/evaleval/)**Anna Sokol, Inge Vejsbjerg, Elizabeth M. Daly, et al.*2026*
287. **[Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs](https://md.research.elamur.ai/free/en/papers/williams-2026-beyond-marginal-distributions-framework-evaluate/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tristan Williams, Franziska Weeber, Sebastian Padó, et al.*2026*
288. **[Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding](https://md.research.elamur.ai/free/en/papers/xu-2026-beyond-single-view-comprehensive-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dexuan Xu, Yuan Jiayin, Jianing Wang, et al.*2026*
289. **[Beyond the Singular: Revealing the Value of Multiple Generations in Benchmark Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-singular-revealing-value-multiple/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenbo Zhang, Hengrui Cai, Wenyu Chen*2026*
290. **[Can AI Revise Research Papers with Human Review Feedback? An Empirical Study and Benchmark](https://md.research.elamur.ai/free/en/papers/luo-2026-can-ai-revise-research-papers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Luo, Hong Huang, Jianxun Lian, et al.*2026*
291. [**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/)
292. **[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*
293. **[Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm](https://md.research.elamur.ai/free/en/papers/yilin-2026-contract-coding-repo-level-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yilin, Lujin Zhao, Yijie Shi*2026*
294. **[CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation](https://md.research.elamur.ai/free/en/papers/yuan-2026-ct-finebench-diagnostic-fidelity-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruifeng Yuan, Wanxing Chang, Weiwei Cao, et al.*2026*
295. **[Demystify the Role of Memory in Machine Learning Engineering Agents](https://md.research.elamur.ai/free/en/papers/zhao-2026-demystify-role-memory-machine-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyu Zhao, Junpeng Wang, Yuzhong Chen, et al.*2026*
296. **[Evaluating Language Model Pluralism through In-the-wild Crowd Discussions](https://md.research.elamur.ai/free/en/papers/mundada-2026-evaluating-language-model-pluralism-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gagan Mundada, Rohan Surana, Nandhini Swaminathan, et al.*2026*
297. **[Evaluating LLM Style Transfer Through Readability-Based Age Assessments](https://md.research.elamur.ai/free/en/papers/maro-2026-evaluating-llm-style-transfer-through/) [International Workshop on Spoken Dialogue System Technology](https://md.research.elamur.ai/free/en/venues/iwsds/)**Maria Di Maro, Antonio Origlia, Leonilda Bilo, et al.*2026*
298. **[FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation](https://md.research.elamur.ai/free/en/papers/oh-2026-finest-improving-llm-responses-sensitive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Juhyun Oh, Nayeon Lee, Chani Jung, et al.*2026*
299. [**From Global Benchmarks to Local Evaluations: Benchmarking LLMs for the German Public Sector arXiv:2608.17827**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-global-benchmarks-local-evaluations-benchmarking/)
300. **[GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-gsm-noise-exploring-enhancing-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengxin Zhang, Chengyu Huang, Xufu Liu, et al.*2026*

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