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title: "Who cites Don't Give Me the — page 2 — 978 citing papers"
description: "978 papers of the Elamur Research library cite «Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks…"
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# Who cites «Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization»

978 papers of our library cite it (of the 149,439 whose bibliographies we read) · 184 with a page here, the most cited first · page 2 of 2 · 101–184 · [← back to «Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization»](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/)

[← prev](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/cited-by/) [1](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/cited-by/) **2**

101. **[Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation](https://md.research.elamur.ai/free/en/papers/deng-2021-compression-transduction-creation-unified-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Bowen Tan, Zhengzhong Liu, et al.*2021 · 102 citations*
102. **[FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Wan, Mohit Bansal*2022 · 101 citations*
103. **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations*
104. [**CodeGen2: Lessons for Training LLMs on Programming and Natural Languages arXiv:2305.02309**Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/nijkamp-2023-codegen2-lessons-training-llms-programming/)
105. **[LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://md.research.elamur.ai/free/en/papers/dou-2024-loramoe-alleviating-world-knowledge-forgetting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Enyu Zhou, Yan Liu, et al.*2024 · 93 citations*
106. **[FineSurE: Fine-grained Summarization Evaluation using LLMs](https://md.research.elamur.ai/free/en/papers/song-2024-finesure-fine-grained-summarization-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hwanjun Song, Hang Su, Igor Shalyminov, et al.*2024 · 92 citations*
107. **[f-Divergence Minimization for Sequence-Level Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wen-2023-f-divergence-minimization-sequence-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuqiao Wen, Zichao Li, Wenyu Du, et al.*2023 · 92 citations*
108. [**Large Language Models are Inconsistent and Biased Evaluators arXiv:2405.01724**Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/stureborg-2024-large-language-models-inconsistent-biased/)
109. [**Better & Faster Large Language Models via Multi-token Prediction arXiv:2404.19737**Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/gloeckle-2024-better-faster-large-language-models/)
110. **[FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-followbench-multi-level-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.*2024 · 86 citations*
111. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations*
112. **[Detoxifying Large Language Models via Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-detoxifying-large-language-models-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ningyu Zhang, Ziwen Xu, et al.*2024 · 84 citations*
113. [**AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model arXiv:2208.01448**Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.*2022 · 84 citations*](https://md.research.elamur.ai/free/en/papers/soltan-2022-alexatm-20b-few-shot-learning/)
114. **[AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/eddine-2022-arabart-pretrained-arabic-sequence-sequence/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Moussa Kamal Eddine, Nadi Tomeh, Nizar Habash, et al.*2022 · 80 citations*
115. [**Out-of-Distribution Detection and Selective Generation for Conditional Language Models arXiv:2209.15558**Jie Ren, Jiaming Luo, Yao Zhao, et al.*2022 · 80 citations*](https://md.research.elamur.ai/free/en/papers/ren-2022-out-distribution-detection-selective-generation/)
116. **[Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps](https://md.research.elamur.ai/free/en/papers/chuang-2024-lookback-lens-detecting-mitigating-contextual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, et al.*2024 · 78 citations*
117. **[Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory](https://md.research.elamur.ai/free/en/papers/cheng-2023-lift-yourself-up-retrieval-augmented/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xin Cheng, Di Luo, Xiuying Chen, et al.*2023 · 76 citations*
118. [**Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers arXiv:2404.04925**Libo Qin, Qiguang Chen, Yuhang Zhou, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-multilingual-large-language-model-survey/)
119. **[Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback](https://md.research.elamur.ai/free/en/papers/roit-2023-factually-consistent-summarization-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Roit, Johan Ferret, Lior Shani, et al.*2023 · 70 citations*
120. **[A Thorough Examination of Decoding Methods in the Era of LLMs](https://md.research.elamur.ai/free/en/papers/shi-2024-thorough-examination-decoding-methods-era/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chufan Shi, Haoran Yang, Deng Cai, et al.*2024 · 68 citations*
121. **[On Context Utilization in Summarization with Large Language Models](https://md.research.elamur.ai/free/en/papers/ravaut-2024-context-utilization-summarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mathieu Ravaut, Aixin Sun, Nancy Chen, et al.*2024 · 67 citations*
122. [**Evaluation of OpenAI o1: Opportunities and Challenges of AGI arXiv:2409.18486**Tianyang Zhong, Zhengliang Liu, Yi Pan, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-evaluation-openai-o1-opportunities-challenges/)
123. [**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/)
124. [**The Generative AI Paradox: "What It Can Create, It May Not Understand" arXiv:2311.00059**Peter West, Ximing Lu, Nouha Dziri, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/west-2023-generative-ai-paradox-create-may/)
125. **[Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts](https://md.research.elamur.ai/free/en/papers/nguyen-2024-democratizing-llms-low-resource-languages/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuan-Phi Nguyen, Mahani Aljunied, Shafiq Joty, et al.*2024 · 55 citations*
126. [**Parameter-Efficient Fine-Tuning Design Spaces arXiv:2301.01821**Jiaao Chen, Aston Zhang, Xingjian Shi, et al.*2023 · 47 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-parameter-efficient-fine-tuning-design/)
127. **[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*
128. **[Learning to Edit: Aligning LLMs with Knowledge Editing](https://md.research.elamur.ai/free/en/papers/jiang-2024-learning-edit-aligning-llms-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Chuhan Wu, et al.*2024 · 39 citations*
129. **[DeAL: Decoding-time Alignment for Large Language Models](https://md.research.elamur.ai/free/en/papers/huang-2025-deal-decoding-time-alignment-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**James Y. Huang, Sailik Sengupta, Daniele Bonadiman, et al.*2025 · 36 citations*
130. [**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/)
131. **[PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition](https://md.research.elamur.ai/free/en/papers/chen-2023-propsegment-large-scale-corpus-proposition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, et al.*2023 · 31 citations*
132. [**ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline arXiv:2404.02893**Yifan Xu, Xiao Liu, Xinghan Liu, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-chatglm-math-improving-math/)
133. [**BERT Fine-tuning For Arabic Text Summarization arXiv:2004.14135**Khalid N. Elmadani, Mukhtar Elgezouli, Anas Showk*2020 · 29 citations*](https://md.research.elamur.ai/free/en/papers/elmadani-2020-bert-fine-tuning-arabic-text/)
134. **[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*
135. [**Efficiently Adapting Pretrained Language Models To New Languages arXiv:2311.05741**Zoltan Csaki, Pian Pawakapan, Urmish Thakker, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/csaki-2023-efficiently-adapting-pretrained-language-models/)
136. **[CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2023-clean-eval-clean-evaluation-contaminated/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhong Zhu, Hongkun Hao, Zhiwei He, et al.*2023 · 20 citations*
137. [**FormalAlign: Automated Alignment Evaluation for Autoformalization arXiv:2410.10135**Jianqiao Lu, Yingjia Wan, Yinya Huang, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-formalalign-automated-alignment-evaluation-autoformalization/)
138. [**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/)
139. **[Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore](https://md.research.elamur.ai/free/en/papers/wu-2025-who-wrote-this-key-zero/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Junchao Wu, Runzhe Zhan, Derek F. Wong, et al.*2025 · 17 citations*
140. **[Jailbreaking LLMs with Arabic Transliteration and Arabizi](https://md.research.elamur.ai/free/en/papers/ghanim-2024-jailbreaking-llms-arabic-transliteration-arabizi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mansour Al Ghanim, Saleh Almohaimeed, Mengxin Zheng, et al.*2024 · 16 citations*
141. **[Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-token-level-density-based-uncertainty/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny, et al.*2025 · 13 citations*
142. **[LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios](https://md.research.elamur.ai/free/en/papers/wu-2025-lifbench-evaluating-instruction-following-performance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Wu, Minhao Wang, Yichen Liu, et al.*2025 · 11 citations*
143. [**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/)
144. **[Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models](https://md.research.elamur.ai/free/en/papers/vazhentsev-2025-unconditional-truthfulness-learning-unconditional-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Artem Vazhentsev, Ekaterina Fadeeva, Rui Xing, et al.*2025 · 10 citations*
145. [**PanGu-π: Enhancing Language Model Architectures via Nonlinearity Compensation arXiv:2312.17276**Yunhe Wang, Hanting Chen, Yehui Tang, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-pangu-pi-enhancing-language/)
146. **[Towards Understanding Multi-Task Learning (Generalization) of LLMs via Detecting and Exploring Task-Specific Neurons](https://md.research.elamur.ai/free/en/papers/leng-2025-understanding-multi-task-learning-generalization/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yongqi Leng, Deyi Xiong*2025 · 9 citations*
147. [**MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferences arXiv:2410.02381**Genta Indra Winata, David Anugraha, Lucky Susanto, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/winata-2024-metametrics-calibrating-metrics-generation-tasks/)
148. **[GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres](https://md.research.elamur.ai/free/en/papers/lin-2024-gumsley-evaluating-entity-salience-summarization/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jessica Lin, Amir Zeldes*2024 · 8 citations*
149. [**PanGu-π Pro:Rethinking Optimization and Architecture for Tiny Language Models arXiv:2402.02791**Yehui Tang, Kai Han, Fangcheng Liu, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-pangu-pi-pro-rethinking/)
150. [**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/)
151. **[FinAraT5: A text to text model for financial Arabic text understanding and generation](https://md.research.elamur.ai/free/en/papers/zmandar-2023-finarat5-text-text-model-financial/) [Conference on Language, Data and Knowledge](https://md.research.elamur.ai/free/en/venues/ldk/)**Nadhem Zmandar, Mo El-Haj, Paul Rayson*2023 · 7 citations*
152. **[GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/liu-2023-gumsum-multi-genre-data-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yang Janet Liu, Amir Zeldes*2023 · 7 citations*
153. **[Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://md.research.elamur.ai/free/en/papers/xue-2026-why-supervised-fine-tuning-fails/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chao Xue, Yao Wang, Mengqiao Liu, et al.*2026 · 6 citations*
154. **[Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution Alignment](https://md.research.elamur.ai/free/en/papers/peng-2025-enhancing-knowledge-distillation-large-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Tianyu Peng, Jiajun Zhang*2025 · 6 citations*
155. **[KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization](https://md.research.elamur.ai/free/en/papers/song-2026-knn-ssd-enabling-dynamic-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingbo Song, Heming Xia, Jun Zhang, et al.*2026 · 5 citations*
156. **[RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates](https://md.research.elamur.ai/free/en/papers/kowsher-2025-rocoft-efficient-finetuning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, et al.*2025 · 5 citations*
157. [**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/)
158. **[Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries](https://md.research.elamur.ai/free/en/papers/pastorino-2026-frame-frame-out-measuring-framing/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Valeria Pastorino, Nafise Sadat Moosavi*2026 · 3 citations*
159. **[Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-prompt-r1-collaborative-automatic-prompting/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenjin Liu, Haoran Luo, Xueyuan Lin, et al.*2026 · 3 citations*
160. **[Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs](https://md.research.elamur.ai/free/en/papers/yang-2026-sparse-brains-are-also-adaptive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiheng Yang, Yujie Wang, Chi Ma, et al.*2026 · 3 citations*
161. **[MetaLead: A Comprehensive Human-Curated Leaderboard Dataset for Transparent Reporting of Machine Learning Experiments](https://md.research.elamur.ai/free/en/papers/timmer-2026-metalead-comprehensive-human-curated-leaderboard/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Roelien C. Timmer, Necva Bölücü, Stephen Wan*2026 · 2 citations*
162. **[OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-opengenalign-preference-dataset-benchmark-trustworthy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanning Zhang, Juntong Song, Juno Zhu, et al.*2026 · 2 citations*
163. **[DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution](https://md.research.elamur.ai/free/en/papers/teja-2026-damasha-detecting-ai-mixed-adversarial/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**L. D. M. S. Sai Teja, N. Siva Gopala Krishna, Ufaq Khan, et al.*2026 · 1 citation*
164. **[InferenceDynamics: Adaptive LLM Routing through Structured Capability and Knowledge Profiling](https://md.research.elamur.ai/free/en/papers/shi-2026-inferencedynamics-adaptive-llm-routing-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haochen Shi, Tianshi Zheng, Weiqi Wang, et al.*2026 · 1 citation*
165. [**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/)
166. **[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*
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