---
title: "Who cites Yangqing Jia — page 2 — 2,224 papers of our library"
description: "2,224 papers of the Elamur Research library cite the works of Yangqing Jia, found in their bibliographies · page 2 of 2 · 101–174; the most cited first."
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---
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# Who cites Yangqing Jia

2,608 citations of their works from 2,224 papers of our library (of the 149,439 whose bibliographies we read) · 174 with a page here, the most cited first · page 2 of 2 · 101–174 · [← Yangqing Jia](https://md.research.elamur.ai/free/en/authors/yangqing-jia/)

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101. [**Training Tips for the Transformer Model arXiv:1804.00247 · The Prague Bulletin of Mathematical Linguistics 110, April 2018, pp. 43-70**Martin Popel, Ondřej Bojar*2018 · 370 citations*](https://md.research.elamur.ai/free/en/papers/popel-2018-training-tips-transformer-model/)
102. **[Combining Language and Vision with a Multimodal Skip-gram Model](https://md.research.elamur.ai/free/en/papers/lazaridou-2015-combining-language-vision-multimodal-skip/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Angeliki Lazaridou, Nghia The Pham, Marco Baroni*2015 · 368 citations*
103. **[emoji2vec: Learning Emoji Representations from their Description](https://md.research.elamur.ai/free/en/papers/eisner-2016-emoji2vec-learning-emoji-representations-their/) [International Workshop on Natural Language Processing for Social Media](https://md.research.elamur.ai/free/en/venues/socialnlp/)**Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, et al.*2016 · 360 citations*
104. [**Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes arXiv:1807.11205**Xianyan Jia, Shutao Song, Wei He, et al.*2018 · 348 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-highly-scalable-deep-learning-training/)
105. **[How Transferable are Neural Networks in NLP Applications?](https://md.research.elamur.ai/free/en/papers/mou-2016-how-transferable-are-neural-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lili Mou, Zhao Meng, Rui Yan, et al.*2016 · 328 citations*
106. [**Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions arXiv:1802.04730**Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, et al.*2018 · 323 citations*](https://md.research.elamur.ai/free/en/papers/vasilache-2018-tensor-comprehensions-framework-agnostic-high/)
107. [**Multi-task Sequence to Sequence Learning arXiv:1511.06114**Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, et al.*2015 · 321 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-multi-task-sequence-sequence-learning/)
108. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language arXiv:2204.00598**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 302 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
109. **[Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement](https://md.research.elamur.ai/free/en/papers/he-2016-pairwise-word-interaction-modeling-deep/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hua He, Jimmy Lin*2016 · 297 citations*
110. **[Black Holes and White Rabbits: Metaphor Identification with Visual Features](https://md.research.elamur.ai/free/en/papers/shutova-2016-black-holes-white-rabbits-metaphor/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ekaterina Shutova, Douwe Kiela, Jean Maillard*2016 · 293 citations*
111. **[SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/wang-2018-switchout-efficient-data-augmentation-algorithm/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinyi Wang, Hieu Pham, Zihang Dai, et al.*2018 · 290 citations*
112. **[Detecting Sarcasm in Multimodal Social Platforms](https://md.research.elamur.ai/free/en/papers/schifanella-2016-detecting-sarcasm-multimodal-social-platforms/) [ACM International Conference on Multimedia (ACM MM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-multimedia-acm-mm/)**Rossano Schifanella, Paloma de Juan, Joel Tetreault, et al.*2016 · 290 citations*
113. [**Hierarchical Multiscale Recurrent Neural Networks arXiv:1609.01704**Junyoung Chung, Sungjin Ahn, Yoshua Bengio*2016 · 288 citations*](https://md.research.elamur.ai/free/en/papers/chung-2016-hierarchical-multiscale-recurrent-neural-networks/)
114. [**Llemma: An Open Language Model For Mathematics arXiv:2310.10631**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/)
115. **[Guided Open Vocabulary Image Captioning with Constrained Beam Search](https://md.research.elamur.ai/free/en/papers/anderson-2017-guided-open-vocabulary-image-captioning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Peter Anderson, Basura Fernando, Mark Johnson, et al.*2017 · 286 citations*
116. [**Beyond Data and Model Parallelism for Deep Neural Networks SysML 2019**Zhihao Jia, Matei Zaharia, Alex Aiken*2018 · 280 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/)
117. **[Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning](https://md.research.elamur.ai/free/en/papers/lazaridou-2015-hubness-pollution-delving-into-cross/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Angeliki Lazaridou, Georgiana Dinu, Marco Baroni*2015 · 276 citations*
118. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/)
119. [**Perceiver: General Perception with Iterative Attention arXiv:2103.03206**Andrew Jaegle, Felix Gimeno, Andrew Brock, et al.*2021 · 269 citations*](https://md.research.elamur.ai/free/en/papers/jaegle-2021-perceiver-general-perception-iterative-attention/)
120. **[Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/zhou-2016-deep-recurrent-models-fast-forward/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jie Zhou, Ying Cao, Xuguang Wang, et al.*2016 · 265 citations*
121. [**Once-for-All: Train One Network and Specialize it for Efficient Deployment arXiv:1908.09791**Han Cai, Chuang Gan, Tianzhe Wang, et al.*2019 · 252 citations*](https://md.research.elamur.ai/free/en/papers/cai-2019-once-all-train-one-network/)
122. [**Long-term Recurrent Convolutional Networks for Visual Recognition and Description arXiv:1411.4389**Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, et al.*2014 · 245 citations*](https://md.research.elamur.ai/free/en/papers/donahue-2014-long-term-recurrent-convolutional-networks/)
123. [**CLUE: A Chinese Language Understanding Evaluation Benchmark arXiv:2004.05986**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
124. [**FigureQA: An Annotated Figure Dataset for Visual Reasoning arXiv:1710.07300**Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, et al.*2017 · 240 citations*](https://md.research.elamur.ai/free/en/papers/kahou-2017-figureqa-annotated-figure-dataset-visual/)
125. **[From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood](https://md.research.elamur.ai/free/en/papers/guu-2017-language-programs-bridging-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kelvin Guu, Panupong Pasupat, Evan Liu, et al.*2017 · 236 citations*
126. **[Understanding the Difficulty of Training Transformers](https://md.research.elamur.ai/free/en/papers/liu-2020-understanding-difficulty-training-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyuan Liu, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 231 citations*
127. **[Unsupervised Deep Embedding for Clustering Analysis](https://md.research.elamur.ai/free/en/papers/xie-2016-unsupervised-deep-embedding-clustering-analysis/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Junyuan Xie, Ross Girshick, Ali Farhadi*2016 · 222 citations*
128. **[Few-Shot Charge Prediction with Discriminative Legal Attributes](https://md.research.elamur.ai/free/en/papers/hu-2018-few-shot-charge-prediction-discriminative/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zikun Hu, Xiang Li, Cunchao Tu, et al.*2018 · 221 citations*
129. **[Generating Fact Checking Explanations](https://md.research.elamur.ai/free/en/papers/atanasova-2020-generating-fact-checking-explanations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, et al.*2020 · 220 citations*
130. **[Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Matena, Colin Raffel*2021 · 208 citations*
131. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
132. [**A Convolutional Attention Network for Extreme Summarization of Source Code arXiv:1602.03001**Miltiadis Allamanis, Hao Peng, Charles Sutton*2016 · 198 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2016-convolutional-attention-network-extreme-summarization/)
133. [**What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation arXiv:2008.03703**Vitaly Feldman, Chiyuan Zhang*2020 · 193 citations*](https://md.research.elamur.ai/free/en/papers/feldman-2020-neural-networks-memorize-why-discovering/)
134. [**End-to-End Differentiable Proving arXiv:1705.11040**Tim Rocktäschel, Sebastian Riedel*2017 · 188 citations*](https://md.research.elamur.ai/free/en/papers/rocktaschel-2017-end-end-differentiable-proving/)
135. [**Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads arXiv:1901.05758**Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/)
136. [**PipeDream: Fast and Efficient Pipeline Parallel DNN Training arXiv:1806.03377**Aaron Harlap, Deepak Narayanan, Amar Phanishayee, et al.*2018 · 162 citations*](https://md.research.elamur.ai/free/en/papers/harlap-2018-pipedream-fast-efficient-pipeline-parallel/)
137. **[Effective Domain Mixing for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/britz-2017-effective-domain-mixing-neural-machine/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Denny Britz, Quoc Le, Reid Pryzant*2017 · 156 citations*
138. **[Fine-Tuning Language Models with Just Forward Passes](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sadhika Malladi, Tianyu Gao, Eshaan Nichani, et al.*2023 · 153 citations*
139. **[HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment](https://md.research.elamur.ai/free/en/papers/vulic-2017-hyperlex-large-scale-evaluation-graded/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Ivan Vulić, Daniela Gerz, Douwe Kiela, et al.*2017 · 148 citations*
140. [**Efficient Processing of Deep Neural Networks: A Tutorial and Survey Proceedings of the IEEE 2017**Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.*2017 · 146 citations*](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/)
141. [**Deep Network Guided Proof Search arXiv:1701.06972 (In Thomas Eiter and David Sands, editors, 21st International Conference on Logic for Programming, Artificial Intelligence and Reasoning (LPAR-21). EPiC Series in Computing, vol. 46, pages 85-105, EasyChair, 2017. ISSN 2398-7340)**Sarah Loos, Geoffrey Irving, Christian Szegedy, et al.*2017 · 139 citations*](https://md.research.elamur.ai/free/en/papers/loos-2017-deep-network-guided-proof-search/)
142. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
143. [**Learning to Represent Programs with Graphs arXiv:1711.00740**Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi*2017 · 127 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/)
144. **[An Improved Baseline for Sentence-level Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhou-2022-improved-baseline-sentence-level-relation/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Wenxuan Zhou, Muhao Chen*2022 · 124 citations*
145. [**Finding Neurons in a Haystack: Case Studies with Sparse Probing arXiv:2305.01610**Wes Gurnee, Neel Nanda, Matthew Pauly, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/gurnee-2023-finding-neurons-haystack-case-studies/)
146. [**Making Neural QA as Simple as Possible but not Simpler arXiv:1703.04816**Dirk Weissenborn, Georg Wiese, Laura Seiffe*2017 · 122 citations*](https://md.research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/)
147. [**ImageNet Training in Minutes arXiv:1709.05011**Yang You, Zhao Zhang, Cho-Jui Hsieh, et al.*2017 · 112 citations*](https://md.research.elamur.ai/free/en/papers/you-2017-imagenet-training-minutes/)
148. [**DAPPLE: A Pipelined Data Parallel Approach for Training Large Models arXiv:2007.01045**Shiqing Fan, Yi Rong, Chen Meng, et al.*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fan-2020-dapple-pipelined-data-parallel-approach/)
149. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
150. [**Accelerating Sparse Deep Neural Networks arXiv 2021 (NVIDIA)**Asit Mishra, Jorge Albericio Latorre, Jeff Pool, et al.*2021 · 97 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2021-accelerating-sparse-deep-neural-networks/)
151. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
152. **[Neural Machine Translation with Supervised Attention](https://md.research.elamur.ai/free/en/papers/liu-2016-neural-machine-translation-supervised-attention/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Lemao Liu, Masao Utiyama, Andrew Finch, et al.*2016 · 85 citations*
153. [**Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training arXiv:2208.06102**Jie You, Jae-Won Chung, Mosharaf Chowdhury*2022 · 82 citations*](https://md.research.elamur.ai/free/en/papers/you-2022-zeus-understanding-optimizing-gpu-energy/)
154. **[Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniela Gerz, Ivan Vulić, Edoardo Ponti, et al.*2018 · 82 citations*
155. [**vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design MICRO 2016**Minsoo Rhu, Natalia Gimelshein, Jason Clemons, et al.*2016 · 81 citations*](https://md.research.elamur.ai/free/en/papers/rhu-2016-vdnn-virtualized-deep-neural-networks/)
156. [**Large Language Models in Law: A Survey arXiv:2312.03718**Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-large-language-models-law-survey/)
157. **[On the limit of English conversational speech recognition](https://md.research.elamur.ai/free/en/papers/tuske-2021-limit-english-conversational-speech-recognition/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Zoltan Tuske, George Saon, Brian Kingsbury*2021 · 49 citations*
158. [**Stable LM 2 1.6B Technical Report arXiv:2402.17834**Marco Bellagente, Jonathan Tow, Dakota Mahan, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/bellagente-2024-stable-lm-2-1-6b/)
159. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/)
160. **[Multimodal Semantic Learning from Child-Directed Input](https://md.research.elamur.ai/free/en/papers/lazaridou-2016-multimodal-semantic-learning-child-directed/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Angeliki Lazaridou, Grzegorz Chrupała, Raquel Fernández, et al.*2016 · 29 citations*
161. [**Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent arXiv:2411.02265**Xingwu Sun, Yanfeng Chen, Yiqing Huang, et al.*2024 · 25 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-hunyuan-large-open-source/)
162. **[Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks](https://md.research.elamur.ai/free/en/papers/jindal-2018-generating-image-captions-arabic-root/) [Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop](https://md.research.elamur.ai/free/en/venues/conference-of-the-north-american-chapter-of-the-association-for-computational/)**Vasu Jindal*2018 · 23 citations*
163. **[CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation](https://md.research.elamur.ai/free/en/papers/ziegler-2025-craft-your-dataset-task-specific/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, et al.*2025 · 20 citations*
164. **[Resolving Discrepancies in Compute-Optimal Scaling of Language Models](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 19 citations*
165. **[Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder](https://md.research.elamur.ai/free/en/papers/mohamed-2023-violet-vision-language-model-arabic/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Abdelrahman Mohamed, Fakhraddin Alwajih, El Moatez Billah Nagoudi, et al.*2023 · 18 citations*
166. [**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/)
167. [**Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy**Andrei C. Aioanei, Regine Hunziker-Rodewald, Konstantin Klein, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/aioanei-2023-deep-aramaic-towards-synthetic-data/)
168. **[Automatically Detecting Reduced-formed English Pronunciations by Using Deep Learning](https://md.research.elamur.ai/free/en/papers/chen-2022-automatically-detecting-reduced-formed-english/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Lei Chen, Chenglin Jiang, Yiwei Gu, et al.*2022 · 4 citations*
169. [**Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English arXiv:1810.13088**Yan Yin, Ramon Prieto, Bin Wang, et al.*2018 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-attention-based-sequence-sequence-model/)
170. **[TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustness](https://md.research.elamur.ai/free/en/papers/zhou-2026-tempperturb-eval-joint-effects-internal/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yongxin Zhou, Philippe Mulhem, Didier Schwab*2026 · 1 citation*
171. **[Chronos: Learning Temporal Dynamics of Reasoning Chains for Test-Time Scaling](https://md.research.elamur.ai/free/en/papers/zhang-2026-chronos-learning-temporal-dynamics-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Zhang, Jiayi Liao, Chengpeng Li, et al.*2026*
172. **[DeepPavlov Strikes Back: A Toolkit for Improving LLM Reliability and Trustworthiness](https://md.research.elamur.ai/free/en/papers/nikolaev-2026-deeppavlov-strikes-back-toolkit-improving/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Evgenii Nikolaev, Timur Ionov, Anna Korzanova, et al.*2026*
173. **[EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models](https://md.research.elamur.ai/free/en/papers/xing-2026-efficientllm-unified-pruning-aware-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingrun Xing, Zheng Liu, Shitao Xiao, et al.*2026*
174. **[Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization](https://md.research.elamur.ai/free/en/papers/xu-2026-understanding-preventing-entropy-collapse-rlvr/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huimin Xu, Shuai Zhao, Xiaobao Wu, et al.*2026*

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