---
title: "Who cites Jean Y. Wu — page 3 — 2,651 papers of our library"
description: "2,651 papers of the Elamur Research library cite the works of Jean Y. Wu, found in their bibliographies · page 3 of 5 · 201–300; the most cited first."
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---
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# Who cites Jean Y. Wu

2,654 citations of their works from 2,651 papers of our library (of the 149,439 whose bibliographies we read) · 418 with a page here, the most cited first · page 3 of 5 · 201–300 · [← Jean Y. Wu](https://md.research.elamur.ai/free/en/authors/jean-y-wu/)

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201. **[CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://md.research.elamur.ai/free/en/papers/ye-2021-crossfit-few-shot-learning-challenge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qinyuan Ye, Bill Yuchen Lin, Xiang Ren*2021 · 210 citations*
202. **[Active Example Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yiming Zhang, Shi Feng, Chenhao Tan*2022 · 209 citations*
203. **[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*
204. **[Recursive Deep Models for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/li-2014-recursive-deep-models-discourse-parsing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Rumeng Li, Eduard Hovy*2014 · 206 citations*
205. **[Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 202 citations*
206. **[Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2020-structured-pruning-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ziheng Wang, Jeremy Wohlwend, Tao Lei*2020 · 202 citations*
207. **[Block Pruning For Faster Transformers](https://md.research.elamur.ai/free/en/papers/lagunas-2021-block-pruning-faster-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**François Lagunas, Ella Charlaix, Victor Sanh, et al.*2021 · 199 citations*
208. [**COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining arXiv:2102.08473**Yu Meng, Chenyan Xiong, Payal Bajaj, et al.*2021 · 198 citations*](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)
209. **[One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations](https://md.research.elamur.ai/free/en/papers/ji-2015-one-vector-is-not-enough/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yangfeng Ji, Jacob Eisenstein*2015 · 198 citations*
210. **[The Right Tool for the Job: Matching Model and Instance Complexities](https://md.research.elamur.ai/free/en/papers/schwartz-2020-right-tool-job-matching-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, et al.*2020 · 196 citations*
211. **[Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/bastings-2017-graph-convolutional-encoders-syntax-aware/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jasmijn Bastings, Ivan Titov, Wilker Aziz, et al.*2017 · 194 citations*
212. **[A Dataset for Multi-Target Stance Detection](https://md.research.elamur.ai/free/en/papers/sobhani-2017-dataset-multi-target-stance-detection/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Parinaz Sobhani, Diana Inkpen, Xiaodan Zhu*2017 · 190 citations*
213. **[Tweet Sarcasm Detection Using Deep Neural Network](https://md.research.elamur.ai/free/en/papers/zhang-2016-tweet-sarcasm-detection-deep-neural/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Meishan Zhang, Yue Zhang, Guohong Fu*2016 · 190 citations*
214. **[Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://md.research.elamur.ai/free/en/papers/qi-2021-mind-style-text-adversarial-backdoor/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fanchao Qi, Yangyi Chen, Xurui Zhang, et al.*2021 · 189 citations*
215. **[Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://md.research.elamur.ai/free/en/papers/han-2020-explaining-black-box-predictions-unveiling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov*2020 · 187 citations*
216. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization arXiv:2106.12672**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 186 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
217. **[Adapting Language Models to Compress Contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 182 citations*
218. **[Modeling Empathy and Distress in Reaction to News Stories](https://md.research.elamur.ai/free/en/papers/buechel-2018-modeling-empathy-distress-reaction-news/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sven Buechel, Anneke Buffone, Barry Slaff, et al.*2018 · 181 citations*
219. **[Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://md.research.elamur.ai/free/en/papers/goot-2021-massive-choice-ample-tasks-machamp/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rob van der Goot, Ahmet Üstün, Alan Ramponi, et al.*2021 · 177 citations*
220. [**On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective arXiv:2302.12095**Jindong Wang, Xixu Hu, Wenxin Hou, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-robustness-chatgpt-adversarial-out-distribution/)
221. **[Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masahiro Kaneko, Danushka Bollegala*2021 · 175 citations*
222. [**StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding arXiv:1908.04577**Wei Wang, Bin Bi, Ming Yan, et al.*2019 · 175 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-structbert-incorporating-language-structures-pre/)
223. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision arXiv:2312.09390**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
224. **[Self-training Improves Pre-training for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/du-2021-self-training-improves-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jingfei Du, Edouard Grave, Beliz Gunel, et al.*2021 · 172 citations*
225. **[Quark: Controllable Text Generation with Reinforced Unlearning](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 170 citations*
226. **[What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation](https://md.research.elamur.ai/free/en/papers/habernal-2016-what-makes-convincing-argument-empirical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ivan Habernal, Iryna Gurevych*2016 · 165 citations*
227. **[Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation](https://md.research.elamur.ai/free/en/papers/huang-2019-achieving-verified-robustness-symbol-substitutions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Po-Sen Huang, Robert Stanforth, Johannes Welbl, et al.*2019 · 163 citations*
228. [**Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue**Shereen Oraby, Vrindavan Harrison, Lena Reed, et al.*2016 · 163 citations*](https://md.research.elamur.ai/free/en/papers/oraby-2016-creating-characterizing-diverse-corpus-sarcasm/)
229. **[Deep Learning Models for Sentiment Analysis in Arabic](https://md.research.elamur.ai/free/en/papers/sallab-2015-deep-learning-models-sentiment-analysis/) [Workshop on Arabic Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-arabic-natural-language-processing/)**Ahmad Al Sallab, Hazem Hajj, Gilbert Badaro, et al.*2015 · 162 citations*
230. **[Larger-Scale Transformers for Multilingual Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 161 citations*
231. **[Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhang-2015-shallow-convolutional-neural-network-implicit/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Biao Zhang, Jinsong Su, Deyi Xiong, et al.*2015 · 161 citations*
232. **[Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering](https://md.research.elamur.ai/free/en/papers/wu-2022-self-adaptive-context-learning-information/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, et al.*2022 · 159 citations*
233. **[Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, et al.*2017 · 156 citations*
234. [**Ask Me Anything: A simple strategy for prompting language models arXiv:2210.02441**Simran Arora, Avanika Narayan, Mayee F. Chen, et al.*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/arora-2022-ask-me-anything-simple-strategy/)
235. **[Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/angelidis-2018-multiple-instance-learning-networks-fine/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Stefanos Angelidis, Mirella Lapata*2018 · 154 citations*
236. **[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*
237. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators arXiv:2303.16854**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
238. **[Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/dou-2019-investigating-meta-learning-algorithms-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos*2019 · 150 citations*
239. **[Discourse Parsing with Attention-based Hierarchical Neural Networks](https://md.research.elamur.ai/free/en/papers/li-2016-discourse-parsing-attention-based-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qi Li, Tianshi Li, Baobao Chang*2016 · 150 citations*
240. **[Do latent tree learning models identify meaningful structure in sentences?](https://md.research.elamur.ai/free/en/papers/williams-2018-do-latent-tree-learning-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Adina Williams, Andrew Drozdov, Samuel R. Bowman*2018 · 149 citations*
241. **[Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://md.research.elamur.ai/free/en/papers/honovich-2022-instruction-induction-few-examples-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Or Honovich, Uri Shaham, Samuel R. Bowman, et al.*2022 · 148 citations*
242. **[Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?](https://md.research.elamur.ai/free/en/papers/pham-2021-out-order-how-important-is/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Thang Pham, Trung Bui, Long Mai, et al.*2021 · 148 citations*
243. [**Is GPT-3 a Good Data Annotator? arXiv:2212.10450**Bosheng Ding, Chengwei Qin, Linlin Liu, et al.*2022 · 147 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/)
244. **[Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaonan Li, Kai Lv, Hang Yan, et al.*2023 · 146 citations*
245. **[Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Chung, Ece Kamar, Saleema Amershi*2023 · 144 citations*
246. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
247. **[Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://md.research.elamur.ai/free/en/papers/wen-2023-hard-prompts-made-easy-gradient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Wen, Neel Jain, John Kirchenbauer, et al.*2023 · 141 citations*
248. **[Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.*2023 · 140 citations*
249. **[Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models](https://md.research.elamur.ai/free/en/papers/yang-2021-be-careful-about-poisoned-word/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Wenkai Yang, Lei Li, Zhiyuan Zhang, et al.*2021 · 135 citations*
250. **[Interpretable Neural Predictions with Differentiable Binary Variables](https://md.research.elamur.ai/free/en/papers/bastings-2019-interpretable-neural-predictions-differentiable-binary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jasmijn Bastings, Wilker Aziz, Ivan Titov*2019 · 135 citations*
251. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches arXiv:1904.01172**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 135 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
252. [**Simple synthetic data reduces sycophancy in large language models arXiv:2308.03958**Jerry Wei, Da Huang, Yifeng Lu, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/)
253. **[Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network](https://md.research.elamur.ai/free/en/papers/khanpour-2016-dialogue-act-classification-domain-independent/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Hamed Khanpour, Nishitha Guntakandla, Rodney Nielsen*2016 · 134 citations*
254. **[Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://md.research.elamur.ai/free/en/papers/czarnowska-2021-quantifying-social-biases-nlp-generalization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Paula Czarnowska, Yogarshi Vyas, Kashif Shah*2021 · 133 citations*
255. **[What to Pre-Train on? Efficient Intermediate Task Selection](https://md.research.elamur.ai/free/en/papers/poth-2021-what-pre-train-efficient-intermediate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.*2021 · 132 citations*
256. **[NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://md.research.elamur.ai/free/en/papers/winata-2023-nusax-multilingual-parallel-sentiment-dataset/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Genta Indra Winata, Alham Fikri Aji, Samuel Cahyawijaya, et al.*2023 · 130 citations*
257. **[Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://md.research.elamur.ai/free/en/papers/shum-2023-automatic-prompt-augmentation-selection-chain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kashun Shum, Shizhe Diao, Tong Zhang*2023 · 128 citations*
258. [**Function Vectors in Large Language Models arXiv:2310.15213**Eric Todd, Millicent L. Li, Arnab Sen Sharma, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/todd-2023-function-vectors-large-language-models/)
259. **[Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment](https://md.research.elamur.ai/free/en/papers/wallace-2015-sparse-contextually-informed-models-irony/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Byron C. Wallace, Do Kook Choe, Eugene Charniak*2015 · 123 citations*
260. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
261. **[I-BERT: Integer-only BERT Quantization](https://md.research.elamur.ai/free/en/papers/kim-2021-i-bert-integer-only-bert/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Sehoon Kim, Amir Gholami, Zhewei Yao, et al.*2021 · 122 citations*
262. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
263. **[A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models](https://md.research.elamur.ai/free/en/papers/ji-2016-latent-variable-recurrent-neural-network/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yangfeng Ji, Gholamreza Haffari, Jacob Eisenstein*2016 · 121 citations*
264. [**Compositional Exemplars for In-context Learning arXiv:2302.05698**Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-compositional-exemplars-context-learning/)
265. **[Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning](https://md.research.elamur.ai/free/en/papers/wang-2023-large-language-models-latent-variable/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyi Wang, Wanrong Zhu, Michael Saxon, et al.*2023 · 116 citations*
266. **[MoEfication: Transformer Feed-forward Layers are Mixtures of Experts](https://md.research.elamur.ai/free/en/papers/zhang-2022-moefication-transformer-feed-forward-layers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, et al.*2022 · 115 citations*
267. **[Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://md.research.elamur.ai/free/en/papers/yoo-2022-ground-truth-labels-matter-deeper/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.*2022 · 114 citations*
268. [**ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning arXiv:2111.10952**Vamsi Aribandi, Yi Tay, Tal Schuster, et al.*2021 · 114 citations*](https://md.research.elamur.ai/free/en/papers/aribandi-2021-ext5-towards-extreme-multi-task/)
269. [**Efficient Large Scale Language Modeling with Mixtures of Experts arXiv:2112.10684**Mikel Artetxe, Shruti Bhosale, Naman Goyal, et al.*2021 · 113 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2021-efficient-large-scale-language-modeling/)
270. [**EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers arXiv:2309.08532**Qingyan Guo, Rui Wang, Junliang Guo, et al.*2023 · 111 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evoprompt-connecting-llms-evolutionary-algorithms/)
271. **[Learning to Retrieve In-Context Examples for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Liang Wang, Nan Yang, Furu Wei*2024 · 106 citations*
272. [**LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions arXiv:2304.14402**Minghao Wu, Abdul Waheed, Chiyu Zhang, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/)
273. **[Tokenizer Choice For LLM Training: Negligible or Crucial?](https://md.research.elamur.ai/free/en/papers/ali-2024-tokenizer-choice-llm-training-negligible/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.*2024 · 104 citations*
274. [**Reducing Sentiment Bias in Language Models via Counterfactual Evaluation arXiv:1911.03064**Po-Sen Huang, Huan Zhang, Ray Jiang, et al.*2019 · 104 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-reducing-sentiment-bias-language-models/)
275. **[Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications](https://md.research.elamur.ai/free/en/papers/benamara-2017-evaluative-language-beyond-bags-words/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Farah Benamara, Maite Taboada, Yannick Mathieu*2017 · 104 citations*
276. **[Parallel Context Windows for Large Language Models](https://md.research.elamur.ai/free/en/papers/ratner-2023-parallel-context-windows-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nir Ratner, Yoav Levine, Yonatan Belinkov, et al.*2023 · 102 citations*
277. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jane Pan, Tianyu Gao, Howard Chen, et al.*2023 · 102 citations*
278. **[Certified Robustness to Text Adversarial Attacks by Randomized MASK](https://md.research.elamur.ai/free/en/papers/zeng-2023-certified-robustness-text-adversarial-attacks/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jiehang Zeng, Jianhan Xu, Xiaoqing Zheng, et al.*2023 · 101 citations*
279. [**Dataless Knowledge Fusion by Merging Weights of Language Models arXiv:2212.09849**Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, et al.*2022 · 101 citations*](https://md.research.elamur.ai/free/en/papers/jin-2022-dataless-knowledge-fusion-merging-weights/)
280. [**Rationale-Augmented Ensembles in Language Models arXiv:2207.00747**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-rationale-augmented-ensembles-language-models/)
281. **[Probing Across Time: What Does RoBERTa Know and When?](https://md.research.elamur.ai/free/en/papers/liu-2021-probing-across-time-what-does/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Leo Z. Liu, Yizhong Wang, Jungo Kasai, et al.*2021 · 96 citations*
282. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
283. **[Interpreting Neural Networks to Improve Politeness Comprehension](https://md.research.elamur.ai/free/en/papers/aubakirova-2016-interpreting-neural-networks-improve-politeness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Malika Aubakirova, Mohit Bansal*2016 · 87 citations*
284. **[NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/) [Northern European Journal of Language Technology](https://md.research.elamur.ai/free/en/venues/northern-european-journal-of-language-technology/)**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 86 citations*
285. **[Finding Skill Neurons in Pre-trained Transformer-based Language Models](https://md.research.elamur.ai/free/en/papers/wang-2022-finding-skill-neurons-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, et al.*2022 · 85 citations*
286. [**Formalizing and Benchmarking Prompt Injection Attacks and Defenses arXiv:2310.12815**Yupei Liu, Yuqi Jia, Runpeng Geng, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-formalizing-benchmarking-prompt-injection-attacks/)
287. [**In-Context Unlearning: Language Models as Few Shot Unlearners arXiv:2310.07579**Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/)
288. **[Mitigating Label Biases for In-context Learning](https://md.research.elamur.ai/free/en/papers/fei-2023-mitigating-label-biases-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Fei, Yifan Hou, Zeming Chen, et al.*2023 · 80 citations*
289. **[Finding Support Examples for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-finding-support-examples-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaonan Li, Xipeng Qiu*2023 · 78 citations*
290. [**Learning in the Rational Speech Acts Model arXiv:1510.06807**Will Monroe, Christopher Potts*2015 · 78 citations*](https://md.research.elamur.ai/free/en/papers/monroe-2015-learning-rational-speech-acts-model/)
291. [**Benchmarking Benchmark Leakage in Large Language Models arXiv:2404.18824**Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmarking-benchmark-leakage-large-language/)
292. [**Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future**Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/klie-2023-annotation-error-detection-analyzing-past/)
293. [**What Language Model to Train if You Have One Million GPU Hours? arXiv:2210.15424**Teven Le Scao, Thomas Wang, Daniel Hesslow, et al.*2022 · 73 citations*](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/)
294. **[Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://md.research.elamur.ai/free/en/papers/gu-2024-model-editing-harms-general-abilities/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, et al.*2024 · 67 citations*
295. **[A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models](https://md.research.elamur.ai/free/en/papers/baly-2017-characterization-study-arabic-twitter-data/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Ramy Baly, Gilbert Badaro, Georges El-Khoury, et al.*2017 · 67 citations*
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297. [**Linear Representations of Sentiment in Large Language Models arXiv:2310.15154**Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/tigges-2023-linear-representations-sentiment-large-language/)
298. **[Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans](https://md.research.elamur.ai/free/en/papers/lampinen-2024-can-language-models-handle-recursively/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Andrew Lampinen*2024 · 63 citations*
299. **[Exploring Different Dimensions of Attention for Uncertainty Detection](https://md.research.elamur.ai/free/en/papers/adel-2017-exploring-different-dimensions-attention-uncertainty/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Heike Adel, Hinrich Schütze*2017 · 63 citations*
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