no code implementations • NAACL (maiworkshop) 2021 • Haidong Zhang, Yekun Chai
Emotion recognition in conversation has received considerable attention recently because of its practical industrial applications.
Ranked #35 on Emotion Recognition in Conversation on IEMOCAP
no code implementations • ACL 2022 • Qiwei Peng, David Weir, Julie Weeds, Yekun Chai
Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings.
no code implementations • Findings (EMNLP) 2021 • Yekun Chai, Haidong Zhang, Qiyue Yin, Junge Zhang
Generative Adversarial Networks (GANs) have achieved great success in image synthesis, but have proven to be difficult to generate natural language.
no code implementations • 16 Apr 2024 • Yekun Chai, Qingyi Liu, Jingwu Xiao, Shuohuan Wang, Yu Sun, Hua Wu
Harnessing visual texts represents a burgeoning frontier in the evolution of language modeling.
1 code implementation • 11 Apr 2024 • Qingyi Liu, Yekun Chai, Shuohuan Wang, Yu Sun, Qiwei Peng, Keze Wang, Hua Wu
This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models.
no code implementations • 30 Mar 2024 • Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Junior, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, Sampo Pyysalo
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
no code implementations • 29 Feb 2024 • Anton Lozhkov, Raymond Li, Loubna Ben allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #25 on Code Generation on MBPP
1 code implementation • 26 Feb 2024 • Qiwei Peng, Yekun Chai, Xuhong LI
These benchmarks have overlooked the vast landscape of massively multilingual NL to multilingual code, leaving a critical gap in the evaluation of multilingual LLMs.
1 code implementation • 2 Oct 2023 • Lei LI, Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Ningyu Zhang, Hua Wu
We validate our approach across a wide range of domains, incorporating seven distinct external tools.
no code implementations • 23 Feb 2023 • Yekun Chai, Qiyue Yin, Junge Zhang
In this work, we (1) first empirically show that the mixture-of-experts approach is able to enhance the representation capacity of the generator for language GANs and (2) harness the Feature Statistics Alignment (FSA) paradigm to render fine-grained learning signals to advance the generator training.
no code implementations • 9 Feb 2023 • Pengfei Zhu, Chao Pang, Yekun Chai, Lei LI, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu
In response to this lacuna, this paper introduces a pioneering contribution in the form of a text-to-waveform music generation model, underpinned by the utilization of diffusion models.
1 code implementation • 13 Dec 2022 • Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu
Extensive results show that ERNIE-Code outperforms previous multilingual LLMs for PL or NL across a wide range of end tasks of code intelligence, including multilingual code-to-text, text-to-code, code-to-code, and text-to-text generation.
no code implementations • SemEval (NAACL) 2022 • Yaqian Han, Yekun Chai, Shuohuan Wang, Yu Sun, Hongyi Huang, Guanghao Chen, Yitong Xu, Yang Yang
Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios.
Multi-Label Classification Natural Language Understanding +2
no code implementations • 21 Oct 2022 • Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
Derivative-free prompt learning has emerged as a lightweight alternative to prompt tuning, which only requires model inference to optimize the prompts.
no code implementations • 8 Sep 2021 • Yekun Chai, Shuo Jin, Junliang Xing
Automatically translating images to texts involves image scene understanding and language modeling.
Ranked #27 on Image Captioning on COCO Captions
no code implementations • 1 Jan 2021 • Yekun Chai, Qiyue Yin, Junge Zhang
Generative Adversarial Networks (GAN) are facing great challenges in synthesizing sequences of discrete elements, such as mode dropping and unstable training.
no code implementations • 24 Nov 2020 • Yekun Chai, Haidong Zhang, Shuo Jin
Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids.
1 code implementation • ACL 2020 • Yekun Chai, Shuo Jin, Xinwen Hou
Self-attention mechanisms have made striking state-of-the-art (SOTA) progress in various sequence learning tasks, standing on the multi-headed dot product attention by attending to all the global contexts at different locations.
1 code implementation • 12 Nov 2019 • Yekun Chai, Naomi Saphra, Adam Lopez
Diverse word representations have surged in most state-of-the-art natural language processing (NLP) applications.