1 code implementation • Findings (EMNLP) 2021 • Yang Zhong, Jingfeng Yang, Wei Xu, Diyi Yang
Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.
no code implementations • EMNLP 2020 • Jingfeng Yang, Diyi Yang, Zhaoran Ma
Existing approaches to disfluency detection heavily depend on human-annotated data.
1 code implementation • EMNLP 2021 • Jingfeng Yang, Federico Fancellu, Bonnie Webber, Diyi Yang
The availability of corpora has led to significant advances in training semantic parsers in English.
no code implementations • 10 Feb 2025 • Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability.
1 code implementation • 28 Oct 2024 • Yilun Jin, Zheng Li, Chenwei Zhang, Tianyu Cao, Yifan Gao, Pratik Jayarao, Mao Li, Xin Liu, Ritesh Sarkhel, Xianfeng Tang, Haodong Wang, Zhengyang Wang, Wenju Xu, Jingfeng Yang, Qingyu Yin, Xian Li, Priyanka Nigam, Yi Xu, Kai Chen, Qiang Yang, Meng Jiang, Bing Yin
Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants.
no code implementations • 11 Oct 2024 • Yangyi Chen, Binxuan Huang, Yifan Gao, Zhengyang Wang, Jingfeng Yang, Heng Ji
Our two-stage approach consists of first estimating a function that maps computational resources (e. g., FLOPs) to the pre-training Loss using a series of sampling models, followed by mapping the pre-training loss to downstream task Performance after the critical "emergent phase".
no code implementations • 31 Jul 2024 • Kewei Cheng, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Binxuan Huang, Ruirui Li, Shiyang Li, Zheng Li, Yifan Gao, Xian Li, Bing Yin, Yizhou Sun
To investigate into the true inductive reasoning capabilities of LLMs, we propose a novel framework, SolverLearner.
1 code implementation • 28 Jun 2024 • Xudong Wang, Jingfeng Yang, Trevor Darrell
By integrating our unsupervised pseudo masks into SA-1B's ground-truth masks and training UnSAM with only 1% of SA-1B, a lightly semi-supervised UnSAM can often segment entities overlooked by supervised SAM, exceeding SAM's AR by over 6. 7% and AP by 3. 9% on SA-1B.
Ranked #1 on
Segmentation
on SA-1B
1 code implementation • 25 Apr 2024 • Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xianfeng Tang, Chen Luo, Ming Zeng, Haoming Jiang, Yifan Gao, Priyanka Nigam, Sreyashi Nag, Bing Yin, Yining Hua, Xuan Zhou, Omid Rohanian, Anshul Thakur, Lei Clifton, David A. Clifton
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention.
1 code implementation • 7 Feb 2024 • Yu Wang, Yifan Gao, Xiusi Chen, Haoming Jiang, Shiyang Li, Jingfeng Yang, Qingyu Yin, Zheng Li, Xian Li, Bing Yin, Jingbo Shang, Julian McAuley
We aim to build models containing a considerable portion of self-updatable parameters, enabling the model to integrate new knowledge effectively and efficiently.
2 code implementations • 2 Jan 2024 • Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, Xia Hu
To achieve this goal, we propose SelfExtend to extend the context window of LLMs by constructing bi-level attention information: the grouped attention and the neighbor attention.
1 code implementation • NeurIPS 2023 • Xin Liu, Zheng Li, Yifan Gao, Jingfeng Yang, Tianyu Cao, Zhengyang Wang, Bing Yin, Yangqiu Song
The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history.
no code implementations • 21 Nov 2023 • Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, Haoming Jiang
QDIT provides a simple method to simultaneously control dataset diversity and quality, allowing us to conduct an in-depth study on the effect of diversity and quality on instruction tuning performance.
no code implementations • 1 Oct 2023 • Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Chia-Yuan Chang, Xia Hu
Our method progressively increases the training length throughout the pretraining phase, thereby mitigating computational costs and enhancing efficiency.
no code implementations • 27 Aug 2023 • Zining Zhu, Haoming Jiang, Jingfeng Yang, Sreyashi Nag, Chao Zhang, Jie Huang, Yifan Gao, Frank Rudzicz, Bing Yin
Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
no code implementations • 19 May 2023 • Jie Huang, Yifan Gao, Zheng Li, Jingfeng Yang, Yangqiu Song, Chao Zhang, Zining Zhu, Haoming Jiang, Kevin Chen-Chuan Chang, Bing Yin
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e. g., "Digital Cameras", generating a list of complementary concepts, e. g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers.
1 code implementation • 26 Apr 2023 • Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.
no code implementations • 27 Mar 2023 • Ruijie Wang, Zheng Li, Jingfeng Yang, Tianyu Cao, Chao Zhang, Bing Yin, Tarek Abdelzaher
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones.
1 code implementation • 25 Feb 2023 • Ruolin Su, Jingfeng Yang, Ting-Wei Wu, Biing-Hwang Juang
With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly.
no code implementations • 15 Dec 2022 • Caleb Ziems, William Held, Jingfeng Yang, Jwala Dhamala, Rahul Gupta, Diyi Yang
First, we use this system to stress tests question answering, machine translation, and semantic parsing.
no code implementations • 28 Nov 2022 • Xutan Peng, YiPeng Zhang, Jingfeng Yang, Mark Stevenson
Although it has been demonstrated that Natural Language Processing (NLP) algorithms are vulnerable to deliberate attacks, the question of whether such weaknesses can lead to software security threats is under-explored.
1 code implementation • Findings (NAACL) 2022 • Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, Diyi Yang
SeqZero achieves SOTA performance of BART-based models on GeoQuery and EcommerceQuery, which are two few-shot datasets with compositional data split.
1 code implementation • NAACL 2022 • Jingfeng Yang, Le Zhang, Diyi Yang
Although sequence-to-sequence models often achieve good performance in semantic parsing for i. i. d.
2 code implementations • ACL 2022 • Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, Shachi Paul
Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.
no code implementations • 28 May 2020 • Wentong Liao, Xiang Chen, Jingfeng Yang, Stefan Roth, Michael Goesele, Michael Ying Yang, Bodo Rosenhahn
This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation.
no code implementations • 27 Aug 2019 • Jingfeng Yang, Federico Fancellu, Bonnie Webber
The availability of corpora to train semantic parsers in English has lead to significant advances in the field.
no code implementations • 30 Aug 2018 • Jingfeng Yang, Sujian Li
Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis.
1 code implementation • EMNLP 2018 • Yizhong Wang, Sujian Li, Jingfeng Yang
Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis.
no code implementations • IJCNLP 2017 • Yizhong Wang, Sujian Li, Jingfeng Yang, Xu sun, Houfeng Wang
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.
General Classification
Implicit Discourse Relation Classification
+3