Search Results for author: Zekun Wang

Found 22 papers, 8 papers with code

Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension

1 code implementation Findings (EMNLP) 2021 Haichao Zhu, Zekun Wang, Heng Zhang, Ming Liu, Sendong Zhao, Bing Qin

Then, we only fine-tune the lottery subnetwork, a small fraction of the whole parameters, on the annotated target domain data for adaptation.

Domain Adaptation Reading Comprehension

GUIDE: A Guideline-Guided Dataset for Instructional Video Comprehension

no code implementations26 Jun 2024 Jiafeng Liang, Shixin Jiang, Zekun Wang, Haojie Pan, Zerui Chen, Zheng Chu, Ming Liu, Ruiji Fu, Zhongyuan Wang, Bing Qin

Our proposed benchmark consists of three sub-tasks to evaluate comprehension ability of models: (1) Step Captioning: models have to generate captions for specific steps from videos.

PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

no code implementations20 Jun 2024 Junjie Wang, Yin Zhang, Yatai Ji, Yuxiang Zhang, Chunyang Jiang, YuBo Wang, Kang Zhu, Zekun Wang, Tiezhen Wang, Wenhao Huang, Jie Fu, Bei Chen, Qunshu Lin, Minghao Liu, Ge Zhang, Wenhu Chen

Recent advancements in Large Multimodal Models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks.

Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation

no code implementations30 May 2024 Jingchang Chen, Hongxuan Tang, Zheng Chu, Qianglong Chen, Zekun Wang, Ming Liu, Bing Qin

To this end, we propose FunCoder, a code generation framework incorporating the divide-and-conquer strategy with functional consensus.

Code Generation Math

Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations

no code implementations22 May 2024 Ziqiao Ma, Zekun Wang, Joyce Chai

In this work, we aim to examine how corrective feedback from interactions influences neural language acquisition from the ground up through systematically controlled experiments, assessing whether it contributes to learning efficiency in language models.

Language Acquisition Language Modelling

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models

no code implementations20 Feb 2024 Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Zekun Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Stephen W. Huang, Chenghua Lin, Jie Fu

The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.

Instruction Following

LLM Agents for Psychology: A Study on Gamified Assessments

no code implementations19 Feb 2024 Qisen Yang, Zekun Wang, Honghui Chen, Shenzhi Wang, Yifan Pu, Xin Gao, Wenhao Huang, Shiji Song, Gao Huang

Psychological measurement is essential for mental health, self-understanding, and personal development.

CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark

1 code implementation22 Jan 2024 Ge Zhang, Xinrun Du, Bei Chen, Yiming Liang, Tongxu Luo, Tianyu Zheng, Kang Zhu, Yuyang Cheng, Chunpu Xu, Shuyue Guo, Haoran Zhang, Xingwei Qu, Junjie Wang, Ruibin Yuan, Yizhi Li, Zekun Wang, Yudong Liu, Yu-Hsuan Tsai, Fengji Zhang, Chenghua Lin, Wenhao Huang, Wenhu Chen, Jie Fu

We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.

Align on the Fly: Adapting Chatbot Behavior to Established Norms

1 code implementation26 Dec 2023 Chunpu Xu, Steffi Chern, Ethan Chern, Ge Zhang, Zekun Wang, Ruibo Liu, Jing Li, Jie Fu, PengFei Liu

In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e. g., social norms) across time and locations.

Chatbot

MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document

no code implementations8 Nov 2023 Zheng Chu, Zekun Wang, Jiafeng Liang, Ming Liu, Bing Qin

To address this issue, we propose MTGER, a novel Multi-view Temporal Graph Enhanced Temporal Reasoning framework for temporal reasoning over time-involved documents.

Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction

no code implementations30 Aug 2023 Jun Li, Jingjian Wang, Hongwei Wang, Xing Deng, Jielong Chen, Bing Cao, Zekun Wang, Guanjie Xu, Ge Zhang, Feng Shi, Hualei Liu

(ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention.

Click-Through Rate Prediction Recommendation Systems

SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models

no code implementations24 May 2023 Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin

Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, significantly hampering their efficiency in real-world applications.

Data Augmentation

Interactive Natural Language Processing

no code implementations22 May 2023 Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang, Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu, Yike Guo, Jie Fu

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.

Decision Making

Chinese Open Instruction Generalist: A Preliminary Release

2 code implementations17 Apr 2023 Ge Zhang, Yemin Shi, Ruibo Liu, Ruibin Yuan, Yizhi Li, Siwei Dong, Yu Shu, Zhaoqun Li, Zekun Wang, Chenghua Lin, Wenhao Huang, Jie Fu

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat. openai. com/}}.

Distilled Dual-Encoder Model for Vision-Language Understanding

2 code implementations16 Dec 2021 Zekun Wang, Wenhui Wang, Haichao Zhu, Ming Liu, Bing Qin, Furu Wei

We propose a cross-modal attention distillation framework to train a dual-encoder model for vision-language understanding tasks, such as visual reasoning and visual question answering.

Question Answering Visual Entailment +2

WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19

1 code implementation4 Jan 2021 Zekun Wang, Pengwei Wang, Peter C. Louis, Lee E. Wheless, Yuankai Huo

The serverless edge-computing design minimizes the extra hardware costs (e. g., specific devices or cloud computing servers).

Blocking Cloud Computing +1

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