Search Results for author: Zhuang Li

Found 46 papers, 23 papers with code

SCAR: Efficient Instruction-Tuning for Large Language Models via Style Consistency-Aware Response Ranking

1 code implementation16 Jun 2024 Zhuang Li, Yuncheng Hua, Thuy-Trang Vu, Haolan Zhan, Lizhen Qu, Gholamreza Haffari

Recent studies have shown that maintaining a consistent response style by human experts and enhancing data quality in training sets can significantly improve the performance of fine-tuned Large Language Models (LLMs) while reducing the number of training examples needed.

Open-Ended Question Answering

NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human

no code implementations6 Jun 2024 Shuo Huang, William MacLean, Xiaoxi Kang, Anqi Wu, Lizhen Qu, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari

Increasing concerns about privacy leakage issues in academia and industry arise when employing NLP models from third-party providers to process sensitive texts.

Privacy Preserving

DragAnything: Motion Control for Anything using Entity Representation

1 code implementation12 Mar 2024 Weijia Wu, Zhuang Li, YuChao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, Di Zhang

We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.

Object Video Generation

SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting

1 code implementation CVPR 2024 Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, Zeyu Wang

We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device.

Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights

1 code implementation6 Mar 2024 Zijie Zeng, Shiqi Liu, Lele Sha, Zhuang Li, Kaixun Yang, Sannyuya Liu, Dragan Gašević, Guanliang Chen

Our empirical findings highlight (1) detecting AI-generated sentences in hybrid texts is overall a challenging task because (1. 1) human writers' selecting and even editing AI-generated sentences based on personal preferences adds difficulty in identifying the authorship of segments; (1. 2) the frequent change of authorship between neighboring sentences within the hybrid text creates difficulties for segment detectors in identifying authorship-consistent segments; (1. 3) the short length of text segments within hybrid texts provides limited stylistic cues for reliable authorship determination; (2) before embarking on the detection process, it is beneficial to assess the average length of segments within the hybrid text.

Sentence Sentence Classification +2

RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations

no code implementations17 Feb 2024 Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Lay-Ki Soon, Zhaleh Semnani Azad, Ingrid Zukerman, Gholamreza Haffari

While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.

Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach

no code implementations7 Feb 2024 Zhuang Li, Levon Haroutunian, Raj Tumuluri, Philip Cohen, Gholamreza Haffari

Post-editing has proven effective in improving the quality of text generated by large language models (LLMs) such as GPT-3. 5 or GPT-4, particularly when direct updating of their parameters to enhance text quality is infeasible or expensive.

Domain Generalization Machine Translation +1

Natural Language Processing for Dialects of a Language: A Survey

no code implementations11 Jan 2024 Aditya Joshi, Raj Dabre, Diptesh Kanojia, Zhuang Li, Haolan Zhan, Gholamreza Haffari, Doris Dippold

Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.

Attribute Machine Translation +4

Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach

1 code implementation18 Dec 2023 Yuyang Chai, Zhuang Li, Jiahui Liu, Lei Chen, Fei Li, Donghong Ji, Chong Teng

Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines.

Data Augmentation Multi Label Text Classification +3

TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce

1 code implementation8 Dec 2023 Tongxin Hu, Zhuang Li, Xin Jin, Lizhen Qu, Xin Zhang

Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms.

Legal Reasoning

Continual Learning for Image Segmentation with Dynamic Query

1 code implementation29 Nov 2023 Weijia Wu, Yuzhong Zhao, Zhuang Li, Lianlei Shan, Hong Zhou, Mike Zheng Shou

Image segmentation based on continual learning exhibits a critical drop of performance, mainly due to catastrophic forgetting and background shift, as they are required to incorporate new classes continually.

Continual Learning Image Segmentation +5

Paragraph-to-Image Generation with Information-Enriched Diffusion Model

1 code implementation24 Nov 2023 Weijia Wu, Zhuang Li, Yefei He, Mike Zheng Shou, Chunhua Shen, Lele Cheng, Yan Li, Tingting Gao, Di Zhang, Zhongyuan Wang

In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation.

Image Generation Language Modelling +1

Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models

no code implementations21 Sep 2023 Levon Haroutunian, Zhuang Li, Lucian Galescu, Philip Cohen, Raj Tumuluri, Gholamreza Haffari

Our approach involves initially generating a set of candidate outputs by prompting an LLM and subsequently reranking them using a task-specific reranker model.

Text Generation

Semantic Parsing in Limited Resource Conditions

no code implementations14 Sep 2023 Zhuang Li

This thesis explores challenges in semantic parsing, specifically focusing on scenarios with limited data and computational resources.

Active Learning Continual Learning +4

FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing

1 code implementation27 May 2023 Zhuang Li, Yuyang Chai, Terry Yue Zhuo, Lizhen Qu, Gholamreza Haffari, Fei Li, Donghong Ji, Quan Hung Tran

Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.

Graph Similarity Human Judgment Correlation +4

The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning

no code implementations22 May 2023 Zhuang Li, Lizhen Qu, Philip R. Cohen, Raj V. Tumuluri, Gholamreza Haffari

Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem.

Active Learning Semantic Parsing

A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension

1 code implementation5 May 2023 Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Hong Zhou, Mike Zheng Shou, Xiang Bai

Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i. e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video.

Reading Comprehension Retrieval +2

FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation

1 code implementation5 May 2023 Yuzhong Zhao, Weijia Wu, Zhuang Li, Jiahong Li, Weiqiang Wang

This paper introduces a novel video text synthesis technique called FlowText, which utilizes optical flow estimation to synthesize a large amount of text video data at a low cost for training robust video text spotters.

Optical Flow Estimation Text Spotting

ICDAR 2023 Video Text Reading Competition for Dense and Small Text

no code implementations10 Apr 2023 Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Mike Zheng Shou, Umapada Pal, Dimosthenis Karatzas, Xiang Bai

In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios.

Task 2 Text Detection +2

Active Learning for Multilingual Semantic Parser

no code implementations30 Jan 2023 Zhuang Li, Gholamreza Haffari

Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language.

Active Learning Semantic Parsing +1

Let's Negotiate! A Survey of Negotiation Dialogue Systems

no code implementations18 Dec 2022 Haolan Zhan, YuFei Wang, Tao Feng, Yuncheng Hua, Suraj Sharma, Zhuang Li, Lizhen Qu, Gholamreza Haffari

Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.

Strong Instance Segmentation Pipeline for MMSports Challenge

2 code implementations28 Sep 2022 Bo Yan, Fengliang Qi, Zhuang Li, Yadong Li, Hongbin Wang

The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation Challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court.

Data Augmentation Instance Segmentation +2

Real-time End-to-End Video Text Spotter with Contrastive Representation Learning

1 code implementation18 Jul 2022 Wejia Wu, Zhuang Li, Jiahong Li, Chunhua Shen, Hong Zhou, Size Li, Zhongyuan Wang, Ping Luo

Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.

Contrastive Learning Representation Learning +2

The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge

no code implementations28 Jun 2022 Bo Yan, Leilei Cao, Zhuang Li, Hongbin Wang

Finally, our approach achieves 63. 008\%AP@0. 50:0. 95 on the test set of CVPR2022 AVA Challenge.

Data Augmentation

The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation

no code implementations24 Jun 2022 Leilei Cao, Zhuang Li, Bo Yan, Feng Zhang, Fengliang Qi, Yuchen Hu, Hongbin Wang

The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames.

Object object-detection +6

A Zipf's Law-based Text Generation Approach for Addressing Imbalance in Entity Extraction

no code implementations25 May 2022 Zhenhua Wang, Ming Ren, Dong Gao, Zhuang Li

The Zipf's Law emerges as a well-suited adoption, and to transition from words to entities, words within the documents are classified as common and rare ones.

Text Generation

Paraphrasing Techniques for Maritime QA system

no code implementations21 Mar 2022 Fatemeh Shiri, Terry Yue Zhuo, Zhuang Li, Van Nguyen, Shirui Pan, Weiqing Wang, Reza Haffari, Yuan-Fang Li

In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.

Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation

1 code implementation27 Feb 2022 Zhuang Li, Lizhen Qu, Qiongkai Xu, Tongtong Wu, Tianyang Zhan, Gholamreza Haffari

In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples.

Data Augmentation Disentanglement +3

Contrastive Learning of Semantic and Visual Representations for Text Tracking

1 code implementation30 Dec 2021 Zhuang Li, Weijia Wu, Mike Zheng Shou, Jiahong Li, Size Li, Zhongyuan Wang, Hong Zhou

Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.

Contrastive Learning

Pretrained Language Model in Continual Learning: A Comparative Study

no code implementations ICLR 2022 Tongtong Wu, Massimo Caccia, Zhuang Li, Yuan-Fang Li, Guilin Qi, Gholamreza Haffari

In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 veins of CL methods on 3 benchmarks in 2 typical incremental settings.

Continual Learning Language Modelling

Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers

1 code implementation EMNLP 2021 Zhuang Li, Lizhen Qu, Gholamreza Haffari

We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.

Continual Learning Semantic Parsing

AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation

1 code implementation11 Jun 2021 Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang

In our evaluations, the method outperforms all the state-of-the-art image retrieval algorithms on some out-of-domain image datasets.

Clustering Image Augmentation +4

Representation Learning for Weakly Supervised Relation Extraction

no code implementations10 Apr 2021 Zhuang Li

The experiments have demonstrated that this type of feature, combine with the traditional hand-crafted features, could improve the performance of the logistic classification model for relation extraction, especially on the classification of relations with only minor training instances.

Relation Relation Extraction +2

On Robustness of Neural Semantic Parsers

no code implementations EACL 2021 Shuo Huang, Zhuang Li, Lizhen Qu, Lei Pan

In this paper, we provide the empirical study on the robustness of semantic parsers in the presence of adversarial attacks.

Data Augmentation Semantic Parsing

Context Dependent Semantic Parsing: A Survey

1 code implementation COLING 2020 Zhuang Li, Lizhen Qu, Gholamreza Haffari

Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.

Semantic Parsing

Asymmetric Correntropy for Robust Adaptive Filtering

no code implementations21 Nov 2019 Badong Chen, Yuqing Xie, Zhuang Li, Yingsong Li, Pengju Ren

Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables.

Cannot find the paper you are looking for? You can Submit a new open access paper.