Papers by Zhiyuan Fang
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Observable changes in the scene are reflected in captions, but actions are also linked to social aspects such as intentions, effects, and attributes that describe the agent. |
| Approach: | They propose to generate captions from videos that describe latent aspects of the human agent's actions. |
| Outcome: | The proposed model can be used to describe latent aspects of human actions in video clips and answer questions about videos. |
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis (2022.coling-1)
Copied to clipboard
| Challenge: | Existing generative methods focus on a single task at a time. |
| Approach: | They propose a unified generative multi-task framework that can solve multiple ABSA tasks . they propose to control the type of task prompts consisting of multiple element prompts . |
| Outcome: | The proposed framework achieves state-of-the-art results in almost all ABSA tasks and competitive results in task transfer scenarios. |
HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses (2025.acl-long)
Copied to clipboard
Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization. |
| Approach: | They propose a retrieval-augmented generation framework which leverages LLMs’ powerful reasoning capacity to compensate for the incompleteness of user queries. |
| Outcome: | The proposed framework improves the accuracy and reliability of Large Language Models (LLMs) by combining the rich knowledge of LLMs with Hypothesis Outputs. |
MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)
Copied to clipboard
Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang
| Challenge: | Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs. |
| Approach: | They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair. |
| Outcome: | The proposed framework integrates graphical information of two molecules in pair. |
GROLE: Instance-Level Group Relative Optimization for LoRA Experts in Incremental Learning (2026.findings-acl)
Copied to clipboard
Yongyi Liao, Wencan Lai, Jun Fang, Jinjin Guo, Xiaohui Zhang, Zhiyuan Liu, Chao Liu, Pengzhang Liu, Qixia Jiang
| Challenge: | Large language models demonstrate remarkable zero-shot generalization, but adapting to downstream tasks requires continual fine-tuning. |
| Approach: | They propose a method that incrementally constructs a pool of frozen, task-specific LoRA experts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in task-free and blurred-boundary settings. |
End-to-end Knowledge Retrieval with Multi-modal Queries (2023.acl-long)
Copied to clipboard
| Challenge: | a new task is proposed to learn knowledge retrieval with multimodal queries . a vision-language model can retrieve knowledge using images and text inputs . |
| Approach: | They propose a task for vision-language models to retrieve knowledge with multi-modal queries . they propose reViz, a model that integrates content from both text and image queries based on a multimodal query task . |
| Outcome: | The proposed task performs better under zero-shot settings than previous work on cross-modal retrieval. |
Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding (2024.emnlp-main)
Copied to clipboard
Weilin Zhao, Yuxiang Huang, Xu Han, Wang Xu, Chaojun Xiao, Xinrong Zhang, Yewei Fang, Kaihuo Zhang, Zhiyuan Liu, Maosong Sun
| Challenge: | Speculative decoding is a widely used method that accelerates the generation process of large language models (LLMs) drafting efficiency has become a bottleneck in the final speedup of speculative drafting, therefore generating longer drafts at less cost can lead to better speedup. |
| Approach: | They propose a method that uses existing model to drafting and target LLM to verify draft in a low-cost parallel manner. |
| Outcome: | The proposed method can achieve speedups of up to 2.4 over speculative decoding and 3.9 over vanilla decoding without fine-tuning draft and target models. |
GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)
Copied to clipboard
Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction. |
| Approach: | They propose a series of datasets for training visual-based GUI agents using general VLMs. |
| Outcome: | The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks. |
AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)
Copied to clipboard
Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang
| Challenge: | Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency. |
| Approach: | They propose an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. |
| Outcome: | The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images. |