activity
20222026
most citedDiffuSum: Generation Enhanced Extractive Summarization with Diffusion

3 citations · 10 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2026

Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models

Dong Chen, Fangyun Wei, Ziyu Wan +18

We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters acro…

cs.CV2026

MMViR: A Multi-Modal and Multi-Granularity Representation for Long-range Video Understanding

Zizhong Li, Haopeng Zhang, Jiawei Zhang

Long videos, ranging from minutes to hours, present significant challenges for current Multi-modal Large Language Models (MLLMs) due to their complex events, diverse scenes, and lo…

cs.CL2025

Token-Level Precise Attack on RAG: Searching for the Best Alternatives to Mislead Generation

Zizhong Li, Haopeng Zhang, Jiawei Zhang

While large language models (LLMs) have achieved remarkable success in providing trustworthy responses for knowledge-intensive tasks, they still face critical limitations such as h…

cs.IR2024★ 1 cited

Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval

Zizhong Li, Haopeng Zhang, Jiawei Zhang

Recent research has explored distilling knowledge from large language models (LLMs) to optimize retriever models, especially within the retrieval-augmented generation (RAG) framewo…

cs.CL2024

GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMs

Xiao Liu, Jiawei Zhang

This study introduces GPTA, a Large Language Model assistance training framework, that enhances the training of downstream task models via prefix prompt. By minimizing data exposur…

cs.CL2024

Unveiling the Magic: Investigating Attention Distillation in Retrieval-augmented Generation

Zizhong Li, Haopeng Zhang, Jiawei Zhang

Retrieval-augmented generation framework can address the limitations of large language models by enabling real-time knowledge updates for more accurate answers. An efficient way in…

cs.CL2023★ 1 cited

A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT

Zizhong Li, Haopeng Zhang, Jiawei Zhang

The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are s…

cs.CL2023★ 1 cited

Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with MultiAPI Benchmark

Xiao Liu, Jianfeng Lin, Jiawei Zhang

The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications. However, these…

cs.CV2023

Generic Attention-model Explainability by Weighted Relevance Accumulation

Yiming Huang, Aozhe Jia, Xiaodan Zhang +1

Attention-based transformer models have achieved remarkable progress in multi-modal tasks, such as visual question answering. The explainability of attention-based methods has rece…

cs.CL2023

Contrastive Hierarchical Discourse Graph for Scientific Document Summarization

Haopeng Zhang, Xiao Liu, Jiawei Zhang

The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extract…

cs.CL2023★ 3 cited

DiffuSum: Generation Enhanced Extractive Summarization with Diffusion

Haopeng Zhang, Xiao Liu, Jiawei Zhang

Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by maki…

cs.CL2023★ 1 cited

SummIt: Iterative Text Summarization via ChatGPT

Haopeng Zhang, Xiao Liu, Jiawei Zhang

Text summarization systems have made significant progress in recent years, but typically generate summaries in one single step. However, the one-shot summarization setting is somet…

cs.CL2023★ 3 cited

Extractive Summarization via ChatGPT for Faithful Summary Generation

Haopeng Zhang, Xiao Liu, Jiawei Zhang

Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent in…

cs.CL2022

HEGEL: Hypergraph Transformer for Long Document Summarization

Haopeng Zhang, Xiao Liu, Jiawei Zhang

Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations mode…