158 citations · 181 across the 19 of their papers we have counts for
19 papers
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
Yuhan Wang, Haopeng Zhang, Yibo Ding +6
Pre-training on text-attributed graphs (TAGs) is central to building transferable graph foundation models, where LLM-as-Aligner methods align graph and text representations through…
HAVEN: Hierarchically Aligned Multimodal Benchmark for Unified Video Understanding
Mengqi Shi, Haopeng Zhang
While Multimodal Large Language Models (MLLMs) exhibit strong performance on standard video tasks, their ability to faithfully summarize and reason over complex narratives remains…
V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization
Yubo Jiang, Yitong An, Xin Yang +7
We introduce V-tableR1, a process-supervised reinforcement learning framework that elicits rigorous, verifiable reasoning from multimodal large language models (MLLMs). Current MLL…
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…
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…
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…
A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models
Haopeng Zhang, Philip S. Yu, Jiawei Zhang
Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language…
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…
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…
Unsupervised Multi-document Summarization with Holistic Inference
Haopeng Zhang, Sangwoo Cho, Kaiqiang Song +4
Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised…
XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates
Haopeng Zhang, Hayate Iso, Sairam Gurajada +1
Text editing is a crucial task of modifying text to better align with user intents. However, existing text editing benchmark datasets contain only coarse-grained instructions and l…
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…
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…
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…
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…
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…
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski +2
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more pron…
Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarization
Haopeng Zhang, Jiawei Zhang
Unsupervised document summarization has re-acquired lots of attention in recent years thanks to its simplicity and data independence. In this paper, we propose a graph-based unsupe…
Graph-Bert: Only Attention is Needed for Learning Graph Representations
Jiawei Zhang, Haopeng Zhang, Congying Xia +1
The dominant graph neural networks (GNNs) over-rely on the graph links, several serious performance problems with which have been witnessed already, e.g., suspended animation probl…