14 citations · 23 across the 10 of their papers we have counts for
4 papers · 1 filter
SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding
Sihang Li, Jin Huang, Jiaxi Zhuang +7
Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarka…
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
Jin Huang, Xingjian Zhang, Qiaozhu Mei +1
Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies de…
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
Lu Chen, Siyu Lou, Keyan Zhang +2
The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural net…
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
Jiaqi Ma, Xingjian Zhang, Hezheng Fan +6
Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader…