16 citations · 76 across the 23 of their papers we have counts for
24 papers
ForgeTrain: Forging Production-Grade Training Frameworks via Harness-Driven AI Development
Qingfeng He, Zhui Zhu, Shangzhan Li +15
Training large models still relies on general-purpose frameworks such as Megatron-LM, whose generality tax constrains scenario-specific optimization and adds runtime overhead throu…
AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code
Shangzhan Li, Xinyu Yin, Xuanyu Jin +8
Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware potential, developers often resor…
VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
Yaoxiang Wang, Qi Shi, ShangZhan Li +6
LLMs have recently demonstrated strong capabilities in automatic RTL code generation, achieving high syntactic and functional correctness. However, most methods focus on functional…
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Shangzhan Li, Zefan Wang, Ye He +8
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations thro…
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
Jianling Li, Shangzhan Li, Zhenye Gao +9
Triton, a high-level Python-like language designed for building efficient GPU kernels, is widely adopted in deep learning frameworks due to its portability, flexibility, and access…
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Sha Li, Ruining Zhao, Manling Li +3
Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a lar…
OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking
Xueqing Wu, Sha Li, Heng Ji
Open-vocabulary state tracking is a more practical version of state tracking that aims to track state changes of entities throughout a process without restricting the state space a…
Explanation Graph Generation via Generative Pre-training over Synthetic Graphs
Han Cui, Shangzhan Li, Yu Zhang +1
The generation of explanation graphs is a significant task that aims to produce explanation graphs in response to user input, revealing the internal reasoning process. This task is…
Non-Sequential Graph Script Induction via Multimedia Grounding
Yu Zhou, Sha Li, Manling Li +4
Online resources such as WikiHow compile a wide range of scripts for performing everyday tasks, which can assist models in learning to reason about procedures. However, the scripts…
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Zhenhailong Wang, Ansel Blume, Sha Li +5
Action knowledge involves the understanding of textual, visual, and temporal aspects of actions. We introduce the Action Dynamics Benchmark (ActionBench) containing two carefully d…
GLEN: General-Purpose Event Detection for Thousands of Types
Qiusi Zhan, Sha Li, Kathryn Conger +3
The progress of event extraction research has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a gene…
Human-in-the-Loop Schema Induction
Tianyi Zhang, Isaac Tham, Zhaoyi Hou +12
Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extract…
Open Relation and Event Type Discovery with Type Abstraction
Sha Li, Heng Ji, Jiawei Han
Conventional closed-world information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied t…
Open-Vocabulary Argument Role Prediction for Event Extraction
Yizhu Jiao, Sha Li, Yiqing Xie +3
The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies…
Code4Struct: Code Generation for Few-Shot Event Structure Prediction
Xingyao Wang, Sha Li, Heng Ji
Large Language Model (LLM) trained on a mixture of text and code has demonstrated impressive capability in translating natural language (NL) into structured code. We observe that s…
Dynamic Global Memory for Document-level Argument Extraction
Xinya Du, Sha Li, Heng Ji
Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.…
Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs
Sha Li, Mahdi Namazifar, Di Jin +4
Providing conversation models with background knowledge has been shown to make open-domain dialogues more informative and engaging. Existing models treat knowledge selection as a s…
Schema-Guided Event Graph Completion
Hongwei Wang, Zixuan Zhang, Sha Li +5
We tackle a new task, event graph completion, which aims to predict missing event nodes for event graphs. Existing link prediction or graph completion methods have difficulty deali…
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
Sha Li, Liyuan Liu, Yiqing Xie +2
We propose P4E, an identify-and-localize event detection framework that integrates the best of few-shot prompting and structured prediction. Our framework decomposes event detectio…
Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion
Yiqing Xie, Jiaming Shen, Sha Li +2
Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. Typical DocRE methods blindly take the full document as input, while…
Document-Level Event Argument Extraction by Conditional Generation
Sha Li, Heng Ji, Jiawei Han
Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information-seeking behavior and leads to incom…
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
Manling Li, Sha Li, Zhenhailong Wang +5
Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event sc…
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
Carl Yang, Jieyu Zhang, Haonan Wang +5
While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semant…
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
Yu Zhang, Frank F. Xu, Sha Li +4
GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search…