4 citations · 5 across the 10 of their papers we have counts for
10 papers
Code as Representation: A Compilable Parsing Paradigm for Academic Documents
Rihui Jin, Jun Wang, chengyuan zhu +11
Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine use. For M…
ELAIPBench: A Benchmark for Expert-Level Artificial Intelligence Paper Understanding
Xinbang Dai, Huikang Hu, Yongrui Chen +6
While large language models (LLMs) excel at many domain-specific tasks, their ability to deeply comprehend and reason about full-length academic papers remains underexplored. Exist…
Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Yongrui Chen, Junhao He, Linbo Fu +10
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way…
Dual-Priv Pruning : Efficient Differential Private Fine-Tuning in Multimodal Large Language Models
Qianshan Wei, Jiaqi Li, Zihan You +9
Differential Privacy (DP) is a widely adopted technique, valued for its effectiveness in protecting the privacy of task-specific datasets, making it a critical tool for large langu…
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in Retrieval-Augmented Generation
Xinbang Dai, Huikang Hu, Yuncheng Hua +5
Retrieval-augmented generation (RAG) is a promising paradigm, yet its trustworthiness remains a critical concern. A major vulnerability arises prior to generation: models often fai…
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge
Yongrui Chen, Junhao He, Linbo Fu +10
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions (NLQs) by using structured sources such as tables, databases, and knowledge graphs in a unif…
Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study
Keyu Wang, Guilin Qi, Jiaqi Li +1
Large language models (LLMs) have shown significant achievements in solving a wide range of tasks. Recently, LLMs' capability to store, retrieve and infer with symbolic knowledge h…
Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
Jiaqi Li, Qianshan Wei, Chuanyi Zhang +5
Machine unlearning empowers individuals with the `right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains…
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
Jiaqi Li, Miaozeng Du, Chuanyi Zhang +6
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks…
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning
Jiaqi Li, Guilin Qi, Chuanyi Zhang +4
Multimodal movie genre classification has always been regarded as a demanding multi-label classification task due to the diversity of multimodal data such as posters, plot summarie…