Search Results for author:
Found 8 papers, 8 papers with code
UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities
To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes.
CogCoM: Train Large Vision-Language Models Diving into Details through Chain of Manipulations
Vision-Language Models (VLMs) have demonstrated their widespread viability thanks to extensive training in aligning visual instructions to answers.
CogAgent: A Visual Language Model for GUI Agents
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #14 on
Visual Question Answering
on MM-Vet
CogVLM: Visual Expert for Pretrained Language Models
We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #4 on
Visual Question Answering (VQA)
on InfiMM-Eval
GPT Can Solve Mathematical Problems Without a Calculator
Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools.
Parameter-Efficient Tuning Makes a Good Classification Head
In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain.
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.
Ranked #5 on
Entity Alignment
on YAGO-WIKI50K
