Search Results for author:
Found 6 papers, 5 papers with code
Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites
The fine-grained object attributes and behaviors non-existent in the image may still be generated but not measured by the current evaluation methods.
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
However, they struggle with semantically rich real-world entities due to limited structural information and fail to generalize to unseen entities.
Ranked #1 on
Link Prediction
on WN18RR
Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models
Results from the experiments demonstrate that there is a significant performance gap between the in-distribution (ID) and OOD settings for document images, and that fine-grained analysis of distribution shifts can reveal the brittle nature of existing pre-trained VDU models and OOD generalization algorithms.
T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering
To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with LLM signals.
ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction
To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples.
Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models
In this paper, we propose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific supervised and alignment-aware contrastive objective.
