Search Results for author:
Found 12 papers, 2 papers with code
Collaborative LLM Numerical Reasoning with Local Data Protection
Furthermore, our method improves accuracy by 16. 2% - 43. 6% while reducing data leakage by 2. 3% - 44. 6% compared to existing data protection approaches.
Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling
However, while language models can effectively leverage chain-of-thought prompting for zero or few-shot learning, similar prompting strategies are less effective for multi-modal LLMs due to modality gaps and task complexity.
A Systematic Survey of Automatic Prompt Optimization Techniques
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks.
VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration
Vision-Language Models (VLMs) have demonstrated impressive performance across a versatile set of tasks.
Customize Your Own Paired Data via Few-shot Way
Existing solutions to image editing tasks suffer from several issues.
Effectively Fine-tune to Improve Large Multimodal Models for Radiology Report Generation
Writing radiology reports from medical images requires a high level of domain expertise.
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis
Classification models learn to generalize the associations between data samples and their target classes.
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning
GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints.
Interpretable and Steerable Sequence Learning via Prototypes
The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data.
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer.
