Search Results for author:
Found 3 papers, 2 papers with code
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
To address this problem, this paper introduces Multi-scale Positional Encoding (Ms-PoE) which is a simple yet effective plug-and-play approach to enhance the capacity of LLMs to handle the relevant information located in the middle of the context, without fine-tuning or introducing any additional overhead.
LLaGA: Large Language and Graph Assistant
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis.
Explaining Neural Networks Semantically and Quantitatively
This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
