Search Results for author:
Found 5 papers, 2 papers with code
CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models
Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks. Despite the significant advancements in LLMs, their effective operation still relies heavily on human input to accurately guide the dialogue flow, with agent tuning being a crucial optimization technique that involves human adjustments to the model for better response to such guidance. Addressing this dependency, our work introduces the TinyAgent model, trained on a meticulously curated high-quality dataset.
RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models
This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs).
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation
Axie infinity is a complicated card game with a huge-scale action space.
Pneumothorax and chest tube classification on chest x-rays for detection of missed pneumothorax
Chest x-ray imaging is widely used for the diagnosis of pneumothorax and there has been significant interest in developing automated methods to assist in image interpretation.
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images
Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta.
