2 citations · 2 across the 10 of their papers we have counts for
5 papers · 1 filter
Self-evolving Agents with reflective and memory-augmented abilities
Xuechen Liang, Yangfan He, Yinghui Xia +11
Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this…
LanguaShrink: Reducing Token Overhead with Psycholinguistics
Xuechen Liang, Meiling Tao, Yinghui Xia +3
As large language models (LLMs) improve their capabilities in handling complex tasks, the issues of computational cost and efficiency due to long prompts are becoming increasingly…
Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue System
Yuheng Feng, Yangfan He, Yinghui Xia +3
Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems…
AICoderEval: Improving AI Domain Code Generation of Large Language Models
Yinghui Xia, Yuyan Chen, Tianyu Shi +2
Automated code generation is a pivotal capability of large language models (LLMs). However, assessing this capability in real-world scenarios remains challenging. Previous methods…
CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models
Xuechen Liang, Yangfan He, Meiling Tao +5
Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significa…