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cs.CL2024
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…
cs.CL2024
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…
cs.CL2024
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…