8 citations · 16 across the 3 of their papers we have counts for
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cs.CL2024★ 5 cited
LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning
Yahe Yang, Chunliang Tao, Xiaojing Fan
Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcem…
cs.CL2024★ 8 cited
Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation
Chunliang Tao, Xiaojing Fan, Yahe Yang
As Large Language Models (LLMs) advance in natural language processing, there is growing interest in leveraging their capabilities to simplify software interactions. In this paper,…
cs.CL2024★ 3 cited
Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness
Xiaojing Fan, Chunliang Tao
With the increasing demand for practical applications of Large Language Models (LLMs), many attention-efficient models have been developed to balance performance and computational…