3 papers
cs.CL2025
A Survey on Prompt Tuning
Zongqian Li, Yixuan Su, Nigel Collier
This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classif…
cs.CL2025
PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning
Zongqian Li, Yixuan Su, Nigel Collier
Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting large language models, yet existing approaches exhibit counter-intuitive phenomena: integrating router…
cs.CL2025
ReasonGraph: Visualisation of Reasoning Paths
Zongqian Li, Ehsan Shareghi, Nigel Collier
Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools. We present ReasonGraph, a web-bas…