1 citations · 1 across the 11 of their papers we have counts for
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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…
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…
General Scales Unlock AI Evaluation with Explanatory and Predictive Power
Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed +23
Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activitie…
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…