4 citations · 4 across the 4 of their papers we have counts for
4 papers
Spectral Principal Paths: A Spectral Perspective on Linear Representation Formation in LLMs
Bowei Tian, Xuntao Lyu, Meng Liu +2
High-level representations have become a central focus in enhancing AI transparency and control, shifting attention from individual neurons or circuits to structured semantic direc…
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
Bowei Tian, Xuntao Lyu, Meng Liu +2
Representation Engineering (RepE) has emerged as a powerful paradigm for enhancing AI transparency by focusing on high-level representations rather than individual neurons or circu…
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
Ziyao Wang, Zheyu Shen, Yexiao He +4
The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Feder…
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Yexiao He, Ziyao Wang, Zheyu Shen +5
The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discover…