2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 1 cited
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
Yiming Ju, Ziyi Ni, Xingrun Xing +4
Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to signif…
cs.CL2023★ 2 cited
KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models
Yiming Ju, Xingrun Xing, Zhixiong Zeng
Recently, Locate-Then-Edit paradigm has emerged as one of the main approaches in changing factual knowledge stored in the Language models. However, there is a lack of research on w…