2 citations · 4 across the 3 of their papers we have counts for
3 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.NE2024★ 1 cited
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Xingrun Xing, Zheng Zhang, Ziyi Ni +6
Towards energy-efficient artificial intelligence similar to the human brain, the bio-inspired spiking neural networks (SNNs) have advantages of biological plausibility, event-drive…
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…