1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Reassessing Layer Pruning in LLMs: New Insights and Methods
Yao Lu, Hao Cheng, Yujie Fang +6
Although large language models (LLMs) have achieved remarkable success across various domains, their considerable scale necessitates substantial computational resources, posing sig…
LLM Unlearning via Loss Adjustment with Only Forget Data
Yaxuan Wang, Jiaheng Wei, Chris Yuhao Liu +6
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations.…
Improving Data Efficiency via Curating LLM-Driven Rating Systems
Jinlong Pang, Jiaheng Wei, Ankit Parag Shah +6
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outp…
Harnessing Business and Media Insights with Large Language Models
Yujia Bao, Ankit Parag Shah, Neeru Narang +30
This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performan…