8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CL2024
CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models
Zi Gong, Hang Yu, Cong Liao +3
Multi-task learning (MTL) benefits the fine-tuning of large language models (LLMs) by providing a single model with improved performance and generalization ability across tasks, pr…
cs.SE2024★ 8 cited
MicroFuzz: An Efficient Fuzzing Framework for Microservices
Peng Di, Bingchang Liu, Yiyi Gao
This paper presents a novel fuzzing framework, called MicroFuzz, specifically designed for Microservices. Mocking-Assisted Seed Execution, Distributed Tracing, Seed Refresh and Pip…
cs.LG2023★ 1 cited
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Bingchang Liu, Chaoyu Chen, Cong Liao +9
Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previo…