1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CR2026
Root-Cause-Driven Automated Vulnerability Repair
Hulin Wang, Zion Leonahenahe Basque, Jie Hu +13
Recent LLM-based systems have made automated vulnerability repair increasingly practical, but two challenges remain. First, without strong signals about where a bug originates, rep…
cs.CL2024★ 1 cited
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
cs.CL2024
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
Yu Yang, Siddhartha Mishra, Jeffrey N Chiang +1
Despite the effectiveness of data selection for large language models (LLMs) during pretraining and instruction fine-tuning phases, improving data efficiency in supervised fine-tun…