16 citations · 29 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 10 cited
Make Your LLM Fully Utilize the Context
Shengnan An, Zexiong Ma, Zeqi Lin +2
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-m…
cs.SE2024★ 16 cited
Compositional API Recommendation for Library-Oriented Code Generation
Zexiong Ma, Shengnan An, Bing Xie +1
Large language models (LLMs) have achieved exceptional performance in code generation. However, the performance remains unsatisfactory in generating library-oriented code, especial…
cs.LG2023★ 3 cited
Does Deep Learning Learn to Abstract? A Systematic Probing Framework
Shengnan An, Zeqi Lin, Bei Chen +3
Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.…