most citedML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CL2024

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs

Yuchen Fu, Zifeng Cheng, Zhiwei Jiang +4

Extracting sentence embeddings from large language models (LLMs) is a promising direction, as LLMs have demonstrated stronger semantic understanding capabilities. Previous studies…

cs.CL2023★ 2 cited

ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

Xiangru Tang, Yuliang Liu, Zefan Cai +21

Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming…