3 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.AI2024
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains
Simeng Han, Aaron Yu, Rui Shen +13
Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales, which are not sufficie…
cs.SE2024★ 1 cited
Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models
Martin Riddell, Ansong Ni, Arman Cohan
While large language models have achieved remarkable performance on various code generation benchmarks, there have been growing concerns regarding potential contamination of these…
cs.CL2023★ 3 cited
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models
Ansong Ni, Pengcheng Yin, Yilun Zhao +11
Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a…