2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CL2025
LLM-as-a-qualitative-judge: automating error analysis in natural language generation
Nadezhda Chirkova, Tunde Oluwaseyi Ajayi, Seth Aycock +4
Prompting large language models (LLMs) to evaluate generated text, known as LLM-as-a-judge, has become a standard evaluation approach in natural language generation (NLG), but is p…
cs.LG2023★ 2 cited
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code
Nadezhda Chirkova, Sergey Troshin
Recent works have widely adopted large language model pretraining for source code, suggested source code-specific pretraining objectives and investigated the applicability of vario…