most citedPost Turing: Mapping the landscape of LLM Evaluation

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

CleanComedy: Creating Friendly Humor through Generative Techniques

Dmitry Vikhorev, Daria Galimzianova, Svetlana Gorovaia +2

Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer…

cs.CL20241 cited

Toxicity of the Commons: Curating Open-Source Pre-Training Data

Catherine Arnett, Eliot Jones, Ivan P. Yamshchikov +1

Open-source large language models are becoming increasingly available and popular among researchers and practitioners. While significant progress has been made on open-weight model…

cs.CL2024

Vygotsky Distance: Measure for Benchmark Task Similarity

Maxim K. Surkov, Ivan P. Yamshchikov

Evaluation plays a significant role in modern natural language processing. Most modern NLP benchmarks consist of arbitrary sets of tasks that neither guarantee any generalization p…

cs.CL2024

Neural Machine Translation for Malayalam Paraphrase Generation

Christeena Varghese, Sergey Koshelev, Ivan P. Yamshchikov

This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) mod…

cs.CL20232 cited

Post Turing: Mapping the landscape of LLM Evaluation

Alexey Tikhonov, Ivan P. Yamshchikov

In the rapidly evolving landscape of Large Language Models (LLMs), introduction of well-defined and standardized evaluation methodologies remains a crucial challenge. This paper tr…