6 citations · 9 across the 5 of their papers we have counts for
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cs.SE2024★ 1 cited
Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code
Timur Galimzyanov, Sergey Titov, Yaroslav Golubev +1
This paper introduces the human-curated PandasPlotBench dataset, designed to evaluate language models' effectiveness as assistants in visual data exploration. Our benchmark focuses…
cs.SE2023★ 2 cited
Dynamic Retrieval-Augmented Generation
Anton Shapkin, Denis Litvinov, Yaroslav Zharov +3
Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hal…