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cs.CL2025
ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs' Capability via Chart Editing
Xuanle Zhao, Xuexin Liu, Haoyue Yang +5
Although multimodal large language models (MLLMs) show promise in generating chart rendering code, editing charts via code presents a greater challenge. This task demands MLLMs to…
cs.CL2025★ 1 cited
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
Jianling Li, Shangzhan Li, Zhenye Gao +9
Triton, a high-level Python-like language designed for building efficient GPU kernels, is widely adopted in deep learning frameworks due to its portability, flexibility, and access…