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Found 5 papers, 1 papers with code
Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization
However, their application to chart-related tasks is not trivial as these tasks typically involve considering not only the underlying data but also the visual features in the chart image.
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
We extend SortedNet to generative NLP tasks, making large language models dynamic without any Pre-Training and by only replacing Standard Fine-Tuning (SFT) with Sorted Fine-Tuning (SoFT).
SortedNet, a Place for Every Network and Every Network in its Place: Towards a Generalized Solution for Training Many-in-One Neural Networks
Deep neural networks (DNNs) must cater to a variety of users with different performance needs and budgets, leading to the costly practice of training, storing, and maintaining numerous specific models.
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data.
Ranked #17 on
Chart Question Answering
on ChartQA
(using extra training data)
Chart Question Answering: State of the Art and Future Directions
Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights.
