Search Results for author: Ahmed Masry

Found 7 papers, 3 papers with code

ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning

no code implementations14 Mar 2024 Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty

Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.

Instruction Following Question Answering

LongFin: A Multimodal Document Understanding Model for Long Financial Domain Documents

no code implementations26 Jan 2024 Ahmed Masry, Amir Hajian

We also propose the LongForms dataset, a comprehensive financial dataset that encapsulates several industrial challenges in financial documents.

document understanding

Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization

no code implementations17 Dec 2023 Xuan Long Do, Mohammad Hassanpour, Ahmed Masry, Parsa Kavehzadeh, Enamul Hoque, Shafiq Joty

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.

Chart Question Answering Question Answering

UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning

1 code implementation24 May 2023 Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, Shafiq Joty

Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data.

Ranked #15 on Chart Question Answering on ChartQA (using extra training data)

Chart Question Answering Question Answering

Chart Question Answering: State of the Art and Future Directions

no code implementations8 May 2022 Enamul Hoque, Parsa Kavehzadeh, Ahmed Masry

Information visualizations such as bar charts and line charts are very common for analyzing data and discovering critical insights.

Chart Question Answering Question Answering

ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning

1 code implementation Findings (ACL) 2022 Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, Enamul Hoque

To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions.

Chart Question Answering Logical Reasoning +1

Chart-to-Text: A Large-Scale Benchmark for Chart Summarization

2 code implementations ACL 2022 Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty

We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images.

Data-to-Text Generation Image Captioning

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