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cs.CL2024
Discrete Subgraph Sampling for Interpretable Graph based Visual Question Answering
Pascal Tilli, Ngoc Thang Vu
Explainable artificial intelligence (XAI) aims to make machine learning models more transparent. While many approaches focus on generating explanations post-hoc, interpretable appr…
cs.CL2024
Prompting-based Synthetic Data Generation for Few-Shot Question Answering
Maximilian Schmidt, Andrea Bartezzaghi, Ngoc Thang Vu
Although language models (LMs) have boosted the performance of Question Answering, they still need plenty of data. Data annotation, in contrast, is a time-consuming process. This e…
cs.CL2024
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
Pascal Tilli, Ngoc Thang Vu
The large success of deep learning based methods in Visual Question Answering (VQA) has concurrently increased the demand for explainable methods. Most methods in Explainable Artif…