Saharsha Tiwari

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2025

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Howard University-AI4PC at SemEval-2025 Task 8: DeepTabCoder - Code-based Retrieval and In-context Learning for Question-Answering over Tabular Data
Saharsha Tiwari | Saurav Aryal
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)

This paper presents our approach, named DeepTabCoder, to SemEval 2025 - Task 8: DataBench, which focuses on question-answering over tabular data. We utilize a code-based retrieval system combined with in-context learning, which generates and executes code to answer questions, leveraging DeepSeek-V3 for code generation. DeepTabCoder outperforms the baseline, achieving accuracies of 81.42% on the DataBench dataset and 80.46% on the DataBench Lite dataset.
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