@inproceedings{tiwari-aryal-2025-howard,
title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2025 Task 8: {D}eep{T}ab{C}oder - Code-based Retrieval and In-context Learning for Question-Answering over Tabular Data",
author = "Tiwari, Saharsha and
Aryal, Saurav",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.223/",
pages = "1702--1708",
ISBN = "979-8-89176-273-2",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Howard University-AI4PC at SemEval-2025 Task 8: DeepTabCoder - Code-based Retrieval and In-context Learning for Question-Answering over Tabular Data
%A Tiwari, Saharsha
%A Aryal, Saurav
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F tiwari-aryal-2025-howard
%X 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.
%U https://aclanthology.org/2025.semeval-1.223/
%P 1702-1708
Markdown (Informal)
[Howard University-AI4PC at SemEval-2025 Task 8: DeepTabCoder - Code-based Retrieval and In-context Learning for Question-Answering over Tabular Data](https://aclanthology.org/2025.semeval-1.223/) (Tiwari & Aryal, SemEval 2025)
ACL