@inproceedings{chernyshevich-2025-core,
title = "Core Intelligence at {S}em{E}val-2025 Task 8: Multi-hop {LLM} Agent for Tabular Question Answering",
author = "Chernyshevich, Maryna",
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://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.174/",
pages = "1313--1317",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes a multi-hop LLM agent for tabular question answering developed for SemEval-2025 Task 8 and ranked 6th with 87{\%} accuracy. Our approach combines proprietary LLM (ChatGPT-3.5-turbo) for code generation and open source LLM (Llama-3.2-3B) for answer validation."
}
Markdown (Informal)
[Core Intelligence at SemEval-2025 Task 8: Multi-hop LLM Agent for Tabular Question Answering](https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.174/) (Chernyshevich, SemEval 2025)
ACL