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2025 SemEval SemEval 2025

Core Intelligence at SemEval-2025 Task 8: Multi-hop LLM Agent for Tabular Question Answering

Abstract

AbstractThis 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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

Authors

Maryna Chernyshevich

Topics

Artificial Intelligence > Core AI > Agent Systems Natural Language Processing > Applications > Question Answering Natural Language Processing > Resources & Methods > Large Language Models Artificial Intelligence > Core AI > Code Generation

Keywords

code generation multi-hop reasoning tabular question answering large language model answer validation large language model agent
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