Team AIREV achieved notable success in table retrieval and question answering tasks, indicating advanced capabilities in handling financial documents.
The AIREV team participated in the NTCIR-18 U4 shared task, which comprises two subtasks, Table Retrieval (TR) and Table Question Answering (TQA), designed to evaluate and advance system capabilities for handling real-world financial documents. This paper reports our approach to solving two subtasks and discusses the experimental results. Our proposed approaches are primarily based on fine-tuning pre-trained LLMs on specific downstream tasks involving several key components, converting tabular form data to natural language representations, well-designed prompts, Bert-based re-ranking, and LLM-based retrieval. Our proposed approaches are placed in the second position in the leaderboard on both the TR and TQA subtasks, based on the performance compared to the other participant teams, demonstrating the effectiveness of our proposed method.
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Fan et al. (2025) studied this question.
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