Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering
Wei Long Zhou, Mohsen Mesgar, Annemarie Friedrich, Heike Adel · 2025
Complex table question answering (TQA) aims to answer questions that require complex reasoning, such as multi-step or multicategory reasoning, over data represented in tabular form.Previous approaches demonstrate notable performance by leveraging either closed-source large language models (LLMs) or fine-tuned open-weight LLMs.However, fine-tuning LLMs requires high-quality training data, which is costly to obtain.The use of closed-source LLMs poses accessibility challenges and leads to reproducibility issues.In this paper, we propose Multi-Agent Collaboration with Tool use (MACT), a framework that requires neither fine-tuning nor closed-source models.In MACT, a planning agent and a coding agent that also make use of tools collaborate for TQA.MACT outperforms previous SoTA systems on three out of four benchmarks and performs comparably to the larger and more expensive closed-source model GPT-4 on two benchmarks, even when using only open-weight models without any fine-tuning.Our extensive analyses prove the effectiveness of MACT's multi-agent collaboration in TQA.We release our code publicly.1 1 https://github.com/boschresearch/MACTPlanning Agent Mp Q: Which country in Europe features the largest percentage change in export between 2021 and 2020? a 1 : Retrieval [Retrieve the export number for France and Germany.