Given a table T in a database and a question Q in natural language, the table question answering (TQA) task aims to return an accurate answer to Q based on the content of T. Recent state-of-the-art solutions leverage large language models (LLMs) to obtain high-quality answers. However, most rely on proprietary, large-scale LLMs with costly API access, posing a significant financial barrier. This paper instead focuses on TQA with smaller, open-weight LLMs that can run on a desktop or laptop. This setting is challenging, as such LLMs typically have weaker capabilities than large proprietary models, leading to substantial performance degradation with existing methods. We observe that a key reason for this degradation is that prior approaches often require the LLM to solve a highly sophisticated task using long, complex prompts, which exceed the capabilities of small open-weight LLMs. Motivated by this observation, we present Orchestra, a multi-agent approach that unlocks the potential of accessible LLMs for high-quality, cost-effective TQA. Orchestra coordinates a group of LLM agents, each responsible for a relatively simple task, through a structured, layered workflow to solve complex TQA problems -- akin to an orchestra. By reducing the prompt complexity faced by each agent, Orchestra significantly improves output reliability. We implement Orchestra on top of AgentScope, an open-source multi-agent framework, and evaluate it on multiple TQA benchmarks using a wide range of open-weight LLMs. Experimental results show that Orchestra achieves strong performance even with small- to medium-sized models. For example, with Qwen2.5-14B, Orchestra reaches 72.1% accuracy on WikiTQ, approaching the best prior result of 75.3% achieved with GPT-4; with larger Qwen, Llama, or DeepSeek models, Orchestra outperforms all prior methods and establishes new state-of-the-art results across all benchmarks.

翻译：给定数据库中的表格T和自然语言问题Q，表格问答（TQA）任务旨在根据T的内容返回Q的准确答案。当前最先进的解决方案利用大语言模型（LLM）来获得高质量答案。然而，大多数方法依赖于需要付费API访问的专有大规模LLM，造成了显著的经济门槛。本文则关注于使用可在台式机或笔记本电脑上运行的、规模较小的开源权重LLM进行TQA。这一设定具有挑战性，因为此类LLM的能力通常弱于大型专有模型，导致现有方法性能大幅下降。我们观察到，性能下降的一个关键原因是先前的方法通常要求LLM使用冗长复杂的提示来解决高度复杂的任务，这超出了小型开源权重LLM的能力范围。受此观察启发，我们提出了Orchestra，一种多智能体方法，旨在释放可访问LLM在高质量、低成本TQA方面的潜力。Orchestra通过一个结构化的分层工作流程，协调一组LLM智能体（每个智能体负责一个相对简单的任务）来解决复杂的TQA问题——类似于一个管弦乐团的协作。通过降低每个智能体面临的提示复杂度，Orchestra显著提高了输出可靠性。我们在开源多智能体框架AgentScope之上实现了Orchestra，并使用多种开源权重LLM在多个TQA基准上对其进行了评估。实验结果表明，即使使用中小型模型，Orchestra也能实现强劲的性能。例如，使用Qwen2.5-14B时，Orchestra在WikiTQ上达到了72.1%的准确率，接近此前使用GPT-4获得的最佳结果75.3%；当使用更大的Qwen、Llama或DeepSeek模型时，Orchestra在所有基准测试中都超越了所有先前方法，并确立了新的最先进结果。