Natural language question answering (QA) over structured data sources such as
tables and knowledge graphs (KGs) have been widely investigated, for example
with Large Language Models (LLMs). The main solutions include question to
formal query parsing and retrieval-based answer generation. However, current
methods of the former often suffer from weak generalization, failing to dealing
with multiple sources simultaneously, while the later is limited in
trustfulness. In this paper, we propose UnifiedTQA, a trustful QA framework
that can simultaneously support multiple types of structured data in a unified
way. To this end, it adopts an LLM-friendly and unified knowledge
representation method called Condition Graph (CG), and uses an LLM and
demonstration-based two-level method for CG querying. For enhancement, it is
also equipped with dynamic demonstration retrieval. We have evaluated
UnifiedTQA with 5 benchmarks covering 3 types of structured data. It
outperforms 2 existing unified structured data QA methods and in comparison
with the baselines that are specific to a data type, it achieves
state-of-the-art on 2 of them. Further more, we demonstrates potential of our
method for more general QA tasks, QA over mixed structured data and QA across
structured data.

提出了一种名为 UnifiedTQA 的可信赖的问答框架，它能同时支持多种类型的结构化数据，并在评估中优于现有方法，具有潜力用于更广泛的问答任务。