ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Semi-structured tables, widely used in real-world applications (e.g.,
financial reports, medical records, transactional orders), often involve
flexible and complex layouts (e.g., hierarchical headers and merged cells).
These tables generally rely on human analysts to interpret table layouts and
answer relevant natural language questions, which is costly and inefficient. To
automate the procedure, existing methods face significant challenges. First,
methods like NL2SQL require converting semi-structured tables into structured
ones, which often causes substantial information loss. Second, methods like
NL2Code and multi-modal LLM QA struggle to understand the complex layouts of
semi-structured tables and cannot accurately answer corresponding questions. To
this end, we propose ST-Raptor, a tree-based framework for semi-structured
table question answering using large language models. First, we introduce the
Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures
complex semi-structured table layouts, along with an effective algorithm for
constructing the tree. Second, we define a set of basic tree operations to
guide LLMs in executing common QA tasks. Given a user question, ST-Raptor
decomposes it into simpler sub-questions, generates corresponding tree
operation pipelines, and conducts operation-table alignment for accurate
pipeline execution. Third, we incorporate a two-stage verification mechanism:
forward validation checks the correctness of execution steps, while backward
validation evaluates answer reliability by reconstructing queries from
predicted answers. To benchmark the performance, we present SSTQA, a dataset of
764 questions over 102 real-world semi-structured tables. Experiments show that
ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code
is available at https://github.com/weAIDB/ST-Raptor.
68ad223786b21a0e2e358d9c
Zirui Tang
68ad223786b21a0e2e358d9d
Boyu Niu
68ad223786b21a0e2e358d9e
Xuanhe Zhou
68ad223786b21a0e2e358d9f
Boxiu Li
68ad223786b21a0e2e358da0
Wei Zhou
68ad223786b21a0e2e358da1
Jiannan Wang
68ad223786b21a0e2e358da2
Guoliang Li
68ad223786b21a0e2e358da3
Xinyi Zhang
68ad223786b21a0e2e358da4
Fan Wu
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
Semi-structured tables, widely used in real-world applications (e.g.,
financial reports, medical records, transactional orders), often involve
flexible and complex layouts (e.g., hierarchical headers and merged cells).
These tables generally rely on human analysts to interpret table layouts and
answer relevant natural language questions, which is costly and inefficient. To
automate the procedure, existing methods face significant challenges. First,
methods like NL2SQL require converting semi-structured tables into structured
ones, which often causes substantial information loss. Second, methods like
NL2Code and multi-modal LLM QA struggle to understand the complex layouts of
semi-structured tables and cannot accurately answer corresponding questions. To
this end, we propose ST-Raptor, a tree-based framework for semi-structured
table question answering using large language models. First, we introduce the
Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures
complex semi-structured table layouts, along with an effective algorithm for
constructing the tree. Second, we define a set of basic tree operations to
guide LLMs in executing common QA tasks. Given a user question, ST-Raptor
decomposes it into simpler sub-questions, generates corresponding tree
operation pipelines, and conducts operation-table alignment for accurate
pipeline execution. Third, we incorporate a two-stage verification mechanism:
forward validation checks the correctness of execution steps, while backward
validation evaluates answer reliability by reconstructing queries from
predicted answers. To benchmark the performance, we present SSTQA, a dataset of
764 questions over 102 real-world semi-structured tables. Experiments show that
ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code
is available at https://github.com/weAIDB/ST-Raptor.
ST-Raptor, a tree-based framework using large language models, addresses challenges in answering questions from semi-structured tables by introducing a Hierarchical Orthogonal Tree and a two-stage verification mechanism.
Hierarchical Orthogonal Tree tree-based framework large language models semi-structured tables tree operations query decomposition operation-table alignment forward validation backward validation SSTQA dataset