111 DUT at S em E val-2025 Task 8:Hierarchical Chain-of-Thought Reasoning and Multi-Model Deliberation for Robust T able QA (2025)

Abstract: The proliferation of structured tabular data in domains like healthcare and finance has intensified the demand for precise table question answering, particularly for complex numerical reasoning and cross-domain generalization. Existing approaches struggle with implicit semantics and multi-step arithmetic operations. This paper presents our solution for SemEval-2025 task,including three synergistic components: (1) a Schema Profiler that extracts structural metadata via LLM-driven analysis and statistical validation, (2) a Hierarchical Chain-of-Thought module that decomposes questions into four stages(semantic anchoring, schema mapping, query synthesis, and self-correction)to ensure SQL validity, and (3) a Confidence-Accuracy Voting mechanism that resolves discrepancies across LLMs through weighted ensemble decisions. Our framework achieves scores of 81.23 on Databench and 81.99 on Databench_lite, ranking 6th and 5th respectively, demonstrating the effectiveness of structured metadata guidance and cross-model deliberation in complex TableQA scenarios.

## introduction in the era of digitization, structured data represented in tabular formats is ubiquitous across domains such as finance, healthcare, and scientific research. table question answering (tableqa), which aims to retrieve precise information from tables based on natural language queries, has emerged as a critical research direction. its applications range from database querying and spreadsheet automation to extracting insights from web tables or even image-based tabular data. despite its practical significance, the complexity of tableqa lies in effectively aligning natural language questions with the structural and semantic features of tables, especially when handling aggregation (e.g., "summarize sales by region"), comparison (e.g., "which * corresponding author product has the highest revenue"), and multi-hop reasoning (e.g., "find the second-largest budget department"). traditional approaches often rely on weakly supervised table parsers to extract relevant cells and apply predefined aggregation operators, which are limited in generalizability and scalability @xcite . recent advancements in large language models (llms) have revolutionized tableqa by enabling more flexible and context-aware reasoning. llms address tableqa challenges through two primary paradigms: in-context learning and textto-sql. these approaches leverage the models' ability to process structured data alongside freeform text, opening new possibilities for handling complex tabular reasoning tasks. the in-context learning paradigm integrates tabular data into carefully designed prompts, allowing models to generate answers in zero-shot or few-shot settings. for example, structured prompting strategies encode table headers, cell values, and structural metadata (e.g., row/column indices) into the input sequence, enhancing the model's ability to reason over numerical and hierarchical relationships @xcite . recent work further improves robustness through reasoning-enhanced prompting, where llms are guided to decompose questions into step-by-step sub-tasks (e.g., filtering, sorting, and aggregating) @xcite . notably, models like tapas @xcite and tabert @xcite demonstrate that pre-training on large-scale table-text pairs significantly enhances structural awareness, achieving state-of-the-art performance on benchmarks like wikitablequestions and wikisql. the text-to-sql approach translates natural language questions into executable sql queries, enabling direct database interactions. this task requires precise alignment between linguistic expressions (e.g., "senior employees") and database schemas (e.g., 'where @xmath0, while ac-counting for structural constraints such as primary/foreign keys and column types. recent studies leverage llms' code-generation capabilities to improve sql accuracy. for instance, din-sql @xcite decomposes complex queries into sub-problems solved by specialized agents, while resdsql @xcite employs a retrieval-augmented framework to align questions with schema elements. semeval-2025 task 8 tackles the challenge of answering diverse, real-world questions over largescale tabular datasets in domains such as healthcare and finance. existing methods face limitations in cross-domain generalization due to implicit semantics (e.g., medical jargon). they also struggle with complex numerical reasoning, including percentile calculations and multi-step arithmetic. to address these challenges, we propose a framework that combines structured schema analysis, hierarchical reasoning, and multi-model deliberation. our method employs a three-stage architecture: (1) a schema profiler that automatically extracts structural metadata through guided llm parsing and statistical verification, (2) a hierarchical chain-of-thought reasoning module that decomposes questions into semantic anchoring, schema mapping, query synthesis, and self-correction stages, and (3) a confidence-accuracy voting mechanism that resolves discrepancies across three llm agents through weighted ensemble deliberation. our proposed method ranks 6th on the databench dataset and 5th on the databench_lite dataset. ## related work the rapid evolution of table question answering has been significantly propelled by advances in large language models (llms) and their application to text-to-sql tasks. early work established the spider benchmark @xcite , a crossdomain dataset that remains a cornerstone for evaluating complex sql generation. building on this, picard was introduced @xcite , which integrates constrained decoding with pretrained models like t5 to ensure syntactically valid sql queries. the advent of powerful llms shifted the paradigm toward in-context learning, exemplified by din-sql @xcite , where gpt-4 iteratively decomposes questions into sub-tasks like schema linking and query refinement. concurrently, retrieval-augmented methods like resdsql @xcite dynamically align questions with database schemas to mitigate domain shift.meanwhile, it has been demonstrated that code-style prompts enable zero-shot sql generation in c3 @xcite . despite these innovations, challenges persist in handling implicit semantics, where domain-specific terms (e.g., medical abbreviations) require external knowledge, and context window constraints @xcite , which lead to truncation of large tables. recent efforts like cot-sql @xcite leverages chain-of-thought prompting to decompose multistep queries. ## system overview in this section, we will introduce the overall structure of our proposed system. our proposed system comprises three core modules that synergistically enhance table-based question answering through structured reasoning and ensemble learning. figure 1 illustrates the overall architecture of our proposed method. module 1: schema profiler: we first feed partial tabular data into a large language model (moonshot-v1) to extract critical schema information. this process automatically identifies field types, value distributions, and contextual relationships within the table structure. the derived metadata establishes a semantic foundation for subsequent processing stages. module 2: hierarchical chain-of-thought reasoning: we design a fourstage chain-of-thought (cot) prompting strategy that combines schema metadata with task-specific instructions. this enhanced prompt is then input into three different large language models to generate candidate sql queries. module 3: multi-model deliberation: to ensure robustness, we implement a deliberation mechanism that directly adopts answers when all models reach consensus. when discrepancies occur, the mechanism employs cross-model voting with mutual evaluation. the voting system weights the models' confidence scores and historical accuracy to resolve conflicts, ultimately selecting the most reliable answer through ensemble decision-making. this hierarchical architecture effectively balances schema comprehension, diverse reasoning patterns, and result verification, demonstrating strong performance on complex table qa scenarios. ## conclusion table question answering. we propose a three-stage framework integrating schema analysis, hierarchical reasoning, and multi-model deliberation. our approach leverages: (1) a schema profiler that extracts structural metadata via guided llm parsing, (2) a hierarchical chainof-thought module decomposing questions into four reasoning stages (semantic anchoring, schema mapping, query synthesis, self-correction), and (3) a confidence-accuracy voting mechanism harmonizing outputs from three llm agents through weighted ensemble decisions. our method achieves scores of 81.23 on databench and 81.99 on databench_lite, ranking 6th and 5th respectively. future work will focus on: (1) enhancing schema profiling with dynamic domain adaptation, (2) refining cot stages for multi-table joins, and (3) extending the deliberation mechanism to hybrid llm-symbolic architectures.