H - STAR : LLM -driven Hybrid SQL -Text Adaptive Reasoning on Tables (2025)

Abstract: Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data analysis. Existing methods employ either textual reasoning, which excels in semantic interpretation but struggles with mathematical operations, or symbolic reasoning, which handles computations well but lacks semantic understanding. This paper introduces a novel algorithm H-STAR that integrates both symbolic and semantic (textual) approaches in a two-stage process to address these limitations. H-STAR employs: (1) step-wise table extraction using ‘multi-view’ column retrieval followed by row extraction, and (2) adaptive reasoning that adapts reasoning strategies based on question types, utilizing semantic reasoning for direct lookup and complex lexical queries while augmenting textual reasoning with symbolic reasoning support for quantitative and logical tasks. Our extensive experiments demonstrate that H-STAR significantly outperforms state-of-the-art methods across three tabular question-answering (QA) and fact-verification datasets, underscoring its effectiveness and efficiency.

## introduction tabular data are among the most widely used formats for storing structured information in realworld applications. tabular reasoning presents an inherently challenging problem, requiring logical, mathematical, and textual reasoning over unstructured queries and structured tables @xcite . thus, understanding and inferring tabular data has become a significant area of research in natural language processing. tabular reasoning tasks (figure 1 ), such as table-based question answering @xcite and table-based fact verification @xcite , have been extensively explored in the past. q: ny americans did not qualify for playoffs in 1936/37 evidence: columns: [year, national cup]; rows: [1] a: false fact verification short-form qa long-form qa row_id year division playoffs national cup 0 1935/36 1 champion ? 1 1936/37 1 dnq champion ... ... ... ... ... 18 1953/54 1 champion champion 19 1954/55 1 no playoff ? row_id year division playoffs national cup q: how was the cup performance in 1936/37 and 1953/54? evidence: columns: [year, national cup]; rows: [1, 18] a: ny americans won the national cup in 1936/37 and 1953/54 q: when did ny americans win the cup after 1936? evidence: columns: [year, playoffs]; rows: [1,18] a: 1953/54 advancements in large language models (llms) have led to better performance across various tasks through carefully crafted prompts. in the domain of table reasoning, symbolic approaches such as program of thought @xcite and textual methods such as chain of thought (cot) @xcite have been explored. however, when applied individually (see figure 2 ) these methods often struggle due to the complexities imposed by the intricate mix of numerical, temporal, and textual data, coupled with complex table structures @xcite @xcite . textual reasoning excels in natural language understanding but often misinterprets table structures and struggles with quantitative reasoning. in contrast, sql-based approaches are strong in quantitative problem solving, but perform poorly on noisy or unstructured inputs @xcite . as a result, recent techniques (cheng q: did the new york americans win the national cup in 1936? a) sql based reasoning b) text based reasoning sql: select `national cup` from w where `year` = '1936/37'; champion the question asks to find whether new york americans win the cup in 1936.... in this paper, we introduce h-star which combines symbolic and textual reasoning abilities of llms, achieving the best of both worlds. h-star decomposes the table reasoning task into two subtasks: (a) table extraction and (b) adaptive reasoning. in the table extraction phase, we employ a step-by-step 'multi-view' chain, first identifying relevant columns using the original table and its transposed form. the filtered table is then used for row extraction, contrary to other approaches, which use the original table for both row and column selection @xcite . focusing on relevant table cells helps llms by providing the right context, thus reducing hallucinations in reasoning. we employ a combination of sql-based and text-based techniques in h-star's table extraction phase. in the adaptive reasoning phase, we use the language comprehension capabilities of llms to guide them with few-shot examples to decide when to support semantic methods with symbolic (sql) approaches. h-star employs semantic reasoning universally, using it exclusively for direct lookup, common sense, and complex lexical queries while using an additional sql step for quantitative, mathematical, and logical tasks. this strategy optimizes performance on diverse question types, supporting text comprehension with computation when required. we demonstrate the efficacy of h-star across three tabular benchmarks involving table qa and table fact verification. our experiments show that using the combined approach of h-star, aligned with multi-view extraction and adaptive reasoning outperforms all prior state-of-the-art approaches. our analysis demonstrates that h-star is robust and generalizable across llms, showcasing its superior reasoning capabilities across various tasks. our main contributions are as follows: (a) we introduce h-star, a two-step method unifying table extraction with adaptive reasoning to enhance performance on tabular reasoning. it is a hybrid approach that effectively integrates symbolic (sql logic) and semantic (textual) methods. (b) through extensive experiments, we show that h-star's hybrid approach surpasses state-of-theart methods that rely solely on either symbolic or semantic techniques, especially when reasoning over larger tables. (c) our ablation study and analysis highlight h-star's effectiveness in table extraction and reasoning across various models and datasets, underscoring the significance of each stage in enhancing overall performance. the code and additional resources are available at: https://hstar-llm.github.io/ . ## ablation study to evaluate the significance of the two primary stages in h-star: (a) table extraction and (b) adaptive reasoning, we conducted an ablation study, removing one stage at a time. for the first condition, the final adaptive reasoning stage was substituted with a basic chain-of-thought reasoning process while maintaining the table extraction stage. conversely, the second condition retained the adaptive reasoning stage but omitted the table extraction stage, specifically removing the row extraction and column extraction steps individually and then collectively. a detailed assessment of the contribution of each stage to the overall performance of the method is shown in ## key findings firstly, our evaluation demonstrates that our hybrid approach achieves substantial improvements, surpassing the performance of previous state-of-theart methods across various table reasoning tasks. secondly, the quantitative analysis demonstrates that our 'multi-view' approach extracts tables spe-cific to the query. furthermore, the qualitative analysis highlights fewer errors in our table extraction method compared to prior approaches, confirming a decrease in irrelevant information. thirdly, our analysis indicates consistent performance even with longer tables, emphasizing our method's effectiveness in accurately extracting relevant information by filtering out noise. lastly, the ablation study shows that decomposing the task into sub-tasks significantly enhances the overall performance, with each sub-task playing a crucial role in achieving superior results. moreover, it also highlights the constraints of relying solely on either text or sql approaches, which are effectively addressed by our h-star approach. together, these findings emphasize the substantial advantages of our hybrid and multi-view approach in addressing complex table reasoning tasks. ## related work table reasoning tasks require the ability to reason over unstructured queries and structured or semi-structured tables. traditional approaches like tapas @xcite , tapex @xcite , tabert @xcite , turl @xcite , pasta @xcite work on pretraining language models jointly on large-scale tabular and text data to reason in an end-to-end manner. advancements in llms, allow them to learn from in-context examples, reducing inference costs. text-to-sql @xcite and programof-thought @xcite use symbolic methods to solve table-based tasks via python/sql programs. textual-based reasoning techniques such as table -cot @xcite and tab-cot (ziqi and lu, 2023) extend prompting methods such as zeroand few-shot cot for tabular reasoning. the decomposition of problems into smaller and manageable tasks has proven effective in solving complex reasoning challenges @xcite . recent techniques in table reasoning follow this approach, either by breaking tasks into fixed sub-tasks @xcite @xcite or by employing iterative methods @xcite @xcite . binder @xcite is an sql-based approach that modifies sql statements to include llm api calls within sql statements. alter @xcite augments both the queries along with the table data. dater @xcite and tabsqlify (nahid and rafiei, 2024) are table decomposition methods that use semantic and symbolic reasoning, respectively. chain-of-table @xcite uses a textual reasoning approach to update tables iteratively before the final reasoning step. @xcite ) is an ensemble approach employing an answer selection mechanism that selects between text-to-sql and text-based models. the works most closely related to our approach are reactable @xcite and e5 ( @xcite . reactable extends the react framework @xcite to table reasoning by employing step-by-step reasoning, where it iteratively generates sub-tables using external tools like sql and python. similarly, e5 introduces a multi-step process for hierarchical table question answering, interpreting table structures, generating and executing code, and performing reasoning over the results to derive answers. in contrast, h-star employs a fixed two-stage pipeline for table extraction and reasoning, utilizing both symbolic and semantic approaches complementarily at each stage, resulting in improved performance. a comprehensive comparison of the methodological differences and their implications for performance is provided in appendix b. ## conclusion in this study, we present h-star, a novel method that effectively integrates semantic and symbolic approaches, demonstrating superior performance compared to existing methods in tasks involving table reasoning. our approach involves a two-step llm-driven process: firstly, employing 'multiview' table extraction to retrieve tables relevant to a query, and then, implementing adaptive reasoning to select the optimal reasoning strategy based on the input query. we address prior bottlenecks with efficient extraction and reasoning, leading to improved overall performance, particularly on longer tables. our results highlight the need to move beyond relying solely on either technique and demonstrate the effectiveness of an integrated approach that combines the advantages of both methods. future directions involve testing the adaptability of our methods to semi-structured, complex hierarchical, and relational tables. enhancing the reasoning process through techniques such as self-consistency and self-verification shows promising potential.