TableMaster: A Recipe to Advance Table Understanding with Language Models
Authors: Lang Cao
Hanbing Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Table Understanding
Table Reasoning
Large Language Model
Natural Language Processing
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: TableMaster analyzes the challenges of table understanding with language models and provides a comprehensive recipe and framework to address them.

Abstract: Tables serve as a fundamental format for representing structured relational data. While current language models (LMs) excel at many text-based tasks, they still face challenges in table understanding due to the complex characteristics of tabular data, such as their structured nature. In this paper, we aim to enhance LMs for improved table understanding. We identify four key challenges: 1) difficulty in locating target data, 2) deficiency in table semantics, 3) numerical inaccuracies in textual reasoning, and 4) semantic inflexibility in symbolic reasoning. To address these issues, we propose TableMaster, a recipe and comprehensive framework that integrates multiple solutions to overcome these obstacles. TableMaster first extracts relevant table content and verbalizes it with enriched semantic context. Additionally, we introduce adaptive reasoning, a flexible approach that dynamically adjusts between textual and symbolic reasoning, tailoring the reasoning process to each query. Extensive analyses and experiments demonstrate our findings and the effectiveness of TableMaster. On the WikiTQ dataset, TableMaster achieves an accuracy of 78.13% using GPT-4o-mini, surpassing existing baselines. We hope this work will serve as a practical step toward more robust and reliable table understanding.

Published as a conference paper at ICLR 2026
TABLEMASTER: A RECIPE TO ADVANCE TABLE UNDERSTANDING WITH LANGUAGE MODELS
Lang Cao
Hanbing Liu
I University of Illinois Urbana-Champaign Tsinghua University
langcao26 Illinois.... .edu liuhb24@ma ils. www.tniinngaa.nm edu edu on
ABSTRACT
Tables serve as fundamental format for representing structured relational data. While current language models (LMs) excel many text-based tasks, they still face challenges in table understanding due to the complex characteristics of tab- ular data, such as their structured nature. In this paper, we aim to enhance LMS for improved table understanding. We identify four key challenges: 1) difficulty in locating target data, 2) deficiency in table semantics, 3) numerical inaccura- cies in textual reasoning, and 4) semantic inflexibility in symbolic reasoning. To address these issues, we propose TableMaster, a recipe and comprehensive frame- work that integrates multiple solutions to overcome these obstacles. TableMas- ter first extracts relevant table content and verbalizes it with enriched semantic context. Additionally, we introduce adaptive reasoning, flexible approach that dynamically adjusts between textual and symbolic reasoning. tailoring the rea- soning process each query. Extensive analyses and experiments demonstrate our findings and the effectiveness of TableMaster. On the WikiTQ dataset, Table- Master achieves an accuracy of 78.1395 13% using GPT-44-mini, surpassing existing baselines. We hope this work will serve as practical step toward more robust and reliable table understanding.
INTRODUCTION
"Data gains extraordinary power as transcends the simplicity of one dimension to embrace the richness of higher dimensions."
Tables are widely used daily life and across various fields, such as healthcare (Ghasemi & Amyot, 2016) and finance (Li et al., 2020; Yi et al., 2025), due their unique ability to efficiently represent two-dimensional relational data. crucial to process tabular data with both efficiency and ac- curacy. Recently, large language models (LLMs) (Gunasekar et al., 2023; OpenAl, 2024; Touvron et al.. 2023) have achieved significant progress in the field of natural language processing. They perform well in a wide range of downstream text-based tasks, including language understanding (Minaee et al., 2024; Zhu et al., 2024) and reasoning (Plaat et al., 2024). Naturally, language models (LMs) are increasingly being used to process and understand tabular data (Fang et al., 2024; Zhang et al., 2024b), enabling reasoning for downstream tasks such table-based question answering (Pasupat Liang, 2015) and table-based fact verification (Chen et al., 2020).
However, the data structure of tables inherently possess unique two-dimensional structure that contrasts with the linear text, which dominates the content in language model pretraining corpora. Most advanced LMs not specifically optimized for processing tabular data. While techniques such as chain-of-thought prompting (Wei et al., 2023) and other reasoning-enhane
methods (Yao al., 2023) have enabled LMs to perform satisfactorily in reasoning with linear text, significant room for improvement remains in table-based reasoning (Chen, 2023). notable gap persists in LMs' ability to fully understand tables and effectively reason with tabular data.
Many previous studies have aimed to improve the table understanding capabilities of LMs. One efficient approach is using prompting to adapt LMs for table understanding without requiring fine-

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Challenges
Proposed Solutions
Tabular Data Characteristies
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Figure 1: Overview of the challenges and proposed solutions in this work. Tabular data is inherently structured, dense, concise, and numerical. Based these characteristies, we identify four key challenges. To address them, we propose four targeted solutions. The gray arrows between the characteristics and challenges represent the potential causes of these challenges stemming from specific characteristics. Each proposed solution corresponds to the challenge presented on the left in the same row. TableMaster a unified recipe developed based on these findings.
tuning, making it applicable to any advanced LM. Recent studies primarily adopt two main strategies to enhance table understanding with LMs. The first strategy involves extracting a sub-table that con- tains relevant content from the original table to reduce the context size, thereby making it easier for LMs to comprehend. Examples include Dater (Ye et al., 2023) and Chain-of- Tabble (Wang et al., 2024), among others. The second strategy leverages SQL or Python programs augment numerical reasoning, locate target data, and enhance table understanding of numerical information, as demon- strated by Binder (Cheng et al., 2023) and LEVER (Ni al., 2023), etc. However, these studies primarily focus on single basic aspect to enhance the performance of LMs in table understand- ing or design complex methods with isolated strategies. There is currently an absence of work that provides a systematic and fundamental analysis of table understanding with language models and proposes comprehensive methods for its improvement.
In this paper, we first provide extensive experiments and discussions to identify the challenges in table understanding with language models. address these challenges, we then introduce Table- Master, recipe and comprehensive framework that integrates multiple solutions to tackle these issues effectively. In summary, this paper makes the following key contributions:
Challenges of Table Understanding. We observe that tabular data is inherently structured, dense, concise, and numerical. Through empirical analysis, we identify four challenges associated with LMs' table understanding: difficulty in locating target data, deficiency of table semantics, numeri- cal inaccuracies in textual reasoning, and semantic inflexibility in symbolic reasoning. (Section 3)
A Recipe for Table Understanding. To address these challenges, we propose targeted solutions: table-of-focus, table verhalization, program-aided reasoning, table normalization, and rext-guided symbolic reasoning. Building on these solutions, we introduce a framework as a unified recipe, TabteMaster. also incorporates Adaptive Reasoning (AR), a flexible approach that dynamically adjusts between textual and symbolic reasoning, tailoring the reasoning process to each query. (Section 4)
Extensive Experiments and Detailed Analyses. We conduct extensive experiments and provide in-depth analyses support our findings on table understanding with language models. Fur- thermore, we evaluate and demonstrate the superior performance of Table across three widely used table understanding datasets: WikiTQ, TabFact, and FetaQA. Notably, on the Wik- iTQ dataset, TableMaster achieves an accuracy of 78. 13% based on GPT-4o-mini, surpassing existing baselines. (Section 3, Section 5, and Appendix)
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RELATED WORK
Reasoning with Language Models. It has been observed that language models (LMs) can exhibit reasoning abilities when they are sufficiently large (Wei et al., 2022; Suzgun et al., 2022). LMs are now widely used for various reasoning tasks, such as question answering (Kamalloo et al., 2023), decision making (Yang et al., 2023), and mathematical reasoning (Ahn et al., 2024). At the inference stage, techniques such as chain-of-thought prompting (Wei et al., 2023) are used to trigger step-by- step reasoning processes and improve reasoning performance. Few-shot prompting (Brown et al., 2020), least-to-most prompting (Zhou et al., 2023), and tongram-of-thougeee prompting (Chen et al., 2023) have proven effective in specific scenarios. Methods like confsccnnssy (Wang et al., 2023b) and structuring the reasoning process in forms like trees (Yao et al., 2023) or graphs (Besta et al., 2024; Cao, 2024a) are also useful for more complex reasoning tasks. Recently, many works have focused on using reinforcement learning (Lightman et al., 2023; Uesato et al., 2022) to improve the reasoning abilities of LMs during training. Our work focuses on inference-time improvements and proposes a general framework applicable to all kinds of LMs for table understanding and reasoning.
Fine-Tuning LMs for Table Understanding. Several studies have focused on fine-tuning lan- guage models to enhance their understanding of tabular data. For example. based on the masked language modeling approach introduced in BERT (Devlin et al., 2019), models like TaPas (Herzig et al., 2020), Pasta (Gu et al., 2022), and TUTA (Wang et al., 2021) propose specialized pre-training methods to improve LMs' ability to process tables. Similarly, TAPEX (Liu al., 2022) pre-trains an encoder-decoder model to function as SQL executor, enabling better table comprehension. Re- cent advancements, such as TableLlama (Zhang et al., 2024a), TableGPT (Zha et al., 2023), and StructLLM (Zhuang et al., 2024), leverage open-sourced decoder-only models like Llama (Touvron et al., 2023) to pre-train larger models optimized for various downstream table-related tasks. For- mula Tuning (Fortune) (Cao al., 2025) is reinforcement learning approach that enables language models to perform symbolic table reasoning by deriving executable spreadsheet formulas.
Adapting LMs for Table Understanding Without Fine-Tuning. Other studies focus on adapting LMs to table-related tasks without requiring fine-tuning. For instance, Binder (Cheng et al., 2023), LEVER (Ni al., 2023), and PoTable (Mao al., 2024) generate SQL or Python programs, ex- tending the capabilities of LMs to analyze tabular data. Dater (Ye et al., 2023), TabSQLify (Nahid & Rafiei, 2024a), ReAc'abble (Zhang al., 2023), TAP4LLM (Sui et al., 2024), and Tree-of-Table (Ji et al., 2024) introduce different methods to construct sub-tables, modifying the tabular context for improved understanding. Chain-of-Table (Wang et al., 2024) generalizes various table opera- tions, dynamically generating reasoning chains to create sub-tables. MIX-SC (Liu et al., 2024b) employs table normalization and leverages self-consistency, combining results from Python agents and textual reasoning to enhance performance. SpreadsheetEncooder (Dong et al., 2024) is specif- ically designed to interpret tabular data within spreadsheet environments. Our work also follows this direction to focus on adapting LMs without fine-tuning. We identify key challenges in table un- derstanding and address them through our proposed method, which can be applied to any advanced LMs.
3 CHALLENGES IN TABLE UNDERSTANDING
As illustrated in Figure 1, we identify and analyze the challenges in table understanding with lan- guage models (LMs) through the experiments shown in Figure 2 and related discussions. Addition- ally, we propose targeted solutions address these challenges. The detailed settings of the challenge analysis experiment are provided in Appendix D)
Tabular Characteristics. Tabular data differs from regular text, which is typically linear and se- quential, due to its structured nature. Although tabular data can be represented as sequential text, it is fundamentally a two-dimensional array of cells. Each cell primarily contains text, but the cells are interconnected and share relationships with one another. Typically, cells within the same column represent the same feature or type, while cells in the same row correspond to a single data instance. Tables are highly efficient for data representation, often containing a large amount of information, making them inherently data- intensive. Moreover, the text in tables is typically concise, consisting of simple words and phrases rather than continuous sentences, leading sparse semantic context.
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Figure 2: Experimental analysis of challenges in table understanding with language models. (a) Impact table size on task difficulty. (b) Effect of verbalized tables with enriched semantic context. (c) Performance comparison of different reasoning methods on calculation-required versus non- calculation questions. (d) Performance differences when processing normalized versus noisy tables.
Lastly, tables frequently include substantial amounts of numerical data, such as dates, times, scores, and measurements, which often require specialized processing.
3.1 DIFFICULTY IN LOCATING TARGET DATA
When LMs encounter tabular data, they often struggle to locate the target data relevant to given query, leading to misunderstandings This challenge arises because tabular data is inherently data- intensive, typically containing large volumes of information. Additionally, the structured nature of tabular data makes it challenging for LMs to interpret individual cell contents within the broader context of headers and other structural information. This issue can lead to long-contes hallucination (Huang et al., 2024). Moreover, LMs are prone to neglecting information in the middle the context (Liu et al., 2024a), making it even harder to locate target data and further impairing their overall comprehension of the table. (Figure CC)
As shown in Figure 2(a), present the changes in table understanding accuracy across four dif- ferent table size metrics: row count, column count, area size, and token count, ranging from small to extra-large tables. Row count represents the number of data entries, while column count reflects the number of dimensions or attributes per entry. Area size is the product of row count and column count, and token count refers table sizes from the perspective of LMs. All figures indicate that, gardless of the model used, overall performance tends to decline as table size increases. For weaker LMs, the performance drop more pronounced.
To address this, we propose let LMs focusing on specific parts of the table by explicitly constructing a focused sub-table that includes only the relevant information needed for the given context. We define this as the table-of-focus. By narrowing the scope, table understanding becomes significantly easier, which aligns with both our previous findings and intuition. (Figure 1 SS)
3.2 TABLE SEMANTIC DEFICIENCY
Tabular data is typically concise, with most cells containing simple words or phrases. Additionally, for each data entry in row, some descriptive information may reside outside the row, such as in the top header or other structural elements. Understanding a cell isolation is challenging and often requires deeper comprehension of the structural relationships within the table. This leads to the problem of sparse semantic context, which fundamentally different from the rich semantic con- text found in most data used during LMs' pretraining (Dong et al., 2022). The semantic deficiency in tables makes difficult for LMs to effectively understand and process tabular data. (Figure C2)
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As shown in Figure 2(b), the Table represents the case where the LM is provided only with the table input, while the Table+ Verbal indicates the table along with an additional description, which refer to verbalized table. This description is generated by the LMs themselves, whereas verbal plus refers to a description produced by more advanced LMs, which can be considered a ground-truth. We observe that verbalization helps LMs perform better certain tables, leading to slight overall performance improvement. This effect is more pronounced in weaker LMs, resulting in 1.5% increase in accuracy. Additionally, the quality of the description plays crucial role in improvement.
To address this issue, we propose a solution where tables are first verbalized into sequential, natural text as a description and then provided to LMs alongside the original table before they directly tackle table-related tasks. It is similar to table2text (Parikh et al., 2020). This transformation enriches the semantic context, making the data more aligned with the LMs' pretraining, thereby enhancing their ability to effectively understand and process tabular data. (Figure 1 -S2)
3.3 NUMERICAL INACCURACY IN TEXTUAL REASONING
Tabular data often contains numerical values, such as dates, times, scores, and other recorded num- bers, and is typically intensive. However, when LMs used to process numerical data in textual reasoning, they often face significant limitations. LMs are prone to arithmetic calculation errors, es- pecially when dealing with large numbers. LMs are also inefficient at handling iterative processes, particularly when the number of iteration steps is large (Chen et al., 2023). (Figure
C3)
As shown in Figure 2(c), questions that do not require calculations relatively easier, allowing tex- tual reasoning to achieve a strong performance of 72.4%. However, when calculations are required, performance drops significantly, falling below that of the enhanced symbolic reasoning introduced later. Specifically, textual reasoning suffers 20.1 % decline, whereas enhanced symbolic reasoning experiences a more moderate drop of 7.6%.
Symbolic methods offer a promising solution to these challenges and have been explored extensively in prior research (Cheng et al., 2023; Ni et al., 2023; Mao et al., 2024). Using symbolic tools, such as SQL or Python programs in combination with LMs, provides an effective approach to handling numerical data tabular formats. (Figure SS3)
3.4 SEMANTIC INFLEXIBILITY IN SYMBOLIC REASONING
Symbolic methods excel at arithmetic calculations. However, when prompting LMs to generate code for program of thought reasoning, the performance is suboptimal. Instead of truly understand- ing the context and generating problem-solving code, LMs often rely on memorized code from the pretraining stage (Yang et al., 2024). We refer to this limitation semantic inflexibility. In ta- ble understanding, this challenge is exacerbated by the table's 's complex structure and concise text content. In real-world scenarios, noisy tables further hinder LMs' symbolic reasoning capabilities. Consequently, while symbolic reasoning with numerical data is highly accurate, the generated code may be incorrect due issues in program logic or data handling, leading errors unintended results. (Figure 1 C4)
As shown in Figure 2(c), basic symbolic reasoning performs worse overall, regardless of whether calculations are required. It indicates that basic symbolic reasoning with current LMs is ineffective. Furthermore, as illustrated in Figure 2(d), when processing the same content in a noisy format, symbolic reasoning suffers a larger performance drop of 31.8%, compared to 20.5% decline for textual reasoning. This highlights the semantic inflexibility of symbolic reasoning when handling noisy tables.
To address this, we first normalize the table structure and content, ensuring that each column follows a consistent format. We then propose a solution where LMs first engage in textual reasoning before generating symbolic reasoning programs. This preliminary textual reasoning step serves as a guide for subsequent symbolic reasoning, improving alignment with the task context. Our approach can be seen as encouraging LMs to think more thoroughly before reasoning, aligning with techniques like plan-and -lan-and-solve (Wang al., 2023a). By incorporating textual reasoning as foundation, enhance the accuracy and contextual relevance of symbolic reasoning. As demonstrated in Figure 2(c). this method achieves higher accuracy of 59.1 % for calculation-requiree questions. (Figure 1 S4)
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Table Structure Understanding
2. Table Content Understanding
3. Table Reasoning for
Construct Table of Foous
Table
Tesua Beane-n
Adaplive Reasoning
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Figure 3: The framework TableMaster. comprises three stages: (1) table structure understand- ing, where the table's structure is analyzed, and a cable-of-focur is constructed through row and column lookup: (2) table content understanding, where the table-of-focus is reconstructed based on the question, and its information is verbalized to enhance the semantic context; and (3) table reason- ing for question answering, where an adaptive reasoning strategy determines whether to use textual reasoning or text-gut sext-guided symbolic reasoning to derive the final answer. The dashed arrows indicate optional workflows, such as the table-of-fooce re-construction and incorporating text- exxt-guided sym- bolic reasoning.
4 TableMaster: A RECIPE FOR TABLE UNDERSTANDING
Based on findings in Section 3, we introduce a recipe and comprehensive framework, TableMas- ter, as shown Figure 3. integrates the propose solution proposed in Section 3 into unified recipe for table understanding. The framework encompasses three key processes: Table Structure Understanding, Table Content Understanding, and Table Reasoning for QA. All notations are list at Appendix Q
4.1 TASK FORMULATION
In table understanding, the objective is to determine an answer A given a table T and a question or statement Q related to it. The table T' represented two-dimensional array of cells,
C1,1
C1 C2
C
Tmx
where Ct,, denotes the cell in the i-th row and j-th column, with the table consisting of m rows and columns. In table-based question answering tasks, Q represents question, and A is the expected answer natural language. contents, and A E (True, False) is Boolean value indicating whether the statement is correct. Therefore, the goal is to develop a system F that can predict the answer accurately based on the table and the given question statement, formalized as F(T, Q) A.
table-based fact verification tasks, Q is statement about the table's
4.2 TABLE STRUCTURE UNDERSTANDING
The goal of table structure understanding is to analyze the table's structure and construct a Table-of- Focus that contains relevant content for the given question. This process reduces context length and simplifies the table as much as possible.
To enhance the efficiency of the framework. we introduce the table peek technique. For structure extraction and certain operations, it is often unnecessary to process the entire table; instead, inspect- ing only the top rows sufficient. Given peek size k; the original table T xmxn is transformed into peek table Taxn. where all columns are retained, but the table is truncated to the first & rows.
Given a wild table TW, we first normalize it. We begin by determining whether the table is in row-major column-major format. If it is in column-major format, transpose it using Transpose( Next, we normalize and clean all columns containing numerical informa- tion. ensuring consistency in formats such dates and numerical values, making them directly processable in bulk by a program. After this normalization process, we obtain the normalized table TN

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We begin by extracting the top headers H and the key column. The top headers used for column lookup, while the key column serves as the subject or unique identifier for each row. Next, we prompt LMs to perform column lookup and row lookup identify the relevant rows and columns required for the task. Specifically, for column lookup, we first define the set of candidate columns as C Rank H). LMs will also rank all candidates based on their relevance to the question. We then prompt the LMs to select ó relevant columns based on a given question Q:
C Column Lookup(TT Q),
where C fcz E H) and |C b. For row lookup, instruct the LMs generate SQL. query to efficiently filter and select a relevant rows R:
= Row Lookup(TT
[Q)-
Using the identified rows and columns, construct the initial table-of-focus: T Table Construction(TT* C", R),
which contains only the filtered information necessary for the task.
4.3 TABLE CONTENT UNDERSTANDING
The goal of table content understanding is enrich the semantic context of the table.
Studies have shown that LMs can assess whether sufficient information is available to answer a question (Cao, 2024b; Yin et al., 2023). We first prompt the LMs estimate whether the constructed Table-of-Focus Th axb. containing C",, provides enough information to answer the given question Q. not, additional column attributes from the candidate column set C are incrementally added from the ranked candidate headers until sufficient information is available or all relevant top headers have been utilized. Subsequently, total of a' columns from C are selected for further reasoning. We use re-construction to mitigate information loss during the table-of-foone construction process. The detailed re-construction algorithm can be found in Appendix J.
Once the information sufficiency check is passed, verbalize the table into natural language, adding descriptions to enrich the semantic context and producing a verbalized table:
T Verbalization((T...r
This verbalized table is represented as sequential natural language text T essentially rather than structured table, preserving rich semantic context while maintaining a concise size. This transfor- mation enhances information density, further facilitating the LMs' reasoning for the given question.
4.4 TABLE REASONING FOR QUESTION ANSWERING
The goal of this stage to answer table-related questions by understanding the table precisely and calculating accurately.
We employ an adaptive reasoning approach. First, we prompt the LMs to determine the most appropriate reasoning strategy S for the given task. In the instruction, for small tables or those without numerical data, the LMs are allowed to perform textual reasoning directly to derive the final result. For larger tables those containing numerical data, symbolic reasoning with programmatic execution is selected.
= Strategy Assessmen((T", TTT Q),
where S (T.S) represents the chosen reasoning strategy, with denoting textual reasoning and S denoting symbolic reasoning.
In symbolic reasoning, we first prompt the LMs to perform textual reasoning to generate guidance G without providing the final result. This intermediate reasoning step is then used as input for symbolic reasoning, transitioning to text-guided symbolic reasoning approach using programmatic methods. This adaptive method dynamically adjusts based the table's size, complexity, and the nature of the question, ensuring accurate and reliable results.
A "hhain-of-Thougg((("" TT. IT,QQ). PProgrrmm of Thought(T TTTTTTTT .T..111110 G)), i
=
where chain-of-then tf-thughtt and program-ot soiimn-f--hhee thought are two prompting techniques, P represents a Python SQL program executor, A is the final answer for the current table understanding task.
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Table Performance comparison between TableMaster and previous work on WikiTQ and TabFact. The values in the table represent accuracy (%). The best result is bold, the second-best result is underlined, and the improvement over the previous best result is highlighted that the result values were not reported in the related papers. For all models in the table, results are obtained from a single inference run without any voting. Our method outperforms all other methods across both datasets and different language models.
green. '.' indicates
Method
WHLTQ
TabFact
gpt-3.-turbbo..mmg
upt--tommini.an
Llama-3.1un
gpt-3.5-turboo
spl-4o-mini..o
Lama--31u
Tex--o-SQU, Rajkunar et (2022)
$2.90
64.7
And-to-Fid QA Wing (2024)
$1.84
70.45
Fow-Shot QA Wang
(2024)
52.56
71.54
Chain-of-Thooe Thought
(2024)
ReAcTable Zhang et (2022))
12.50
Binder Chong al.. (2025)
56.74
58. 16
50.51
79.17
84.63
78, 16
Dater Ye (2023)
52.81
58.33
43.53
78.01
80.98
81.57
TabQQLify Nahid Radiel (2024a)
64.70
57.02
55.78
78.75
70.70
Chain-of- Table Wang et all (2024)
59.94
55.60
62.
80.20
84.24
85.62
Tree-of-Table et al. (2024)
61.11
81.92
PoTable Mao et (2024)
64.73
65.56
88.93
87.06
(astleMaster)
(+3.51)
(+13.40)
(+12.39)
(+1.73)
(+.119))
(+4.10)
5
EXPERIMENTS
5.1 SETTINGS
We conduct extensive experiments to evaluate the performance TableMaster. Specifically, we as- sess its effectiveness actoss three different table understanding datasets: WikiTQ (Pasupat & Liang, 2015) (table-based question answering). TabFact (Chen et al., 2020) taaeee-ased fact verification), and FetaQA (Nan et al., 2022) (table-based free-form question answering). For WikiTQ and Tab- Fact, following previous work (Wang et al., 2024; Liu et al., 2024b), we use exact match accuracy as the evaluation metrie. For FetaQA, evaluate performance using BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004) scores. We also conduct experiments on HiTab (Cheng et al., 2022) and FinQA (Chen al., 2021b). Tables encoded in Markdown format before being input into language models, with or without addresses, depending on the specific case N.2.
Our experiments utilize OpenAl models hosted on Microsoft Azure. Unless otherwise stated, we set the temperature to 0 to ensure stable output while keeping all other hyperparameters at their default values. The models used in our evaluation include gpt-4o (gpt-4o-0806), gpt-4o-mini (gpt- 4o-mini-0718), gpt-3.5-turbo (gpr-35-turrbo-02555 ol (ol-preview-0912).. and ol-mini (ol-mini- 0912). Additionally, we evaluate our methods on open-sourced Llama-3.1-70 (Llama-3. 3-700B Instruct). For comparison, we select several strong baselines, including both classic and state-of- the-art methods such as Binder (Cheng et al., 2023). Dater (Ye et al., 2023), and Chain-of Table (Wang et al., 2024). Performance results for other methods not in this work are cited directly from their original or related papers, with sources indicated alongside the method names in the results table.
Further analysis and additional experiments on TableMaster can be found the Appendix. The prompts used in TableMaster be found in Appendix O while other prompts used in this work are provided in Appendix P.
5.2 MAIN RESULTS
As shown in Table 1, our TableMaster approach consistently achieves the highest performance across both WikiTQ and TabFact under different backbone models (gpt-3.5-turbo, gpt-4o-mini, and Llama-3. 3.1--B... On WikiTQ, TableMaster outperforms the strongest baselines by +3.51, +13.40, and +12.39 points, respectively. A similar trend is observed on TabFact, with improvements of +1.73, +1.19, and +4.10 points, demonstrating the robustness of our method across diverse large language models. Results the FetaQA, HiTab, FinQA dataset provided in Appendix E. E.2, E.3. These results confirm that TableMaster not only generalizes well across different base language models but also significantly enhances table understanding and reasoning in complex QA tasks.
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Notably, methods such Binder, Dater, TabSQLify, and Chain-of-Tablle exhibit subpar performance with gpt-4o-mini, in some cases performing worse than with gpt-3.5-turbe Our empirical analysis suggests that these methods primarily rely symbolic approaches to construct subtables, which often fail to leverage the strengths of chain-of-thought reasoning in textual contexts. This limita- tion underscores the necessity of integrating advanced textual reasoning strategies, effectively demonstrated by our TableMaster approach.
5.3
ABLATION STUDY
To analyze the contribution of each component in TableMaster, conduct an ablation study WikiTQ and TabFact. Table 2 presents the results, and the performance drop from the full model highlighted in red. The results demonstrate that removing any component leads decrease in accuracy, confirming the importance of each module in the overall framework.
Structure. The structure understand- ing components play a crucial role in table comprehension. Removing structure extraction results in a no- table accuracy drop of 3.38% on Wik- iQQ and 1.14% on TabFact, indicat- ing that explicitly extracting the ta- ble's structure is essential for effee- tive reasoning, as failing to do so can lead to errors in subsequent steps. Among lookup strategies, removing row lookup leads to 1.54% de- crease in WikiTQ accuracy, whereas removing column lookup results in smaller drop of 1.13%. This suggests that row-based information retrieval is more critical than column-based lookup, as large tables typically con- tain a greater number of rows. Addi- tionally, removing the table-of-focus reduces performance by 1.73% on WikiTQ and 0.79% on TabFact, fur- ther emphasizing its important role in structuring relevant table content to extract key information for reasoning.
Table 2: Ablation results on WikiTQ and TabFact. The val-
ues in the table represent accuracy (%), with v indicating the performance drop. The red text highlights the drop mag- nitude. Removing any component from TableMaster results in a decrease in performance.
Method
WikiTQ
TabFact
Table (gpt-4o-mini)
78.13
90.12
Structure
Structure Extraction
74.75
(-3.38)
88.98
(-1.14)
w/o Column Lookup
77.00
(-1.13)
90.51
(-0.40)
w/o Row Lookup
76.59
(-1.54)
89.23
(-0.89)
w/o Table of Focus
76.40
(-1.73)
89.33
(-0.79)
Content
w/o Re-Construction
75.55
(-2.58)
89.72
(-0.40)
Verhalization
75.78
(-2.35)
89.23
(-0.89)
Reasoning
w/o Textual Reasoning
73.85
(-4.28)
88.39
(-1.73)
w/o Symbolic Reasoning
76.10
(-2.03)
89.18
(-0.94)
w/o Textual Guidance
75.21
(-2.92)
89.67
(-0.44)
Content. Table content understanding also significantly influences performance. Eliminating construction, which iteratively refines the Table-of- -ocus based on the question, results 2.58% accuracy drop on WikiTQ and 0.40% on TabFact, highlighting the importance of this process. Sim- ilarly, removing table verbalization, which enriches the semantic context of the table by adding de- scriptive elements, leads to 2.35% decrease in WikiTQ accuracy. However, its impact TabFact is minimal (0.23% drop), suggesting that verbalization becomes even more beneficial for complex table understanding tasks.
Reasoning. The reasoning stage exhibits the most significant performance drop when removed. Removing textual reasoning leads to the largest accuracy decline, with 4.28% drop on WikiTQ and 1.73% on TabFact, underscoring its necessity for complex reasoning tasks. Similarly, remov- ing symbolic reasoning results 2.03% and 0.79% drop on WikiTQ and TabFact, respectively, demonstrating that symbolic reasoning enhances numerical and structured table interpretations. Fi- nally, removing textual guidance, which improves the semantic flexibility of symbolic reasoning, reduces accuracy by 2.92% on WikiTQ and 0.44% on TabFact. This highlights that textual guid- ance is particularly beneficial and important in symbolic reasoning by ensuring aligmment with the problem context. More analysis of adaptive reasoning can be found at Appendix I..

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Published as a conference paper at ICLR 2026
6 CONCLUSION
In this paper; we explore table understanding with language models. Given the characteristics of tabular data, we identify key challenges in table understanding. To overcome these challenges, we propose TableMaster, a recipe and comprehensive framework that integrates multiple solutions. Extensive analyses and experiments demonstrate our findings and the effectiveness of TabteMaster. In the future, we plan to extend and refine the framework to improve its performance across diverse practical applications, where discussed in Appendix B.
ACKNOWLEDGMENTS
This work was completed during my internship in the gap year prior to starting my PhD and was motivated by core product needs within the company. I sincerely thank the organization for the resources provided, as well as all the friends and colleagues who supported me during this period.
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Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023.
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Yunhu Ye, Binyuan Hui, Min Yang. Binhua Li, Fei Huang, and Yongbin Li. Large language models are versatile decomposers: Decompose evidence and questions for table-based reasoning, 2023.
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Deyin Yi, Yihao Liu, Lang Cao, Mengyu Zhou, Haoyu Dong, Shi Han, and Dongmei Zhang. Tablepilot: Recommending hueferereffereed tabular data analysis with large language models. arXiv preprint arXiv: 1326., 2025.
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. Do large language models know what they don't know? In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.). Findings of the Association for Computational Linguistics: ACL 2023, pp. 8653- 8665, Toronto, Canada, July 2023. Association for Computational Linguistics. doi: 10.18653/ v1/2023. find33..iniing-al.551... URL https:: //cll antho wothology.co org/2023. ftee233:fnnniggg findings-acl. 551.
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Contents of Appendix
Ethics Statement
18
B Limitations, Extendability, and Future Works
18
B.1 Technical Refinement
18
B.2 Downstream Applications
19
Datasets Used for Evaluation
19
Detailed Settings of Challenge Analysis Experiments
19
Extended Experiments on Additional Table Understanding Benchmarks
20
Evaluation on Free-form QA with the FetaQA Dataset
20
E.2 Evaluation on Hierarchical Tables with the HiTab Dataset
22
E.3 Evaluation on Numerical Reasoning with the FinQA Dataset
22
Table Understanding Baselines
22
Performance Analysis Under Different Table Sizes
23
H Performance Analysis Under Different Table Peek Sizes
25
I Efficiency Analysis of Table
26
1.1 Theoretical Analysis...
26
1.2 Empirical Analysis
26
J Detailed Algorithm of Table-of-Focus Re-Construction
27
Analysis of Table-of-Focus Re-Construction
28
Analysis of Adaptive Reasoning
29
Information Missing and Table Reasoning with Full Table
30
Case Study
N.1 Case Study Table Verbalization
31
N.2 Case Study of TableMaster
32
Prompt Design in TableMaster
34
P Prompts Used in Analysis Experiments
40
Notation Table
45
R The Use of Large Language Models (LLMss)
46
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A ETHICS STATEMENT
TableMaster introduces a general-purpose. modular framework that improves the ability of language models (LMs) to understand and reason over tabular data. Its applications span domains such as business intelligence, scientific reporting, education, and healthcare, where structured data plays critical role. By enabling adaptive reasoning across textual and symbolic paradigms, TableMaster improves both accuracy and transparency in question answering, verification, and analysis over tables. These advances may lead to better decision-suppot systems and streamlined human collaboration in spreadsheet-heavy workflows.
Despite these benefits, there are potential risks. As TableMaster automates reasoning over tabular content, it could be misused to generate misleading analyses automate decisions without adequate human oversight. Furthermore, inaccurate reasoning, especially when symbolic operations are ap- plied incorrectly, could result in flawed conclusions financial misjudgments. Biases in training data might also manifest in generated answers or program logic. In addition, as the framework relies on LLMs' capabilities, disparities across languages, domains, or spreadsheet conventions may lead to uneven performance, potentially disadvantaging users in low-resource settings.
To mitigate these concerns, we propose several safeguards. First, outputs involving symbolic rea- soning should be verified via deterministic execution (e.g., code validation or unit tests) before downstream use. Second, we encourage model evaluation on a diverse range real-world tables, including messy, hierarchical, multilingual formats. Third, human oversight is recommended in high-stakes applications, especially when deployed in financial, legal, or healthcare settings. Fourth, interpretability tools-such such as reasoning traces or program annotations-shhont cilitate debugging and auditing. Finally, we advocate for transparent reporting model limitations and publishing benchmark results across different domains and table types to promote responsible deployment.
be integrated to fa-
B LIMITATIONS, EXTENDABILITY, AND FUTURE WORKS
Although conduct extensive experiments and in-depth analysis, this work still has certain lim- itations. However, we believe that TableMaster possesses extensibility, allowing for future refine- ments. These improvements may include technical refinement as well as optimizing its application in downstream applications.
B. TECHNICAL REFINEMENT
Wild Table. In experiments, the tables in the three datasets are already cleaned; therefore, we do not explicitly implement table normalization in our evaluation experiments. However, we conduct analysis experiments to highlight the importance of table normalization for handling wild tables. In practical scenarios, various tools are available for table normalization. Regular expression matching can be employed for formatting, and small language models can also be leveraged to efficiently process and normalize tables (Nahid & Rafiei, 2024b).
Hierarchy Table. In our work, we assume all tables are flat, allowing for straightforward utilization and extraction of structural information. However, many real-world tables are hierarchical, where data is organized in a tree structure, making table structure understanding more challenging. We envision two possible solutions: converting hierarchical tables into flat tables or designing a tree- based structure extraction method to effectively locate target data.
employ two efficient methods: LM prompting for column lookup and SQL. generation for row lookup. The Table-of- is then constructed based on the results of these two lookups. Many previous works (Ji et al., 2024; Wang et al., 2024) have introduced complex approaches for extracting relevant sub-tables. In contrast, our method remains intentionally simple, prioritizing efficiency and adaptability. We believe that in the future, more advanced techniques may emerge to further enhance the extraction of key information.
Table-of-Focus Construction. In designing the Table-of-Focus,
Table Verbalization. To facilitate the implementation of TableMaster, we utilize language models themselves to verbalize the table. However, the quality of the generated text is not optimal due to the challenges of open-ended text generation. Several existing studies, such as Table-to- (Parikh
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et al., 2020), have explored this sub-task. In the future, can enhance performance and efficiency by replacing this step with specifically trained small language models, which could further improve the semantic density of the verbalized table.
Adaptive Reasoning. Adaptive reasoning can be unstable, as language models may not always select the optimal strategy. We further explore this issue in Appendix L. In the future, training dedicated machine learning model to guide LMs in selecting the most effective reasoning strategy could improve stability and performance.
Information Missing. The construction of the Table-of-Focus involves a trade-off between preci- sion and recall. If recall is insufficient, essential information may be missing for final reasoning. while low precision can render the extracted content less useful. Although we use re-construction to mitigate information loss during the Table-of-Focus construction process, our analysis reveals that some information missing persist in row lookup. We further investigate this issue in Appendix M.
Efficiency. Efficiency crucial in table processing and table understanding. To enhance efficiency, we incorporate the table peek technique, which reduces the context that language models need to process at certain steps. We further explore this technique in Appendix H and analyze the overall efficiency in Appendix I. In real-world applications, for optimal efficiency, we consider replacing certain steps with specialized small language models, balancing the trade-off between efficiency and performance
B.2 DOWNSTREAM APPLICATIONS
Web Tables. The web contains a vast number of structured tables, including Wikipedia tables, govermment reports, and other online tabular data. Extracting and reasoning over these tables is crucial for applications such as fact verification, web search, and knowledge graph construction. TableMaster enhances the ability to interpret, query, and reason these tables, enabling more accurate and context-aware information retrieval.
Spreadsheets. Spreadsheets are widely used in business, finance, and scientific research for data management and analysis. Traditional spreadsheet tools require manual formula creation and human intervention derive insights. In contrast, TableMaster can automate tasks such data summa- rization, trend analysis, anomaly detection, and reasoning-based computations. By integrating with tools like Microsoft Excel and Google Sheets, TableMaster enables intelligent spreadsheet interac- tions, allowing users to query data using natural language and receive precise, structured responses.
Databases. Structured databases store vast amounts of relational data, typically accessed through SQL queries or predefined interfaces. However, many users lack SQL proficiency, posing barriers efficient data retrieval. TableMaster, with its Table-of-Focus mechanism, facilitates the quick understanding of large databases, enabling seamless querying of relational data without the need for manual SQL query writing. Additionally, it enhances database reasoning tasks, including knowledge extraction, making structured data accessible to non-technical users.
In real-world applications, different scenarios have varying requirements, and it may not be nec- essary to incorporate all aspects of TableMaster. Instead, certain components can be adapted or selectively applied based on specific needs.
Finally, discussed above, there is still much work to be done in the future further enhance language model- based table understanding. We hope this work serves as a recipe of comprehensive references on current oftt--of-the-an methods and provides guidance for future advancements in this field.
C DATASETS USED FOR EVALUATION
Table 3 shows all datasets use for evluation in this study, license and source also included.
D DETAILED SETTINGS OF CHALLENGE ANALYSIS EXPERIMENTS
We conduct extensive experiments to analyze the challenges of table understanding with language models (LMs). Specifically, perform challenge analysis experiments the WikiTQ dataset
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Table 3: Benchmarks used for evaluation.
Dataset
Test
Table Type
Domain
License
Source
WikiTQ (Pasupat Liang, 2015)
4,217
Relational
Wikipedin
CC-BYSA-40
Link
TabFact (Chen et al., 2020)
2,024
Relational
Wikipodia
CC-BY-4.0
Link
FetaQA (Nan al., 2022)
Relational
Wikipedia
CC-BY-AA-000
Link
FinQA (Chen et al,, 2021b)
1,147
Relational
Finance
MIT
Link
HiTab (Cheng ol., 2022)
1,583
Hierarchical
Reports
C-UDA 1.0
Link
(Pasupat & Liang. 2015), which consists of 4,344 data instances. Following previous work (Wang et al., 2024; Liu et al., 2024b), we use the exact match of the final answer as the evaluation metric to measure accuracy. Our experiments utilize OpenAl models hosted on Microsoft Azure'. Unless oth- erwise stated, we set the temperature to 0 to ensure stable output while keeping all other hyperparam- eters their default values. For each model, gpt-4o-mini (gpt-4o-mini-077 18), gpt-3.5-turbo
the following versions: gpt-4o (gpt-4o-0806), and of (ol-preview-0912)
Effect of Table Size (Figure 2(a)). We evaluate how table size impacts task diffieulty using direct prompting approach (Prompt 21) with gpt-4o, gpt-4o-mini and gpi-3.5-turbo to generate answers. We categorize table size based four metries: row count, column count, size (computed the product of row and column counts), and token count (measured using the cl100k_ base encoding). small, medium, large, and
The tables are divided into four size categories- -mall,, partitioned into quartiles from the smallest to the largest. We then analyze results by splitting per- formance based on table size.
Effect of Verbalization (Figure 2(b)). We investigate the impact of enriching semantic context through verbalized tables by comparing three approaches. In the Table setting, the LM processes the raw table directly using direct prompting (Prompt 21). In Table + Verbul, the table is first verbalized using the LMM itself (Prompt 24), and both the original and verbalized tables are then provided as imput. Lastly, in Table Verbal Plus, the verbalized table is generated using gpt-4o, further enhancing the semantic richness of the input.
Comparison of Reasoning Methods (Figure 2(c)). We compare different reasoning ap- proaches-textuall reasoning (Prompt 22), symbolic reasoning (Prompt 23), and text-guided sym- bolic reasoning (Prompt 25) on on iileulation-equirred versus non-caleulation questions using gpr- 3.5-turbo. To classify WikiTQ questions into calculation-requiree or not, we use o/ (Prompt 28), identifying 2,692 calculation-required questions and 1,652 non-caleulation questions. The results are then analyzed based on this classification.
Impact of Noisy Tables (Figure 2(d)). We investigate how performance varies between normalized and noisy tables. To generate noisy tables, we use ol (Prompt 29), instructing it to introduce noise into table contents while preserving actual values and diversifying entries within columns. Addi- tionally, each table has a 50% chance of being randomly transposed from the default row-major format to the column-majon format. We then filter the generated tables through a combination of human verification and o/ checks to ensure that answers remain derivable from the noisy tables. After filtering, 2,565 noisy tables remain. We evaluate textual reasoning (Prompt 22) and symbolic reasoning (Prompt 23) on both the noisy and original normalized tables using gpt-4o-mini.
E EXTENDED EXPERIMENTS ON ADDITIONAL TABLE UNDERSTANDING BENCHMARKS
We perform additional experiments on diverse table-understanding tasks to further assess the robust- ness of TableMaster.
E. EVALUATION ON FREE-FORM QA WITH THE FETAQA DATASET
PaLM 2 has been deprecated (Anil al., 2023) and is longer accessible. Therefore, we comparable language model, gpt-4o, to conduct experiments on FetaQA and compare the results
htttss//anure.microoottt http://www...roaaooooooooooo/aaa suppppott///gglll
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Table Performance comparison on FetaQA. The values are multiplied by 100, and the percentage improvement represents the performance gain compared to the end-to-end QA of the base model. The results demonstrate that TableMaster achieves strong performance in long-form question an- swering.
Methods
BLEU
ROUGE-1
ROUGE-2
ROUGE-L
Tine-TTuing (TS-large) (Ye et al,, 2023)
30.54
63
41
$3
End-to-End QA (Codex) (Chen al., 102
27.96
52
End-to-End QA (PaLM M. (Wang
2024)
28.37
41
53
Dater (PaLM 2) (Yo et al., 2023)
29.47
63
41
53
Chain-of-Tad Table (PaLM (Wang al., 2024)
32.61 (+14.9%)
(+4.8%)
(+7.3%%
(45.7%)
End to- End QA (gpt-4o)
24,91
62.05
41.29
50.36
Ours (Tablemaster gpt4o)
28.94 (+16.2%)
66.06 (+6.5%) %))
45.29 (49.7 %)
54.56 (+8.3%
Question: How did Napolitano perform compared to the other candidates?
Candidate
Votes
Republican
Phil Garwood
5,467
17
Prediction
TableMaster
Victoria
Victoria Napolitano performed the best among the candidates, receiving the highest percentage of votes at 18% with a total of 5,580 votes.
Republican
Pate Palko
5,321
17
Democratic
Newcomer
5,345
17
Democratic
Brien Sattinger
4,899
15
Democratic
Mark tines
4,859
15
Evaluation Results
Ground-truth
BLEU: 0,0411
On election Napolitans the getter with 5,580 vates, outpacing her Republican running mates as well Democrat opponents.
ROUGE-1: 0.2791 ROUGE-2: 0.0976
ROUGE-L: 0,2790
Figure 4: An example (fetaqa-164) from the FetaQA dataset where the result accurate, but the evaluation metric assigns low score.
with previous methods. Additionally, we use 20 exemplars for few-shot in-context learning to align with the dataset's format.
Table shows that TableMaster improves free-form question answering performance on FetaQA compared to the base End-to-Ent End QA model, achieving improvements of 16.2% in BLEU and 6.5% in ROUGE-1, These improvements surpass those of Chain-of- when compared to its respective End-to-End QA baseline.
However, the improvement of TableMaster over baseline methods remains marginal, with some values even falling below those of previous approaches in absolute terms. We believe this does not fully reflect the model's actual performance in free-form QA. We attribute this to the n-gram text similarity metrics used in ROUGE-1/2/L (Lin, 2004), which are known be insensitive to improvements gained from in-context learning (Maynez et al., 2023). These metrics struggle to capture stylistic and structural enhancements in free-form text generation. Since models rely on instructions and limited number of examples, they may not fully adapt to the expected output format, leading to an underestimation of performance gains.
To further investigate this, we analyze specific case, FetaQA-164, shown in Figure 4. In this instance, the BLEU and ROUGE metrics assign low scores, as only two words match in the entire sentence. However, manual review confirms that the generated answer is indeed correct-these two words are the most important, and the overall meaning of the response is both accurate and superior to the ground truth. This highlights the limitations of ROUGE in evaluating free-form QA and suggests that qualitative analysis is essential for a more comprehensive assessment of model improvements. Nonetheless, based quantitative analysis, TableMaster is overall effective.
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E.2 EVALUATION ON HIERARCHICAL TABLES WITH THE HITAB DATASET
UUnlike WikiTQ and TabFact, the HiTab dataset (Cheng et al., 2022) contains hierarchical tables that violate the flat row-column assumption. Such structure challenges models reason multi-level headers and parent-child cell relations.
We employ a GPT-4o backbone and follow HiTab's s official data split. MultiCoT (CYQIQ. 2025) extends Chain-of- the same converted relational tables. E5 (Zhang et al., 2024c) represents the current SOTA on HiTab, being explicitly tailored for complex tables. Because our framework (TableMaster) is designed for relational tables, we introduce a lightweight relational-table converter via prompting ol: each hier- archical table is decomposed into several relational subtables, while contextual tags are propagated to preserve structural cues.
reasoning to multiple tables; both MultiCoT and TableMaster operate on
Table 5: Accuracy (%) on the HiTab dataset. "After Converting" rows evaluate models on relational tables produced by our converter; "Direct" reports results the original hierarchical tables.
Method
Accuracy
After Converting to Relational Tables
MultiCoT (original (CYQIQ, 2025))
64.0
MultiCoT (optimized prompt)
70.0
MultiCoT (optimized prompt + verbalized table)
73.5
TasteMaster
74.2
Direct E5 (Zhang et al., 2024c)
77.3
Table shows that TableMaster outperforms the strongest chain-of-table baseline by +0.7 point (from 73.5 to 74.2). Residual errors are mainly due to information loss during the conversion step. Future work will integrate hierarchical relations directly into the reasoning module.
E.3 EVALUATION ON NUMERICAL. REASONING WITH THE FINQA DATASET
FinQA (Chen al., 2021b) requires multi-step numerical reasoning over financial reports- e.g. computing growth rates or combining multiple cells with arithmetic operators. Hence it evaluates whether TableMaster can execute numerical formulas correctly, not just extract text spans. We keep the same training recipe as in. Two backbones are considered: GPT-4o-mini (4m) and GPT-4o (4o).
Table 6: Accuracy on FinQA. TableMaster consistently boosts numerical-reasoning accuracy both backbones.
Method
Accuracy (%)
A
GPT-4o-mini
50.7
TableMaster (4m)
66.4
+15.7
GPT-4o
63.1
Taste (4o)
70.9
+7.8
Table 6 shows that TableMaster delivers impressive improvements on both backbones, demonstrat- ing that its symbolic reasoning module reliably handles complex calculations the financial do- main.
F TABLE UNDERSTANDING BASELINES
To better facilitate future research, evaluate different reasoning methods across various base mod- els. Table presents the accuracy results our reproduced baselines on WikiTQ and TabFact. com- paring different base LLMs and reasoning methods. The table includes evaluations of opreeveww
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Table 7: Results of our reproduced baselines on WikiTQ and TabFact. represent accuracy (%).
The values in the table
Base LLM
Method
WikiTQ
TabFact
ol-previeww.3008
Direct
84.60
92.05
ol-mini..o0s
Direct
83.49
91.35
Direct
73.07
84.73
Chain of Thought
83.98
91.90
gpt-40-2008
Program of Thought
74.63
90.02
Table.Master (gpt-4o)
84.55
94.52
Direct
59.53
71.25
Chain of Thought
72.97
87.40
gpt-4o-mini..s
Program of Thought
61.83
85.18
TableMaster (gpt-4o-mini)
78.13
90.12
Direct
56.58
70.90
Chain of Thought
59.92
69.52
gpt-3.5-turbo-.1158
Program of Thought
50.32
68.82
Master (gpt-3.5-turbo)
68.21
83.65
(~300B), ol-mini (~100B), gpt-40 (~2200B,, gpt-4o-mini (~8B), and gpt-3,5-turbe (~175B). The exact number of parameters for several LMs (e.g., GPT, ol) has not been publicly disclosed. Most parameter counts are estimates reported to provide context for understanding model performance. For more precise information, please refer to the original or future official documentation (Abacha et al., 2025). Each model tested with various reasoning strategies, including Direct, chain of thought, and Program of Thought, alongside proposed TableMaster.
Across all base models, TableMaster consistently achieves the highest accuracy. For gpt-4o, Table- Master reaches 84.55% WikiTQ and 94.52% on TabFact, outperforming both chain of thought (83.98%, 91.90%) and Program of Thought (74.3%% 90.02%). Similarly, for gpt-4o-mini, Table- Master achieves 78. 133%% on WikiTQ and 90.12% TabFact, significantly improving over the Direct method (59.53%, 71. 71.25%) and surpassing chain of thought (72.97%, 87 87.40%).
The performance gap is 68.21% % on WikiTQ and 83.65% on TabFact, significantly outperforming both chain of thought (59.92%, 69.52%) and Program of Thought (50.32%, 68. 08.82%). Interestingly, TableMaster 's improvement is limited on gpt-4o, the weaker the base model, the greater the perfor- mance improvement. While ol-preview and ol-mini achieve high accuracy with the Direct method (84.60%, 92.05% for ol or-preveww and 83.49%, 91.35% for ol ol-mini), the results of TableMaster on gpt-4o demonstrate that our method capable of achieving state-of-the-an art performance across dif- ferent LL architectures.
pronounced for gpt-3.5-turbo, where TableMaster reaches
observe that while
Additionally, we find that chain of thought reasoning is highly effective, achieving strong accuracy across models. Even simple chain of thought approach outperforms previous methods that rely solely on symbolic reasoning (Mao et al., 2024), indicating that chain of thought should be retained key component in the reasoning framework.
These results confirm that TableMaster enhances table reasoning performance across various LLMs effectively outperforming both direct prompting and traditional reasoning strategies, particularly in cases where table complexity and reasoning demands are higher.
G PERFORMANCE ANALYSIS UNDER DIFFERENT TABLE SIZES
Table 8 presents performance comparison across different table sizes, categorized into small (<2k tokens), medium (2k~4k tokens), and large (>4k tokens). The results compare several methods, including Binder (Cheng al., 2023), Dater (Ye et al., 2023), and Chain-of-Tablee (Wang et al.,
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Table 8: Performance Comparison Across Table Sizes (Token).
Table Size (Token)
Method
Small (<2k)
Medium (2k ~ 4k)
Large (>4k)
Binder (Cheng et al., 2023)
56,54
26.13
6.41
Dater (Ye et al., 2023)
62.50
42.34
34.62
Chain-of-Table (Wang et al., 2024)
68.13
52.25
44,87
TableMaster (gpt-3.5-turbo)
69.01
58.00
56.73
TableMaster (gpt-4o-mini)
78.71
70,50
70.19
Accuracy vs Table Size (Row Count)
Accuncc va Table Size (Column Count)
25
Accurecy PS
Acurse
70
Metler (1/th)
Tesse 12820)
Veukee (2p14nt
Tetsolaster spatirr
2-10
11-15
25
28- 3TB
7-21
Table Blzc Count)
Accuracy Table Size (Area Bize)
Accuracy ve (Token Count)
26
Accurety 09
Accunncy
90430
22/4
galli
(y00))
Mt/ader (2:02) gante)
Table Size (Area Size)
Tabre Bize (Token Count)
Figure 5: Performance Comparison Across Table Sizes (Row Count, Column Count, Area Size, Token Count).
2024), against TableMaster. All methods are evaluated using gpt-3.5-turbo, with additional results of TableMaster provided for gpt-4o-mini.
Across all table sizes, TableMaster consistently outperforms baseline methods. Specifically, for gpt-3.5-turbo, TableMaster achieves the highest performance in all table size categories, scoring 69.01% % on small tables, 58.00% on medium tables, and 56.73% on large tables. This demonstrates its ability to maintain robust performance even as table size increases, significantly outperforming Binder, Dater, and Chain-of-Table especially on medium and large tables, where the performance gap becomes more pronounced.
Furthermore, TableMaster with gpt-4o-mini achieves even stronger performance, with accuracy scores of 78.71% (small tables), 70.50% (medium tables), and 70.19% (large tables). These re- sults highlight that leveraging stronger base models further enhances TableMaster 's effectiveness, making it particularly well-suited for large-scale table reasoning tasks. Notably, when transition- ing from medium to large tables, TableMaster (gpi-4o-mini) experiences only 0.31% performance drop (from 70.50% to 70. 19%), demonstrating its strong capability in handling increasing table com- plexity. This minimal decline contrasts sharply with other methods, which show significantly larger drops, further reinforcing the scalability and robustness of TableMaster in processing large-scale tabular data.
Figure 5 illustrates the accuracy trends of different models across various table sizes, categorized based row count, column count, area size, and token count. The models evaluated in this study include 3-t-3.5-turbo (gpt35), gpt-4o-mini (gpt4m), TableMaster (gpt35), and TableMaster (gpt4m). The results provide insights into how these models handle increasing table complexity and size,
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revealing the comparative strengths and limitations of each approach. The size split in this study is strictly partitioned into quartiles, ranging from the smallest to the largest tables.
The top-left plot analyzes accuracy trends row count increases. TableMaster (gpt4m) consistently outperforms other models, maintaining high accuracy levels even with an increasing number of rows. In contrast, gpt-3.5-turbo (gpt35) starts with the highest accuracy, peaking in the 11-15 row range before experiencing a decline as row count further increases. Smaller models such as gp135 and gpt4m exhibit a sharper decline, highlighting the challenge of processing larger tables with more rows.
Row Count.
Column Count. The top-right plot examines model performance column count increases. Table- Master (gpt4m) again achieves strong performance, peaking at around five columns before showing slight decline. This result highlights the effectiveness of table-of-focus re-construction, demon- strating that column re-selection can effectively adapt to scenarios with many columns. While gpt35 initially maintains the highest aceuracy, other models experience a steeper drop as the number of columns increases. These trends suggest that column-heavy tables pose greater challenges for rea- soning compared to row-heavy tables, likely due to the increased dimensional complexity and inter- dependencies between attributes.
Area Size. The bottom-left plot evaluates the relationship between accuracy and table area size, cal- culated as the product of row and column counts. TableMaster (gpt4m) reaches peak performance in the mid-range (96-167 area size) before slightly declining for larger tables. gpt35 initially performs well but deteriorates as table area size increases, while gpt4m and gpt35 show noticeable decline overall, reinforcing that larger tables significantly impact accuracy across models.
Token Count. The bottom-right plot assesses accuracy function of table token count, which reflects the amount of textual information models need to process. TableMaster (gp14m) consistently achieves the highest accuracy, followed by TableMaster (gpt35). A general downward trend is observed across all models as token count increases, indicating that larger input lengths negatively affect performance. Notably, gpt35 experiences the sharpest drop, suggesting lower capacity for handling long-context table data compared to gpi4m.
Overall, these findings confirm that TableMaster is highly scalable and generalizable across different table sizes, consistently outperforming previous methods, particularly in handling larger and more complex tables. Its robust performance and gradual decline accuracy as table size increases make it a reliable and efficient solution for table-based reasoning tasks.
H PERFORMANCE ANALYSIS UNDER DIFFERENT TABLE PEEK SIZES
Dansly Piot Row Sasa WHITO Datsnst
Accurscyve vs Size
Marier Roat Takie
Pebc Sbx
(b)
Figure 6: The row count distribution in the WikiTQ dataset and the analysis of accuracy variation with different peek sizes.
We propose the concept of table peek, which enhances the efficiency of TableMaster for table un- derstanding tasks by reducing the context that language models need to process at certain steps.
To analyze the effectiveness of this approach, we first examine the count distribution in the Wik- iTQ dataset. as shown in Figure 6a. To improve visualization, we remove 72 extreme outliers with exceptionally large row counts. The resulting density plot illustrates that the majority of tables con-
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tain fewer than 20 rows, with pronounced peak around 10 rows. As the number of rows increases, the density gradually declines, indicating that large tables are relatively uncommon. Although a small number of tables exceed 100 rows, their frequency is minimal.
The line graph in Figure 6b illustrates the variation in accuracy with different peek sizes, where the peek size determines the number of rows considered during processing. Initially, accuracy is relatively low when only a small number of rows (e.g.. 2-4) are used, reaching its minimum at a peek size of 4. We hypothesize that this occurs because, at a peek size of 2, the table includes only the top headers and single example row, which may provide a clear structure for the language model follow. However, at a peek size of 4, the table includes three example rows, potentially causing the language model to overfit the first few rows and misinterpret the overall table structure. This misalignment may lead to ineffective SQL generation for row lookup, resulting in temporary drop in accuracy.
As the peek size increases, accuracy improves significantly, showing sharp rise up to rows. Beyond this point, the accuracy continues to improve but at slower rate, eventually reaching its peak when the entire table is utilized ('All"). This trend suggests that moderate peek size can effectively balance efficiency and accuracy, eliminating the need to process the full table while still maintaining strong performance.
I EFFICIENCY ANALYSIS OF TableMaster
1.1 THEORETICAL ANALYSIS
Efficiency is critical factor in table-understanding methods. We analyze the efficiency of Table- Master theoretically, following the notations introduced in Section 4. Our analysis considers length of the table input as the primary computational cost, excluding any additional prompts or external information, and does not account for output length. This because, in most cases, the output is relatively short compared to the large volume of data in the table. Specifically, define the computational cost in terms of the total area size of the table that the language model processes.
Below the main components of our efficiency analysis:
Structure extraction: & x
Row lookup:
Column lookup: n
Table-of-Focus Re-Construction a x
Table Verbalization: x b,
Reasoning Strategy Assessment: x b,
Reasoning: a.5 b (where the factor 1.5 accounts for textual processes weighted and symbolic processes weighted as 2)
Here, 1 represents the size of table peek, and e represents the number of table-of-focur re- constructions after information estimation. and denote the dimensions of the table-of-focus Taxb- Combining these components, the total computational cost given by:
Total Cost (2 1) (c 2.5) (a b).
I.2 EMPIRICAL ANALYSIS
The bar chart Figure illustrates the change in table area size before and after table condensation for the WikiTQ and TabFact datasets. The y-axis represents the table size, while the x-axis catego- rizes the datasets. Each dataset has two bars: the blue bar represents the original table size, and the orange bar represents the condensed table size after table-of-focus construction. WikiTQ exhibits significant reduction in table size, approximately 1:3, with the condensed table being much smaller than the original. In contrast, TabFact also undergoes condensation but lesser extent, around
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Change Table
Original Tabbe
Condensed lable
WkTT
TabFact
Dataset
Figure 7: Changes in Table Condensation After Table-of-Focus Construction in Table Structure Understanding.
1:2. This suggests that WikiTQ tables require more substantial structural modifications to focus on relevant content, while TabFact tables need comparatively less condensation.
As shown in Equation 1, the theoretical cost is independent of the number of rows m, while a x reflects the size of the small sub-table, which is influenced the estimated table condensation ratio 2.5. As stated in Table 10, the reconstruction occurs 1.5 averagely, so is typically 1.5. In an ideal scenario, if the peek size is negligible, the cost approximately 1.6 x (m X n). In the worst- case scenario, where the entire table must be examined and all content is required, the cost reaches (m n) approximately. The The estimation range for each table 1.6 times the original table size.
Recent advancements in table understanding, such as CHAIN-OF-TARLE (Wang et al., 2024) and TREF-OF-TABLL (Ji et al., 2024), involve a step-by-step evolution of tables through a long chain of transformations. In each new step, both the original table and the newly generated sub-table must be processed by language models. Additionally, their iterative process is complex, unstable, and diffi- cult to analyze theoretically. In contrast, our approach is general and comprehensive, avoiding the trivial overhead of sub-table extraction. Instead, it focuses holistic reasoning while maintaining ideal efficiency.
On the first 100 examples of TabFact, evaluate TableMaster against the representative base- line CHAIN-OF-TABLE (Wang et al., 2024) using GPT-4o, ensuring fair comparison without self- consistency decoding.
Table 9: Token usage comparison on the TabFact subset using GPT-4o.
Method
Prompt Tokens
Completion Tokens
Chain-of-Table.
13,209.6
914.2
TableMaster (Ours)
3,393.5
738.6
As shown in Table 9, TableMaster consumes substantially fewer tokens while maintaining strong performance. While our design may appear intricate, it integrates practical features such as fall- backs to early-exit or full-table reasoning, thereby avoiding unnecessary table transformations. In contrast, CHAIN-OF- -ABBLE continuously evolves the table without fallback safeguards, leading to much higher token usage. This limitation is not acknowledged in the original paper, whereas our framework explicitly incorporates token efficiency as a design principle.
J DETAILED ALGORITHM OF TABLE-OP-FOCUS RE- CONSTRUCTION
Here, we provide a detailed description of the Table-of-Foode Focuu Re-Construction algorithm, as shown in A Algorithm 1.
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Algorithm Algorithm of Table-of-Focus Re-Construction
Require: T: The original table
Require: Q: The question
Require: R: Selected rows
Require: CO: Initially selected columns
Require: C: Ranked candidate column indices
Ensure: TF; Final table-of-focus
Ensure: C: Updated selected columns
1: Initialize Ceandidate + {cE C | C*)
2: Initialize C + Copy(C")
3: while true do
4: T < extractTable(T, R,C) C)
5: E + estimatelnformattion T Q)
6: if or len(Crannddeen
then
7: break
8: else
9:
- (C-poooont(C
SSelect the next candidate column
10: C
11: end if
12:
end while
return TT. C
The Table-of-Focce Re- Ce-Coontruction Algorithm iteratively refines table by selecting relevant columns to form the final table-of-focus TF. It starts by initializing the set of candidate columns Grondidate that are not part of the initially selected columns C* and copies C initialize C. In each iteration, it extracts sub-table T using the current selected columns and estimates whether the extracted sub-table contains sufficient information to answer the given question Q. If the in- more candidate columns remain, the process terminates.
formation is sufficient E' True Otherwise, the next ranked candidate column is selected and added to C, repeating the process. The algorithm ultimately returns the refined table T and the updated set of selected columns, ensuring an efficient and structured approach to dynamically refining a table while balancing relevance and minimal table size.
K ANALYSIS OF TABLE-OF-FOCUS RE-CONSTRUCTION
Table 10: Column Selection Statistics Before and After Table-of-Foowe Re-Construction for TabFact and WikiTQ.
Dataset
Initial Columns
Final Columns
Added Columns
Number (#)
Percentage (%)
Number (#)
Percentage (%)
Number (#)
Percentage (%)
TabFact WikiTQ
2.44
40.74
3.34
54.64
0.90
13.91
2.87
47.67
4.72
75.91
1.85
28.2
Table 10 presents Column Selection Statistics before and after Table-of-Foon -fecuus Re- Re-Construction for two datasets: TabFact and WikiTQ. The table measures how many columns were initially selected, how many remained after refinement, and how many were newly added during the reconstruction process.
The table structured into three main sections: Initial Columns, Final Columns, and Added Columns. Each section includes two metrics: the number of columns and the percentage of total columns the dataset. The Initial Columns represent the starting number of columns before any refinement. The Final Columns show the number of columns retained after the reconstruction pro- cess. The Added Columns indicate the number of additional columns incorporated to enhance table comprehension.
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For the TabFact dataset, the number of Initial Columns is 2.44, covering 40.74% of the table's 's total columns. After the reconstruction process, the Final Columns increase to 3.34, covering 54.64%. This means that 0.90 additional columns were introduced averagely, which accounts for 13.91% of the total columns. For the WikiTQ dataset, the pattern is similar but with higher values. The Initial Columns start at 2.87, representing 47.67% of the total table. After reconstruction, the Final Columns expand to 4.72, covering 75.91 % of the table's s total structure. This increase results from 1.85 additional columns, which make up 28.23% of the total columns.
Overall, this mechanism has been proven to be effective while remaining lightweight. The ta- ble demonstrates that Table-of-Focce -ocuu re-Coonsruuttion slightly increases the number of selected columns, with a more pronounced effect in the WikiTQ dataset compared to TabFact. This suggests that WikiTQ tables require a greater degree of expansion to ensure adequate information coverage, whereas TabFact tables undergo more moderate refinement process.
L ANALYSIS OF ADAPTIVE REASONING
Table 11: Performance of Different Reasoning Methods Across Base LLMs
Base LLM
Method
Calculation Required #2892
No Calculation Required #2682
Overall p434
Textual Reasening
81.17
88.56
83.98
8pt-4o
Symbolic Reasoning
74.59
74.70
74.63
Text Text-Guided Symbolic Reasoning
77.36
76.82
Textual Reasoning
67.50
81.90
72.97
gpt-4o-mini
Symbolic Reasoning
61.55
62.29
61.83
Text Guided Symbolic Reasoning
67.24
71.43
68.83
Textual Reasoning
52.27
72.40
59.99
apt-3.5-turbo
Symbolic Reasoning
43
61.80
$0.32
Text-Guided Symbolic Reasoning
59.10
66.65
61.97
We consider adaptive reasoning key component in table understanding. Concurrent work, such as Abhyankar et al. (2025), also explores this direction.
Table 11 compares different reasoning methods textual reasoning, symbolic reasoning, and text- guided symbolic reasoning- various LLMs under calculation-required and no-calculation- required scenarios. This experiment is conducted using gpt-4o-mini on the WikiTQ dataset.
For gpi-4o, textual reasoning achieves the highest accuracy (83.98% overall), excelling in both calculation-required (81.17%) and no-caleulation-requiee (88.56%) cases. Symbolic reasoning performs worse (74.63% overall), while text-guided symbolic reasoning offers slight improvements (76.82%) For gpt-4o-mini, a similar trend is observed, with textual reasoning maintaining the high- est accuracy (72.97% overall), followed by text- eext-guided symbolic reasoning (68.83%) and symbolic reasoning performing the worst (61.83%) For gpt-3.5-turbo, performance drops significantly, with textual reasoning at 59.92%, symbolic reasoning struggling at 50.32%, and text-guided symbolic reasoning achieving the best results (61.97%) indicating that symbolic guidance benefits weaker models.
Symbolic reasoning is consistently outperformed by textual reasoning, while text-guided symbolic reasoning surpasses textual reasoning only in g-t-3.5-turbo under caleulation-required scenarios. One reason for this is that not all calculation-requiree questions necessarily benefit from sym- bolic reasoning; for simple calculations, textual reasoning is more effective. However, for com- plex calculation-required questions, text-guided symbolic reasoning is the preferred approach. This provides a key insight for prompt design of reasoning strategy assessment.
Overall, textual reasoning consistently outperforms symbolic reasoning across all models, while text-guided symbolic reasoning helps mitigate weaker numerical capabilities in smaller models. These results suggest that adaptive reasoning should prioritize textual approaches, incorporating symbolic methods selectively for numerical calculations in weaker models.
Table 12 compares the performance (aceuracy %) and inference times of various reasoning meth- ods, including chain of thought (CoT), program of thought (PoT), text-guided program of thought (TPoT), self-consistency, and adaptive reasoning. This experiment is conducted using gpt-4o-mini on the WikiTQ dataset.
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Table 12: Performance and Inference Times for Different Methods
Method
Accuracy (%)
Inference Times (#)
Chain of Thought
72.97
Program of Thought
61.83
Text-Guided Program of Thought
68.83
Self-Consistency (5 CoT)
74.98
caff-Consittncy (5 PoT)
63.97
Mix Self- Seff-oonsistency (3+3)
76.70
Mix Self-Consistency (5+5)
77.46
Self-Eval
70.58
2
Adaptive Reasoning (POT)
71.18
Adaptive Reasoning (TPOT)
74.08
Adaptive Reasoning (POT Upper Bound)
82.99
Adaptive Reasoning (TPOT Upper Bound)
85.06
Among single-pass methods (1 inference), chain of thought achieves 72.97% accuracy, outperform- ing program of thought (61.8 (1.8%%) and text-guided program of thought (68.833 (3)8. This suggests that CoT is more effective than pure symbolic reasoning when only one inference allowed.
Self-comsistency methods, which perform multiple inferences improve reliability, achieve better results. Five-shot CoT self-consistency reaches 74.98%, while five-shot PoT self-consistency lags behind 63.97%. As introduced in (Liu et al., 2024b), mixed self-consistency (3 CoT PoT) and (5+5) further improve accuracy to 76. 70% and 77.46% respectively, at the cost of increased inference time (6 and 10 passes). Self-evaluation (self-eval) first performs CoT and PoT inferences (Prompt 26), then selects the better result, achieving 70.58% with inferences.
Adaptive reasoning achieves competitive performance while maintaining single-pass efficiency. PoT-based adaptive reasoning reaches 71.18%, while TPOT-based adaptive reasoning, which com- bines textual and text-guided symbolic methods, improves 74.08%. The upper-bound perfor- mance of these adaptive strategies (assuming perfect strategy selection) reaches 82.99% (PoT) and 85.06% (TPOT), significantly outperforming all other methods, highlighting the importance of tex- tual guidance and strategy selection.
For the selection distribution between CoT and PoT (TPoT):
Self-eval: 1,962 PoT and 2,382 CoT
Adaptive reasoning (PoT): 1,590 PoT and 2,754 CoT
Adaptive reasoning (TPoT): 1,590 PoT and 2,754 CoT
Adaptive reasoning (PoT upper bound): 435 PoT and 3,909 CoT
Adaptive reasoning (TPoT upper bound): 525 PoT and 3,819 CoT
These results suggest that language models should prioritize textual reasoning and reserve symbolic reasoning for more complex numerical calculations where it provides clear advantage.
Overall, self-consistency enhances accuracy but requires multiple inferences, whereas adaptive rea- soning effectively balances accuracy and efficiency. To further improve strategy assessment, we will explore ways to approach this upper bound in future work. This demonstrates that well-designed adaptive reasoning strategies can rival more computationally expensive self-consistency methods while maintaining efficiency.
M INFORMATION MISSING AND TABLE REASONING WITH FULL TABLE
As discussed in limitations, the table-of-focur process may sometimes lead to the loss omis- sion of key relevant information. This issue inevitable when attempting to locate specific data. relevant data exists within the selected portion, the reasoning result will naturally be incorrect.
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Performance comparison of reasoning with and without the full table on WikiTQ and TabFact.
Table 13:
Method
WikiTQ
TabFact
PoTable (Previous SOTA) (Mao et al., 2024)
64.73
88.93
TableMaster w/ Full Table in Reasoning TableMasttr w/o Full Table in Reasoning
78.13
90.12
77.23 (-0.90)
89.58 (-0.54)
In our experiments, we found that when using the table-of-focus and its verbalized representation for reasoning, 265 out of 4,344 questions in the WikiTQ dataset had available answers. This led to performance drop, as the language model responded with an inability to provide an answer. To address this, replaced the table-of-focus with the original full table, combined with verbalized table-of-fooee focus as input in those questions. The performance under this adjustment is shown in Ta- ble 13, reaching 77. 27.23% WikiTQ. When we directly replaced the ofbl-oo-foocus with the full table for all questions in WikiTQ, the performance increased to 78. 13%, resulting in a slight improvement of 0.9%. Two results are similar.
We believe this approach does not contradict previous steps such structure extraction and table-of- focus selection. These steps remain valuable, as the extracted target data is retained the verbalized table, where the information density is higher and semantic context is richer. The language model prioritizes this high-density information, and if it is insufficient, it can then reference the global information from the full table. This demonstrates the complementary nature of the full table and the verbalized cable-o-ffocur From an efficiency perspective, it preferable to use the sub-table for reasoning initially and only switch to the full table when necessary.
To highlight the performance of TableMaster, we report the best scores 78.13 13 and 90.12 12 in the main results table. Regardless, PoTable (Mao et al., 2024), on both WikiTQ and TabFact.
method consistently outperforms the previous state-of-the-art,
N
CASE STUDY
N.1 CASE STUDY OF TABLE VERBALIZATION
Table
many people stayed years office? Ground-truth:
Error Prediction: 5
William Mccheery
1803
March
Democratic Republican
Alecander Hekim
1829
March
Democrati Republican
13
Willem Pirknay
Murch 4, 1815
April 18, 1516
Demoocctlc Repubtican
Resigred to
x
14
Peteri Itti
September2, 1816
Marzh 3, 1872
Democretic BepuiNiccan
14
Litile
March 4, 1815
March 3, 1525
Jscksonian Df:
LM Reasoning
14
Unte
March4, 1825
Maroh 3, 1829
Adame
Benjani--G. C. Howard
March4, 1829
March 3, 1859
Jooksoriin
Verbalization
Verbalized Table
William McCreery, Democratic Republican, served office from March 1803, March 1809, followed position
Correct Prediction:
McKim, Democratic Republican,
William Pinkney then office March 1815, resigned April 18, accapt diplomatic role Ministar Planipotentiary to Rusaie, Peter Litte succsaded him, serving from Septembar 1818, to March 3, 1823, as Democratic Rapublican, than continued office under different poltiicl effiiiatoon: as Jacksonian March 4,
Democratic Repubikann from March 1923, March was by Benjomin Howard, Jacksonian, who served
and
Adams
1825, 3, 1829.
LM Reasoning
1829, to March 3,, 1833.
Figure 8: Case study on the impact of table verbalization. The data is from the WikiTQ dataset.
Table verbalization brings slight overall improvement in table understanding and is particularly effective in cases where deeper comprehension of the table's 's context is required to answer questions accurately.
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Figure 8 presents case study on the impact of table verbalization in helping language models reason about structured data. The setup includes a table listing U.S. congressional representatives, their terms in office, political affiliations, and notable events. The question posed is: How many people stayed at least 3 years in office?, with a ground-truth answer of 4.
When the table is input directly, the model incorrectly predicts 5, it mistakenly counts rather than identifying unique individuals. This suggests that the model relies on simple row counting instead of truly understanding the data. However, with the verbalized table, the model accurately interprets the descriptions, grasps the actual meaning, and correctly answers with 4.
N.2 CASE STUDY OF TableMaster
As shown in Figure 2, we present a case study of TableMaster to illustrate its detailed workflow in answering the question: "Total wins by Belgian riders?" with ground-truth answer of 7. The process begins with structure extraction, identifying key columns such as Rider, Country, and Wins. Next, column lookup selects relevant data, and row lookup filters the rows containing Belgian riders. SQL generation and execution retrieve only the relevant records where Country Belgium.
The refined table is then constructed into table-of-focus, keeping only the necessary columns. Table verbalization converts structured data into a description to enrich semantic context, providing insights into the number of wins for each Belgian rider. A textual guidance module generates structured step-by-step explanation of the counting process, ensuring clarity in symbolic reasoning. The reasoning and execution phase involves symbolic reasoning (Program of Thought), where Python snippet correctly extracts and sums the wins, leading to the correct prediction of 7.
This case study highlights TableMaster 's ability to accurately extract, process, and reason over structured data, demonstrating its effectiveness in table-based question answering with combina- tion of structured queries, reasoning steps, and code execution.
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Original Table
Question: total wins by belgian riders? fround-truth:
Structure Extraction
111
Tour
Aed
Loaad apin
H
H
Topheaders: Place, Rider, Country, Team, Points, Wins
Key Column Index: Rider
SQL Generation
Row Lookup
Column Lookup
SELECT FROM WHERE Country Belgium
Selected Column:
Rider, Country, Wins
SQL Execution
1, 6,9
Ranked Column: Rider, Country, Wins, Team, Points, Place
6. Table-of-Focus Construction
Table-of-Focus
Verbalized Table
Rider
Country
Wans
table highlights performance
7. Table Verbalization
Selpwn Sylosin Geboers and Roger each, by Robert with one win, while Gaston Rahler not wan any races.
Belgam
Information Estimation /
Textual Guidance
9. Textual Guidance Generation
Micntilly Data Locate
columnn
values Belgien riders
Convert
Numeric
Ensure valuus
format.
4. the Wins Add the rotal number wins.
5. Calculate the Total Perform the addition.
6. Verify the Calculation Double check for accurzcy.
Present the Besult: - Provide the linel lotal win..
10. Symbolic Reasoning (Program of Thought)
11. Code Execution
rowd2)) tabletl:11
Prediction: 7
total_yins sun(vins)
strit wiins)
Figure 9: Case study of TableMaster. The data is from the WikiTQ dataset.
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O PROMPT DESIGN IN TableMaster
Prompt - ExtraccttteEEttaatiioo Extraction
#* Objective
You are provided with a text representation of tobie string formot, detailing the content of eoch cell.
identify
Top Heoder Key of toble.
HR Table Definition
The table represented by coll value where each consists of cell address and value the content that cell, seporated by o comma (egg A1,, Yeor).
Muttiple
Yeor Cells may contain empty values, represented as 'AL, Proofft...
seporated
#* Table (table)
## Instructions
Header: The section the the table, sponning muitiple colurnss horizantally, that descobee the primary Information presented in the table.
represent the subject identifier lobels or keys (e.g. year, date, number, name, etc.).
typically contoining volue, ['A3,Year', 'AZ,Praftt', --
the top
key, cowluonnlinnnne index should be like 'A' or 'B'
The key column should contain meoningful values instead of id.
#e Response Format
response should be ISON format: "Json
"topheaders"; I ["oddresss,,hadeer header1", hadderss2,hedder2,, ....h "ky calumo "columnt",
Figure 10: Prompt for structure extraction in TableMaster. Blue text indicates placeholders for vari- ables within the prompt. The prompt guides the language model in extracting the table's structure.
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Prompt for TableMaster-Columm Ranking
Hi Objective
You are provided with information of toble and a question related to the table. Your task rank column indices based on the relevance the question.
Table Definition
The table is represented by cell-value pairs, where each poir consists of celi address and value of the content in that celi, seporated by a comma (e.g. Yeor), Muttiple cells ore seporoted by (e.g, Yeor(A, Profit)).
Cells may contain empty valves, represented PAZ.fftt.
#* Table Information
Tobie:
(table)
Top Headers: (topheaders)
#B Question
[question)
## Instructions
column indices contain ietters, '8,,
You should first rank all the column indices based on the relevonce to the question.
output
HR Response Format
The response should be ISON I format:
"'son
"ronedd column, indices": ('column index"", "column indexx" ...]
Figure 11: Prompt for column ranking in TableMaster. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to rank the priority of all columns based on the given table, top headers, and related question.
Prompt for TableMaster Column Lookup
#* Objective
provided with informotion of toble a question related Your task is to lookup the column indices that are needed to answer the question based the cable.
#* Table Definition
table is represented by cell-value poirs, where each poir consists of cell address and value content in that celi, seporated by comma (e.g.. 'A1, Year)).
Multiple cells are separated by ['(e.g, YA1,Year|Ab (A2, Prafit').
Colls may contain empty values, represented Profftt
eN Table Information
Toble:
(table)
Top Heoders: (topheoders)
#* Question
(question)
The column indices must only contain letters, like ('A', 'C, 'C)...)
Your output
indices should not any contain number, like ['AT', '82;, 'C., Your the column indices should not contain the column
You should select the column that are relevant and necessory to onswer the guestion.
HNl Response Format
response
/5ON format:
"Json
selected column, indices"; Pcolumn Indext "column indexx"
Figure 12: Prompt for column lookup in TableMaster: Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to select relevant columns based on the given table, top headers, and related question.
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Prompt for T Generttion for Row Lookup
HB Objective
provided with information of toble and question related the table.
Your task is to generate SQL query that can be used to find the rows that onswer the question.
#R Table Information
Table:
(table)
## Question (question)
HN Instructions
query must format of 'SELECT
XIX...
where Table the toble name, XXX the column nome, and WHERE... the criteria.
If the information not enough answer question, you should return sel rows.
Do give complex soi just simple query select Use this SQL query only select relevont rows, not for getting the final answer.
#e Response Format
Provide response the following JSON formot:
"Json
"s
"SELECT XXX, FROM Toble WHERE XXX..."
Prompt for SQL generation for row lookup in TableMaster. Blue text indicates place- holders for variables within the prompt. The prompt guides the language model generate SQL for selecting relevant rows based on the given table and related question.
Figure 13:
Prompt for TableMaster Table Verbalization
## Objective
provided with table strngformatt
Your task is to convert the table into a detailed text description.
#R Table [table)
## Instructions
Provide detoiled description of table, covering
2. Include every detail and numerical value without omitting or summarizing.
knowledge only enhonce clarity, staying faithful If the table only contains headers and no rows, should be described as empty table.
Now, please provide the verbalized description the toble:
Figure 14: Prompt for table verbalization TableMaster. Blue text indicates placeholders for vari- ables within the prompt. The prompt guides the language model to verbalize the given table by adding detailed descriptions and additional knowledge about the table.
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Published as a conference paper at ICLR 2026
Prompt TabfeMaattee-llorrmattoon Estimation
Objective
You are provided with information from table and question related to the tobie.
Your estimate whether Information of con onswer question.
HR Table Information
Headers: (topheoder, info)
Toble Content:
(table)
(question)
## Response Format
The response should be in format:
f
of
Figure 15: Prompt for information estimation in TableMaster. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to evaluate the given table's content and determine whether it contains sufficient information answer the provided question
Prompt for TableMaster -eeaaasnne--RRssiiingg Strategy Assessment
#* Objective
provided with table reloted
Your task is to assess whether answering this question needs motheraticaa colculation.
## Table (table)
## Question (question)
If the question can be directly arowered using the information the table, you should respond with Tolse"
question counting something, you respond
the question requiress calculations based on the data in the table, should respond with 'True'.
## Format
The response should be in format::
"Json
8
"resuits:: False
Figure 16: Prompt for reasoning strategy assessment in TableMaster. Blue text indicates placehold- ers for variables within the prompt. 'The prompt guides the language model to evaluate whether answering the given question requires direct information retrieval, counting, or mathematical calcu- lations based on the table's 's content. The response determines the subsequent reasoning strategy.
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Published as a conference paper at ICLR 2026
Prompt for TableMaster Textual Reasoning
HB Objective
provided with table, verbalization the tobie, and question reloted the toble. Your task is to reason step by step to answer the question based on the tobie.
#R Table Definition
by
pairs,
poir consists of
cell,
by comma (s.g. 'A1, Yeor').
Muttiple separated by 'I' (e.g. AAJYear]AR A22 Profit).. Cells may contain empty values, represented os 'AI, AA,,/rffttt
en Table (table)
#* Verbalized Table (verbalized toble)
Question Answering
onswer should short and simple. it can be number, a word, or phrase the table, but not a full sentence. Your should end with 'Arswer: (onswer to the question).
Your response should end
question). the table only contoin headers and no rows, indicotes there is no information available for this question, therefore the orswer "Yolse."
fulse"
Fact Verification
Now, give onswer step by step: Question: (question)
Figure 17: Prompt for textual reasoning in TableMaster. Blue text represents placeholders for vari- ables within the prompt, while the grey region indicates optional sections to adapt the prompt for question-ane anniiirnn or fact-verification tasks. The prompt guides the language model to answer the question step by step.
Prompt for FableMaster-Textee Guidance Generation
#* Objective
are provided with table, verbolized table, and a question related the toble. Your task to give step-by-step quidance onswer the question based table.
Table Definition
The table is represented by cell-value pairs, where each poir consists of cell address and value of the content in that cell, seporated by a comma (e.g.. Yeor)) Muttiple cells are seporated by (e.g, 'AJ, Year(A,, Profit)). Cells may contain empty values, represented 'AL, Pro.fft
#e Table (table)
Verbalized
(verbalized, table)
You do not need to give the onswer. You need give reasoning process as guidance thot used loter.
## Response Format
response should be of steps:
XXX
Now, me the guidonce answer the question step by step: Question: [question)
Figure 18: Prompt for textual guidance generation in TableMaster. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to generate textual guidance that can be utilized for subsequent symbolic reasoning.
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Published as a conference paper at ICLR 2026
Prompt for TableMaster (Pasoninggnoogggggnnnnnn of Thought)
Reasoning
#e Objective
are provided table, verhalized guldanoe, question
Your tosk is to generote Python code that onswers the question using the table and the guidiance guide.
#e Table Definition
The toble is represented by cell value poirs, where each poir consists of a cell address and value of the content in that cell, soporated by (e.g. Yeor).
Multiple cells are seporated by 'I (e.g. Ye., (eoof/),. may empty values,
Proftttttt.
Table (toble)
## Verbalized Table (verbalized_ng table)
W9 Guidance guidance)
#* Question
(question)
The octual data of the toble is stored in the variable 'table' as a list of lists. store the 'answer" string You need to generote Python code within "python" code block.
print it.
Now, the executable python code the question: "python table aarra]
Figure 19: Prompt for symbolic reasoning in TableMaster. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to generate Python code to answer the question.
Prompt for T - Formatting
## Objective
provided with process guided reasoning with programming question related to toble. Your task is to onswer the question using the reasoning process.
## Table Definition
represented by by comma (e.g. 'A1, Year").
pairs,
pair consists of value
coll, seporated
Muttiple cells are seporated by (e'e.. VA,, Yeor Profit'). Cells may contain values, represented 'A1, Proftt,
#e Table (table)
## Textual Reasoning Process [textual_ reasoning process]
8B Progrommed Reosoning Process (symbolic reasaning process]
simple, number, phrase
Your respanse should be in the format of 'Answer xox (onswer the question).
Question: (question)
Answer:
Figure 20: Prompt for formatting in TableMaster. Blue text indicates placeholders for vari- ables within the prompt. The prompt guides the language model to format the final answer based on the given table, question, and reasoning process.
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Published as a conference paper at ICLR 2026
P PROMPTS USED IN ANALYSIS EXPERIMENTS
Prompt for Direct Baseline
Objjective
You are provided with o toble and question related to the table. Your onswer question directly based the
Toble
(table)
#t Question (question)
The onswer should be short and simple. It can be a number, word, phrase in the table, but not a full sentence. question directly:
Answer:
Direct prompt for table understanding in analysis experiment. Blue text indicates place- holders for variables within the prompt. The prompt guides the language model to directly give the final answer based on the given table and question.
Figure 21:
Prompt for Chain of Thought Baseline
Objective
You are provided with o table and question reloted to the table. Your tosk onswer the question step by step based on the toble.
## Toble (table)
(question)
The onswer should be short ond simple. It can be number, word, phirss the toble, but not a full sentence. Your response should end with 'Answer: xox' (onswer to the question). Now, the question
step:
Chain of thought prompt for table understanding in analysis experiment. Blue text indi- cates placeholders for variables within the prompt. The prompt guides the language model to give the answer step by step based on the given table and question.
Figure 22:
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Published as a conference paper at ICLR 2026
Prompt for Program of Thought Baseline
ail Objective
provided table question Your tosk onswer the question based on the table by writing python code salution.
#t Toble (table)
## Reasoning instructions
executable python
question.
2. The final answer should be variable named "onswer" in the code.
code response.
4. The python code should be the following format:
"python
your code here
Now, answer the question by writing python code as solution: Question: (question) "python
Figure 23: Program of thought prompt for table understanding analysis experiment. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to generate code to derive the answer based on the given table and question.
Prompt for Verbalization Baseline
#tt Objecttive
are provided with o toble string formot Your tosk convert the table detailed text description.
Toble (toble)
## Instructions
comprehensive description of the 2. Include all detoils and numerical values from the toble in your response.
3. not omit or summarize any information from table.
4. You external knowledge enhance your understonding the table, but the response remain faithful to the table's content.
Now, pleose provide the verbolized description of the table:
Figure 24: Prompt for table verbalization in analysis experiment. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to verbalize table to add detailed description.
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Published as a conference paper at ICLR 2026
Prompt for Textual Guidance Generation
#t Objecctie
are provided with o toble, and question refoted to the table.
Your tosk give step-by-step quidonce onswer the question based the tobie.
#t Toble Definition
The toble represented by cell-volue palrs, where each pair consists of cell oddress and value of the content in that cell, seporated by ge... Year')
Muttiple cells are seporated by 'I' (e.g. 'A1, YAar Colls may contain empty values, represented 'A1, Proffitt....
Toble (table)
You do not need Keep the reasoning process concise and clear. Control
You give reasoning guidonce that used later.
steps reasoning process range
Response
The response should be list of steps:
1. xxX
2.
Now, give me the guidonce to onswer the question step by step: Question: (question)
Figure 25: Prompt for textual guidance generation in analysis experiment. Blue text indicates place- holders for variables within the prompt. The prompt guides the language model to generate textual guidance that used for symbolic reasoning.
Prompt for Reasoning Strategy Evaluation
Question: (question)
Table: (table)
Method 1 Solution: (cot_ prediction)
Method 1 Reasoning: fcot, reasoning)
Method 2 Solution:
(pot_predine prediction) Method Reasoning: (oot_ reasoning)
evaluate method Respond in the follawing ISON format:
ft
"better method": or
B
Figure 26: Prompt for reasoning strategy evaluation in analysis experiment. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to select the better reasoning process after table reasoning.
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Published as a conference paper at ICLR 2026
Prompt for Reasoning Strategy Assessment in Adaptive Reasoning
You are provided with o table and question related to the table.
Your whether answering this question mathemotical colculation.
(table)
Mi Question
(question)
## Instructions
question easily using
respond
2. If the question involves comporison, respond with Folse.
counting o substantial (more than Sj items rows, respond 3. ff the question demands complex colculations multi-step mathematicol operations based on the toble's dota, the response should
question
be True.
4. simple anithmetic smoll scale counting that requires minimal computationol effort, respond with Faise.
Formot
The response should be JSON format:
ison
f
"ned calculation" true/folse
B
Figure 27: Prompt for reasoning strategy evaluation in analysis experiment. Blue text indicates placeholders for variables within the prompt. The prompt guides the language model to select the better reasoning strategy before table reasoning.
Prompt for Question Type Classification (Calculation Required)
Table:
(table)
Question:
[question]
Determine whether colculation required to onswer the question, or the question con be directly answered using the information toble
Provide your response in the following JSON format:
'need, calculation" true/folse
Figure 28: Prompt for classifying question type based on whether calculation is required in the analysis experiment. Blue text indicates placeholders for variables within the prompt.
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Published as a conference paper at ICLR 2026
Prompt for Noised Table Generation
Given table, need generate toble by disrupting the content in the table.
Table:
(toble)
Rules:
Your gool possible.
content row
follows difforent farmat diversity as
You cannot change structure the toble.
You cannot edd
columns.
You connot modify the column names in the first raw.
only alter format of the content each cell, the actual values.
You should not moke the content in each row within the some column in the some formot as much as possible.
Format Change Examples:
Change number format from
123,456. Change format from 2024-01-01 2024/01/01.
Simplify or obbreviote text content.
Provide your toble the following format: "json
"table": (1-,h---. b--
B
Figure 29: Prompt for generating noised tables in the analysis experiment. Blue text represents placeholders for variables within the prompt. The prompt instructs the language model to add noise by altering the cell content format based on given table.
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Published as a conference paper at ICLR 2026
Q
NOTATION TABLE
Table 14 provides a comprehensive list of the notations used throughout this paper, along with their corresponding descriptions. This table serves as quick reference help readers better understand the concepts presented in our work.
Table 14: Notation used throughout the paper
Notation
Description
General
Given question query
Generated answer
Input table
TW
Wild table before normalization Normalized table
Tw
TF
Table-of-Focus
C
Cell in the i-th and j-th column
m, n
Number of rows and columns the table
Table Structure Understanding
H
Set of top headers
K
Key column serving row identifier
Candidate column set
CO
Selected relevant columns
R
Selected relevant rows
Peek size for table processing
Table Content Understanding
T
Verbalized table (natural language text)
a',6'
Number of refined columns and rows after reconstruction
Table Reasoning
Selected reasoning strategy
T
Textual reasoning strategy
Symbolic reasoning strategy
G
Textual reasoning guidance
P
Program executor (Python/SQL))
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Published as a conference paper at ICLR 2026
R THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, large language models (LLMs) were used only to aid with writing and polishing the manuscript. Specifically, LLMs were employed for grammar correction, phrasing suggestions, and improving readability. All research ideas, methodological contributions, theoretical analyses, and experiments were entirely conceived, designed, and executed by the authors without the involvement of LLMs. The authors take full responsibility for the scientific content of the paper.
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Reviews:
[{"review_id": "0KGpPCYL1a", "reviewer": "Reviewer_MV4i", "summary": "This paper introduces TableMaster, a novel framework designed to enhance how Large Language Models (LLMs) understand tabular data. The research addresses four key obstacles: difficulty in data localization, semantic deficiency, numerical inaccuracies, and inflexible symbolic reasoning. TableMaster employs a multi-faceted strategy, beginning by isolating relevant data into a \"table-of-focus\" and then using \"verbalization\" to enrich it with semantic context. The framework integrates program-aided reasoning and features an adaptive mechanism that dynamically balances textual and symbolic approaches based on the query. This method has achieved state-of-the-art performance on the WikiTQ and TabFact benchmarks, notably reaching 78.13% accuracy on WikiTQ with GPT-4o-mini, significantly surpassing existing baselines.", "questions": "Please refer to the weaknesses part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper demonstrates strong empirical rigor through comprehensive experiments across multiple benchmark datasets and baselines. The thorough ablation studies effectively validate the contributions of individual components, providing clear evidence of the method's effectiveness.\n2. The authors developed a robust system for analyzing and extracting information from general tabular data, with carefully designed modules compatible with various language model backbones.", "weaknesses": "1. Several key experiments are missing from the main paper, such as the analysis of adaptive reasoning. Including these results in the main body would strengthen the paper.\n2. The framework is thoughtfully designed and comprehensive, but many of its sub-tasks have been extensively studied, with closely related methods already proposed. As a result, the incremental novelty appears limited. \n3. In the related work section the connections of similar methods to this work are not clear. It would help to position the framework relative to each major line of work (what is shared, what differs, and why those differences matter), and to articulate the specific gaps in prior methods that this paper addresses.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8r3qbKILjp", "reviewer": "Reviewer_sJMb", "summary": "This paper addresses table understanding with language models by identifying four key challenges: (i) difficulty in locating target data, (ii) deficiency of table semantics, (iii) numerical inaccuracy in textual reasoning, and (iv) semantic inflexibility in symbolic reasoning. The authors propose TableMaster, a comprehensive framework that integrates multiple solutions including table-of-focus construction, table verbalization, and adaptive reasoning that dynamically switches between textual and symbolic approaches. The method is evaluated on WikiTQ, TabFact, and FetaQA datasets, showing improvements over existing baselines.", "questions": "1. What are the minimum model capabilities required for TableMaster? Have you tested on 7-13B parameter models? At what model size does the framework start to break down?\n2. Given that each component is well-established, what specific insights or contributions does TableMaster provide beyond engineering integration?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- The paper provides a thorough empirical analysis of challenges in table understanding, with systematic experiments examining the impact of table size, verbalization, and different reasoning approaches.\n- TableMaster achieves notable improvements across various large-scale LLMs (GPT-3.5-turbo, GPT-4o-mini, LLaMA-3 70B).", "weaknesses": "- While the integration is well-executed, most individual components (sub-table extraction, table verbalization, program-aided reasoning) have been proposed in prior literature. The novelty primarily lies in their combination rather than in introducing fundamentally new techniques.\n- The section 3-4 can be condensed to leave more space for experiment and analysis. Currently, most the results are in the appendix.\n- The evaluation focuses exclusively on large-scale models. It remains unclear how TableMaster performs on smaller (7–8B) models or what minimal model capabilities are required for it to function effectively.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jniY3cUyfG", "reviewer": "Reviewer_oMBW", "summary": "This paper proposes TableMaster, a framework designed to enhance language models'  table understanding capabilities. The paper identifies several challenges: difficulty in locating target data, semantic deficiency in tables, numerical inaccuracies in textual reasoning, and semantic inflexibility in symbolic reasoning. To address these, TableMaster integrates multiple solutions including table-of-focus construction, table verbalization, program-aided reasoning, and adaptive reasoning that dynamically selects between textual and symbolic approaches. Extensive experiments on WikiTQ, TabFact, and FetaQA datasets show that TableMaster achieves good performance.", "questions": "please refer to the weakness", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper provides a structured analysis of four fundamental challenges in table understanding, with each challenge directly linked to a targeted solution. \n\n2. TableMaster integrates multiple techniques (table-of-focus, verbalization, adaptive reasoning) into a pipeline. \n\n3. The paper conducts extensive experiments across diverse datasets and LLMs.", "weaknesses": "1. The core contributions of the paper are primarily engineering-focused. The paper lacks novel advancements in LM architecture or reasoning mechanisms specific to table understanding.\n\n2. Experiments are concentrated on clean, structured tables from specific domains. The framework's performance on real-world noisy tables, hierarchical tables remains insufficiently explored, for example, the BIRD dataset. \n\n3. The dynamic strategy selection relies on LM judgment without robust error handling. \n\n4. While efficiency is discussed, no actual latency measurements or comparison with simpler baselines are provided. The multi-step process (verbalization, reconstruction, adaptive reasoning) likely introduces significant inference time overhead.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)