TableRAG: Million-Token Table Understanding with Language Models
Authors: Si-An Chen
Lesly Miculicich
Julian Martin Eisenschlos
Zifeng Wang
Zilong Wang
Yanfei Chen
Yasuhisa Fujii
Hsuan-Tien Lin
Chen-Yu Lee
Tomas Pfister
Venue: NeurIPS 2024 poster (2024)
Keywords: large language model
large scale
tabular reasoning
retrieval
LLM
Primary area: natural_language_processing
TL;DR: We introduce TableRAG, a framework that enables LLMs to handle large tables by retrieving only essential schema and cell data. Additionally, we provide two new benchmarks, ArcadeQA and BirdQA, to evaluate performance on real-world large tables.

Abstract: Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables.
However, these methods often require the entire table as input, leading to scalability challenges due to the positional bias or context length constraints.
In response to these challenges, we introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework specifically designed for LM-based table understanding.
TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information  before providing it to the LMs.
This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss.
We have developed two new million-token benchmarks from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's effectiveness at scale.
Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-art performance on large-scale table understanding.

TableRAG: Million-Token Table Understanding with Language Models
Si-An Chen'; Lesly Miculicich', Julian Martin Eisenschlos Zifeng Wang", Zilong Wang"; Yanfei Chen', Yasuhisa Fujii", Hsuan-Tien Lin', Chen- Yu Lee², Tomas Pfister
National Taiwan University, Google Cloud Al Research, "Google DeepMind, 'UC San Diego
Abstract
Recent advancements in language models (LMs) have notably enhanced their ability reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables. However, these methods often require the entire table input, leading scalability challenges due to the positional bias context length constraints. In response to these challenges, we introduce TableRAG, Retrieval- Augmentee Generation (RAG) framework specifically designed for LM-based table understanding, TableRAG leverages query expansion combined with schema and cell retrieval to pinpoint crucial information before providing it to the LMs. This enables more efficient data encoding and precise retrieval, significantly reducing prompt lengths and mitigating information loss. We have developed two new million-token benchmarks from the Arcade and BIRD-SQL. datasets to thoroughly evaluate TableRAG's effectiveness at scale. Our results demonstrate that TableRAG's retrieval design achieves the highest retrieval quality, leading to the new state-of-the-ar performance on large-scale table understanding. The implementation and dataset will be available /tttps://github.... com/ google- research hwwgggooolgeeeeee rerearcctrrreerreeeeeeeeeeeeeeee wtwpeeaccceeeeeemtteeeeeeeeeeeee htt////attettteeeaaaaeaaa
Introduction
Recent advancements have leveraged language models (LMs) for table understanding tasks [5]. This typically involves prompting LMs with entire tables to ensure thorough analysis [24, 3, 11, 22, 10, 29]. However, scaling larger tables poses several challenges. First, LMs face context-length constraints; for example, a medium-sized table with 100 columns and 200 rows translates into over 40,000 tokens, surpassing the limits of popular LMs like LLaMA and the GPT series. Additionally, long contexts can degrade reasoning capabilities, a phenomenon known as the Lost-in-the-Miin -iiddld [9]. Finally, computation costs and latency increase significantly with table size. Therefore, developing scalable solution that efficiently handles large tables remains a critical area of research.
Naive approaches making large table understanding feasible, such truncating the table only reading the schema, often result in the loss of critical information. To address this, previous works have attempted to retrieve key rows and columns construct sub-table that captures essential information for answering queries [8, 17]. These methods encode entire rows and columns into sparse dense embeddings reduce token costs for LMs, yet they still face computational and performance challenges with extremely large tables. Encoding entire rows and columns requires processing the whole table, which is infeasible for large tables containing millions of cells. Furthermore, compressing
Work done while the author was a student researcher at Google Cloud Al Research. Correspondence to: Si-An Chenksianchen, kevi i@gmari 1. com>, Chen Yu Lee<chenyuleeeggog le com>
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Read5chema RowColletrieval TableRAG
36.6
30
F1 score
23.4
23.7
(a) Read Table
(b) Read Schema
20
17.6
10-
Column Retrieval
Cell Retrieval
(c) Row-Column Retrieval
(d) Schema-Cell Retrieval (Ours)
(e) Retrieval Performance on ArcadeQA
Figure 1: Comparison between table prompting techniques for LMs. (a) - (d): Data included the LM prompt (shaded region). (a) Read Table: The LM reads the entire table, which is often infeasible for large tables. (b) Read Schema: The LM reads only the schema, consisting of column names and data types, resulting in loss of information from the table content.
(c) Row-Column Retrieval: Rows and columns are encoded and then selected based on their similarity to the question. Only the intersection of these rows and columns is presented to the LM. It still infeasible encode all rows and columns for large tables. (d) Schema- Cell Retrieval (our work): Column names and cells are encoded and retrieved based on their relevance to LM-generated queries about the question. Only the retrieved schema and cells are provided to the LM, enhancing efficiency in both encoding and reasoning. (e) Retrieval results on the ArcadeQA dataset show that TableRAG outperforms other methods in both column and cell retrieval, thereby enhancing the subsequent table reasoning process. The Read Table technique is excluded as reading entire tables typically infeasible in this context.
long rows and columns into a fixed-size embedding can obscure semantic meaning, particularly when the table contains diverse or less semantically rich content (e.g.. numerical values).
In this work, we introduce, Ta, a scalable framework that leverages retrieval- aagmmnted generation (RAG) for LM-based table understanding. We illustrate the key differences between prior table prompting approaches and the proposed TableRAG in Fig.
Our method integrates schema retrieval and cell retrieval to extract essential information from tables, enabling program-aided LM agent to solve queries based on the provided information. Schema retrieval allows LMs to identify crucial columns and their data types solely by column names, avoiding the need to encode entire columns. Cell retrieval enables the identification keywords for indexing pinpointing columns that contain necessary but hard-to-fing information missed by schema retrieval alone. To build the database for cell retrieval, TableRAG encoded each cell independently, addressing the issue faced when encoding entire TableRAG only encodes distinct and the most frequent categorical values, reducing the encoder's token cost (as shown in Fig. 3) and operating within user-specified budget effectively. Both retrieval processes in TableRAG are enhanced by query expansion [21] with dedicated prompts for schema retrieval and cell retrieval, ensuring thorough and relevant data extraction.
and columns. Furthermore,
Existing TableQA benchmarks typically feature only small tables with dozens of rows and columns. To verify the scalability of TableRAG for larger tables, build two benchmarks sourced from the real-world Arcade [26] and BIRD-SQL [7] datasets. Additionally, to assess performance across various scales, generated synthetic data expanding tables from the TabFact dataset to larger sizes, while maintaining consistent questions and key table content for evaluation. Our experimental results demonstrate that Ta outperforms existing table prompting methods significantly and consumes fewer tokens across different table sizes.
Our contributions can be summarized as follows:
We conduct the first extensive study exploring the application L.Ms to large-scale, real-world tables, analyzing the scalability and limitations of existing LM-based table reasoning approaches.
We introduce two new real-world benchmarks derived from Arcade and BIRD-SQL, along with expanded synthetic dataset from TabFact. These datasets include tables ranging from tens to

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millions of cells (Table 7), enabling comprehensive evaluation of LM capabilities across various table scales.
We develop TableRAG, an efficient framework for LM-based table understanding that demonstrates superior performance on large tables while minimizing token consumption. We conduct detailed ablation study to validate the effectiveness of each component within TableRAG.
2 Related Work
Research in table understanding has evolved from fine-tuning specialized architectures [6, 4] to leveraging LMs in few-shot setups [22, 10), capitalizing on the emerging reasoning capability of these models. To enable LMs to understanding tables, table information must be included in the prompts. Representative works like Dater [24], Binder [3] and subsequent works [22, 29, 11, 10, 28] typically require LMs to process entire tables. These methods, while effective in leveraging LMs' reasoning and programming capabilities for question answering, are often not feasible for larger tables due to the context length constraints.
To address the limitations of processing full tables, two main streams have emerged: schema- based and row-column retrieval methods. Schema-base based methods, such as Tex2SQL [30] and more recent developments [20, 18, 13, 14]. focus primarily on schema understanding to generate SQL commands. This significantly reduces token complexity but at the cost of omitting valuable cell data. Row- column retrieval methods, such as ITR [8] and TAP4LM [17], attempt to address scalability issues by encoding and retrieving essential rows and columns. While this strategy reduces input lengths for reasoning, it still requires substantial computation to encode entire rows and columns and can suffer from poorer embedding quality for long sequences.
Our approach, TableRAG, innovates by combining schema retrieval with selective cell value retrieval and frequency-awan truncation. This creates an efficient table prompting method where the input length to LMs is independent of table sizes. This strategy significantly reduces computational demands while preserving the benefits of accessing table contents, leading to superior performance compared to other methods across various scales.
TableRAG
3.1 Motivation
An illustration of the workflow of our method is shown in Fig. The core idea is to incorporate schema retrieval and cell retrieval to obtain necessary information for solving the questions by program-aided LMs. In practice, we often find that processing the entire table for LMs is unnecessary. Instead, the critical information usually lies in specific column names, data types, and cell values that directly relate the question. For example, consider the question "What the average price for wallets?" To address this, program may simply need to extract rows related to "wallets" and then calculate the average from the price column. Knowing just the relevant column names and how "wallets" are represented in the table suffices to write the program. Our method, TableRAG, leverages the observation and addresses the context length limitations by RAG.
3.2 Problem Formulation
In harge-scale table understanding, we are presented with a table T, represented as T My l J, S M, where N is the number of rows, M is the number of columns, and Vij is the cell value at row i and column j. We address a natural language question Q and aim to produce an answer A by LM L. Given the often impractical size of T for direct processing, a table prompting method P is employed to transform T into a more manageable prompt P(T),, allowing L. to effectively generate the answer A L(P(T)). Our objective significantly reduces the size of the prompt, IP(T)), compared to the original table, [T'), making it feasible for the LM process large tables.
develop efficient P that
3.3 Core Components of TableRAG
Tabular Query Expansion effectively manipulate the table, it is essential to pinpoint the precise column names and cell values necessary for the query. Unlike previous works [8, 17] that may use the question as a single query, we propose generating separate queries for both schema and cell values. For instance, for question like "What is the average price for wallets?". the LM is prompted
3

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Tabular Query Expansion
Schema & Cell Retrieval
Schema Query
Schema
Program- Aided LM
Answer
Question
A
Call Query
Cell DB (Cot-Cal Paino
Relevant Schema Cells
Table
LM
Program
Figure 2: Workflow of the TableRAG Framework. The table is utilized to build the schema and cell databases. question is then expanded into multiple schema and cell queries by LMs. These queries are sequentially utilized to retrieve schemas and column-cell pairs. The top K candidates from each query are combined and fed into the LM solver's prompt to answer the question. The pseudocode and an answering example on ArcadeQA can be found in Alg. and Fig. 8 respectively.
to produce potential queries for column names such "product" and "price", and for relevant cell values like "wallet". These are then used to retrieve relevant schema and cell values from the table.
Schema Retrieval Following the generation of queries, the schema retrieval fetches pertinent column names using pre-trained encoder Senc. which encodes the queries and matches them against the encoded column names to determine relevance. The retrieved schema data includes column names, data types, and example values. We convert columns to integer, float, or datetime data types when feasible; otherwise, we keep them categorical columns. For columns identified numerical or datetime data type, we display the minimum and maximum values as example values. Conversely, for categorical columns, present the three most frequent categories example values. We combine the top-K retrieval results for each query and rank them by their similarity to the closest query. The retrieved schema provides structured overview of the table's format and content that will be used for more targeted data extraction.
Cell Retrieval Following schema retrieval, we proceed to extract specific cell values needed to answer the question. This involves building database of distinct column- pairs from T, denoted as V Uig (Cj, Vij), where Cj is the column name of the j-th column. In practice, the set of distinct values is often much smaller than the total number of cells, as illustrated in Fig. 3. This discrepancy significantly enhances the efficiency of cell retrieval.
Cell retrieval plays an crucial role in TaG It enhances LM's table understanding capabilities in:
1. Cell Identification: allows LMs to accurately detect the presence of specific keywords within the table, which is essential for effective indexing. For example, it can distinguish between terms like "tv" and "television". ensuring that searches and operations are based on precise data entries.
2. Cell-Column Association: It also enables LMs associate particular cells with their relevant column names. This crucial when questions pertain to specific attributes, such as linking the term "wallet" directly to the "description" column, thereby enables row-indexing.
It should be noted that cell retrieval is primarily beneficial when indexing by cell values is required. In other scenarios, simply knowing the schema may suffice. For example, to answer the question "What is the average price?", identifying the relevant column name for prices is sufficient because the actual computation of the average can be handled programmatically. Nevertheless, cell retrieval still improves TableRAG with additional key values from the table, which will be shown in Sec 4.8.
Cell Retrieval with Encoding Budget In the worst case, the number of distinct values could match the total number of cells. To maintain the feasibility of TableRAG in such cases, we introduce cell encoding budget B. the number of distinct values exceeds B, we restrict our encoding to the B most frequently occurring pairs, thus improving efficiency when processing large tables. It is important to note that the encoding budget impacts only the cell retrieval process. Even if a cell not included for retrieval, the subsequent solver can still access the cell if its column name is known

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Table I: Token complexities of primary table prompting approaches without truncation. Note that Read Schema does not aware of any cell content. N: number of rows, M: number of columns, K: number of top retrieval results, D: number of distinct values in the table. It is
25
generally observed that K < M D N.
Count
Table Prompling Approach Methods
Token Complexity
Dater [24)
Binder
10-
MulliTableQAA [11]
Read Table
Chain-of.Table [22]
Reasoning: ((NM)
5
Mix-SC [10]
TableLlama [28]
0.0
0.2
0.4
0.6
ReAcTable [29]
distinct values cells
MAC-SQ1.
Figure 3: Histogram of the proportion of number of distinct values to number of cells in ArcadeQA and BirdQA. The figure in- dicates that for most tables, the number of distinct values (D) are much smaller than the number of cells (NM).
SQL-PaLM
Read Schema
Reasoning: ((3))
DIN-SQL [13]
DTS-SQL [14]
Row Col Retrieval
ITR [8]
Encoding: O(NM)
TAP4LM
Reasoning: O(K3)
Encoding: O(D)
Schema Retrieval
TableRAG (our work)
Reasoning: O(K)
through schema retrieval or other cells. For instance, shown in Fig. the "description" column contains free-form text, which likely results high number unique values, many of which may be truncated due the cell encoding budget. Nevertheless, long as the solver recognizes the column, it can still perform operations on that column to extract the required information.
Program-Aides Solver After obtaining the column names and cell values relevant to the question, the LMs can use these information to effectively interact with the table. TableRAG is compatible with LM agents which can interact with tables programmatically. In this work, we consider ReAct [23], which is a popular approach to extend the capability of LMs and has been used in recent literature to achieve ofte-of-theeart results in Table QA benchmarks [10, 29]. An example of how TableRAG works with ReAct illustrated in Fig.
3.4 Token Complexity Analysis
The efficiency and latency of invoking LMs are significantly influenced by the number of input tokens. Therefore, we focus on analyzing token complexity. which refers the complexity introduced by the number of tokens in the LM's input. We examine the token complexity of each operation within the TableRAG framework and other table prompting methods. We assume that the length of a column name, a cell value, and the question are all O(1). Note that N represents the number of rows, M represents the number of columns, budget, and K is the number of top retrieval results.
the number of distinct text cell values, B is the cell encoding
The token complexity of primary table prompting approaches shown in Table 1.
Read Table feeds the entire table to LMs, resulting ONN MM) tokens for reasoning.
Read Schema only feeds the schema to LMs, which is ((M) tokens, but loss the information from the table content.
Row- Row-Column Retrieval encodes all rows and columns embeddings, resulting O(NM) tokens for encoding. Then it retrieves top-K rows and columns to construct K x K sub-table for reasoning, which O(K²).
We analyze the token complexity of each step in TableRAG as follows:
The prompt to the LM is primarily based the question, which typically comprises only a few words, thus the token complexity for this component O(1).
Tabular Query Expansion:
Building Schema Database: Each column name is encoded using the encoder function fene- resulting in a token complexity of O(M) for the encoder.
Building Cell Database: This operation involves encoding distinct column-value pairs using fenc- The total number of distinct pairs D is capped at B when exceeding the limit. Therefore, the token
5

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complexity for building the cell database is ( D. B)), ensuring that the most frequent data is processed to optimize performance. Note that takes (M) CPU time to build the cell database, but the computation cost is negligible compared to LM calls. For example, computing the distinct values from 10 elements takes only 60 ms on a personal laptop, while it takes 1 second to encode merely hundreds of words on CPUs
LM Reasoning:
The query expansion process generally produces approximately 3-5 queries, which are considered (1). Each query then retrieves the top- results, leading to total complexity of O(K) for the columns and cell values included in the LM's prompt. This step ensures that the LM processes only the most pertinent information from the table, enhaneing efficiency and effectiveness in generating responses.
Overall, given that typically M B and D, the token complexity for TableRAG is O(min(D) D, B)) for encoding and O(K) for reasoning, with neither being dependent on the overall size of the table. Consequently, TableRAG maintains manageable costs even for large tables, optimizing computational resources and response times for large-scale table understanding tasks.
4
Empirical Studies
4.1 Dataset
Existing TableQA benchmarks such as TabFact [2] and WikíTableeQA [12] feature small web tables that fall within the context limits of most LMs, while others like Spider dataset [27] are designed purely as text-to-SQL tasks with access only the schema. To better assess reasoning capabilities over larger, more realistic tables, we have developed two extensive table QA datasets, ArcadeQA and BirdQA, derived from the Arcade [26] and BIRD-SQL [7] datasets, respectively. ArcadeQA comprises tables with an average of 79, 000 rows and a maximum of 7 million cells, while BirdQA tables feature an average of 62, 000 rows with a peak at 10 million cells. Furthermore, we expanded TabFact to include synthetic tables ranging from 100 x 100 to 1, 000 x 1, 000, equivalent to a million of cells, to examine the impact of different table sizes under the same question and key information. Detailed methodology for dataset generation and the statistics of these datasets are provided in Appendix C and summarized in Table 7.
4.2 Baseline
We compare TableRAG to four different approaches for incorporating table information into LMs. To ensure fair comparison of table access strategies, have implemented the baselines and our TableRAG based on the same PyReAct [10, 29] solver.
ReadTable This common approach in recent research includes embedding the entire table in the prompt, then asking the LMs to solve the problem with the provided table information. This approach is limited by the context length of the LMs. We discard data instances and consider them failures when the table size exceeds the context length.
ReadSchema Widely-used in Text2SQL literature, this method assumes that table content is not directly accessible. It incorporates column names and data types into the prompt, enabling LMs to execute commands based on these column names without direct access to the row data.
RandRowSanpling This method is prevalent rule-based sampling approach [17] that randomly selects rows from the table with equal probabilities. When the total number of rows exceeds K, we select K rows form representative sample. This baseline is employed to underscore the benefits of more targeted retrieval methods, illustrating how they can provide more relevant and efficient data selection compared random sampling.
Roltreletii This state-of-the-ard approach reduces table sizes prior to LM reasoning. Follow- ing the methodology of Sui et al. [17], we encode and columns and then retrieve the top K rows and columms based on their similarity to the question's 's embedding to form a sub-table. Since encoding all rows and columns requires 2NM tokens, which is impractical for large tables with B millions of cells, we truncate the tables to 2M rows. This truncation limits the number of tokens encoded to B, aligning with the token limit in our TableRAG implementation.
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Table Performance comparison of table prompting approaches on ArcadeQA and BirdQA across
LMs.
GPT 3,5 Turbo
Gemini 1.0 Pro
Mistral Nemo
Method
ArcadeQA
BirdQA
ArcadeQA
BirdQA
ArcadeQA
BirdQA
ReadTTalle
4.6
9.1
1.5
4.9
5.4
8.4
ReadSchema
43.1
40.3
30.8
31.8
32.3
35.7
RandRowSSapllig
42.3
34.7
20.8
25.3
28.5
33,4
RowColRetrieval
37.7
39.6
16.9
21.8
30.0
36.4
TableRAG
49.2
45.5
42.3
44.2
46.2
45.1
Table 3: Evaluation of retrieval performance. TableRAG shows best retrieval quality on all tasks. R: recall, P: precision.
ArcadeQA
BirQQA
Column Retrieval
Cell Retrieval
Column Retrieval
Cell Retrieval
Method
F1
R
P
Fl
P
F1
R
P
Fl
ReadSchema
100.0
12.4
23.4
0.0
0.0
0.0
100.0
30.8
41.6
0.0
0.0
RandRow Sampling
100.0
12.4
23.4
66.5
0.5
3,5
100.0
30.8
41.6
48.9
1.7
7.2
RowColRetrieww
99.6
12.5
23.7
62.8
0.6
5.0
93.5
31.1
42.4
52.7
4.6
14.0
TableRAG
98.3
21.2
36.6
85.4
3.4
17.6
95.3
36.0
48.8
87.4
5.7
17.3
4.3 Experimental Setup
Our experiments employ GPT-3,5-turbo [1]. Semini-L.O-PPro [19] and Mistrra--emo--nnsttueee as LM solvers. In ablation study, we use GPT-3.5-turbe if not specified. We use OpenAl's text- embedding-33large" as the encoder for dense retrieval. For TableRAG. we set the cell encoding budget B = 10, 000 and the retrieval limit K 5. For RandRowSampling and RowColRetrieval, we increase the retrieval limit to = 30. Each experiment conducted 10 times and evaluated by mojority-voting ensure the stability and consistency. The evaluation metric is the exact-match accuracy if not specified.
4.4 Main Results
In evaluations across the datasets shown in Table 2, TableRAG consistently outperformed other methods, achieving the highest accuracies across all LMs on both ArcadeQA and BirdQA. The ReadTable method underperforms on both in ArcadeQA and BirdQA, indicating it suffers from long context. Among the three LMs, GPT 3.5 Turbo consistently delivers the best performance, regardless of the table prompting method used. These results demonstrate the effectiveness of TableRAG in handling large-scale TableQA tasks.
4.5 Retrieval Performance Analysis
To better understand the retrieval quality of various table prompting approaches, Table 3, we assessed the recall, precision and fl score for the prompts fed to LMs for reasoning, The ground truths are extracted from the program annotations in the ArcadeQA and BirdQA datasets. In column retrieval, while all methods achieved high recall due the small number of columns, TableRAG demonstrated superior precision across both datasets, indicating its effectiveness in identifying the most relevant columns concisely. In contrast, ReadSchema and RowColRetrieval showed lower precision, suggesting that they retrieved more irrelevant columns. For cell retrieval, TableRAG consistently outperformed other methods on all metrics. TableRAG's high recall in cell retrieval marks a significant improvement over other table prompting methods, indicating it can retrieve most necessary cells for the subsequent reasoning. In summary, this analysis underscores TableRAG's efficacy in retrieving essential information in both the column and cell aspects.
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ReadTable
RawColRetrieval
RandRowSSmmlling
RowColRetrieval
TableRAG
ReadSchema
TableRAG
ReidiRowSampling
ArcadeQA
BirdQA
55
90
50
Accuracy
80
Accuracy
60-
30
2000
4000 6000
2000 4000 6000 8000 Prompt Length
original
50
100
500
1000
Prompt Length
Synthetic
Figure 5: Impact varying top retrieval results (K). Different K values influence both prompt length and accuracy. Each point labeled with its corresponding K value. TableRAG retrieves the top K schema and cell values, RandRowSSmmiin selects K random rows, and RoweteRetrieval retrieves K and K columns.
Figure 4: Performance evaluation of Synthetic Tabfact in varying scales. TableRAG shows consistently superior results, and it decreases gracefully com- pared competitive methods.
Table 4: Comparison of TableRAG with state- of-the-art methods WikiTableeA.
Table 5: Performance comparison of different retrieval approaches in Ta on Arcad- eQA and BirdQA.
Method
Accuracy
TaBERT [25]
52.30
Method
ArcadeQA
BirdQA
Text-to-SQL. [15]
52.90
Binder [3]
56.74
TableRAG (BM25)
37.7
35.7
Dater [24]
52.81
TableRAG (Hybrid)
46.2
44.5
TableRAG (Embed)
49.2
45.5
TableRAG (Ours)
57.03
4.6 Scalability Test on TabFact
To understand the performance across varying table sizes under similar conditions, we create set of synthetic data from TabFact with table sizes ranging from 50 x 50, 100 x 100, 500 x 500, and 1000 1000. The synthetic data allow us analysis how table prompting methods perform different scales with the same questions and key table contents. The results are shown in Fig. 4. ReadTable exhibited strong initial accuracy with the original data but failed dramatically as table sizes increased, infeasible for sizes 100 and beyond due to the context length limitations. Conversely, TableRAG demonstrated the most consistent and scalable performance, decreasing moderately from 83. to 68.4 as table size increased to 1000 rows and columns, showcasing its effectiveness in understanding larger tables. Both ReadSchema and RowColRetrieval showed declines performance with increasing table size, yet maintained moderate accuracy, highlighting their relative scalability compared to ReadTable but less effectiveness than TableRAG in handling large tables.
4.7 Comparison with State-of-the-Art WikiTableQQA
To evaluate performance on small-scale TableQA datasets, we compared TableRAG with state-of- the-art approaches that rely reading entire tables, using the commonly used WikiTableQA [12] benchmark. As shown in Table 4, TableRAG surpasses all existing methods, including TaBERT [25], Text-to-SQL. [15], Binder [3], and Dater [24]. This highlights TableRAG's effectiveness, even in small-scale settings. These results confirm that, while TableRAG is designed for large-scale TableQA, its approach is versatile and maintains state-of-the-an performance across different table sizes and complexities.
Ablation Studies
Impact of Retrieval Methods in TableRAG: Table 5 compares different retrieval approaches within TableRAG AG. BM25 [16], a well-known statistical retrieval method, excels efficiency and can process all cells but lacks semantic understanding. We also compare it with our embedding based retrieval and

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RolCoeeetrevall TableRAG
ArcadeOA
BirDOA
TableRAG
W/o Query Expansion
gpt 3.5.tbbbbbboooooo
gemini-pre pro
50-
55
Accuracy
50
49.2
45
Accuracy
45
43.8
44.2
41.9
42.3
40-
36.7
100 s00 1000 5000 10000 Encoding Budget
100 soo 1000 s000 10000
35
Enzoding Budget
30
Figure Impact of cell encoding budget B. TableRAG retrieves from the B most frequent cells, maintaining robust performance even with smaller budget. RowColSampling truncates more rows as B decreases, showing greater sen- sitivity budget changes and generally under- performing compared to TableRAG.
ArcadeQA
BirdQA
ArcadeQA
BirdQA
Figure 7: Effect of query expansion. Query expansion consistently improves TableRAG across all scenarios, indicating that provides better coverage of user intents.
Table 6: Ablation study of schema retrieval (Rows 1 vs 3 and 2 4) and cell retrieval (Rows 1 vs 2 and vs 4). The first column indicates whether the I.M processed all schemas or only retrieved schemas. The second column indicates whether the LM ignored cell values or processed retrieved column-cell pairs. Schema retrieval leads to an improvement in accuracy of up to 9.4%, while cell retrieval results in an increase of up 11.5%.
GPT 3.5 Turbo
Gemini 1.0 Pro
Schema
Cell
ArcadeQA
BirdQA
ArcadeQA
BirdQA
all all retrieval
43.1
40.3
30.8
31.8
retrieval
48.5
42.5
42.3
41.6
none
46.9
44.8
36.9
42.2
retrieval
retrieval
49.2
45.5
42.3
44.2
hybrid approach that combines scores from both methods. The results show that smbedding-basee retrieval achieves the best performance, outperforming both BM25 and the hybrid method, despite not processing the entire table due encoding constraints. This underscores the importance of semantic understanding in retrieval, where embedding-base methods offer better comprehension of table data, significantly enhancing TableRAG's performance.
Number of Top Retrieval Results K: Fig 5 illustrates the impact of varying the number of top retrieval results (K) on the performance and the token cost for the subsequent LM reasoning. The results demonstrate that that increasing the number K, while increasing the prompt lengths, does not consistently improve performance. Though larger K allows LMs access more information, also results longer context which can exacerbate the lost-in-the-miinn middle phenomenon. In contrast, TableRAG excels by requiring fewer K values, thus reducing the context tokens needed and lowering subsequent reasoning costs while still outperforming other methods.
Encoding Budget B: The results from Fig. 6 demonstrate how different token encoding budgets (B) affect the performance of TableRAG and RowColRetrieval. While higher budget theoretically allows for more information to be retrieved, the results show that it does not always lead to better performance. Specifically, RowColRetrieval shows a decline in performance with increased budgets, potentially due to the retrieval of more rows that complicate selecting the correct ones and produce noisier embeddings from longer column sequences. In contrast, TableRAG maintains consistent performance across various budgets, indicating that its approach of building the corpus by cell frequency effectively captures essential information even with limited budgets.
The effectiveness of our query expansion method is analyzed in Fig. 7. The results demonstrate that query expansion consistently enhances TableRAG's performance across various datascts and LMs.
Query Expansion:

---

Schema Retrieval and Cell Retrieval: We analyze the impact of schema retrieval and cell retrieval on performance Table The results demonstrate that schema retrieval consistently enhances reasoning performance across datasets and LMs, with maximum improvement of 9.4%, regardless of whether cell values are considered. The results indicate that even for tables with small number of columns (average 20.5 columns in ArcadeQA and 11.1 columns in BirdQA), schema retrieval is still helpful to only provide relevant columns for the subsequent reasoning. Similarly, cell retrieval consistently improves accuracies across all datasets and LMs, with maximum improvement of 11.5%, indicating cell retrieval can effectively extract key information from the table contents.
5 Conclusion
In this work, have presented TableRAG, the first framework demonstrate effective and efficient LM-based table understanding with millions of tokens. To fill in the evaluation gap, we have introduced three million-token scale table understanding benchmarks. TableRAG's retrieval design, combining schema and cell retrieval with frequency-awar truncation, significantly reduces computational costs and token usage without sacrificing performance. Our extensive experiments on both real-world and synthetic datasets showcase TableRAG's superior performance across various table sizes. TableRAG paves the way for future research on even larger and more complex table understanding tasks.
Limitations
Theoretically, the worst-case complexity for cell retrieval could reach O(NM) if all cell values are distinct, potentially leading loss of crucial information even with frequency- truncation. Also, our evaluation is limited to QA and verification tasks due to the scarcity of large-scale table benchmarks. Finally, we focus on benchmarking on comparing TableRAG with other representative table prompting methods using the same LM solver, rather than against the latest state-of- -frte----th techniques, to specifically assess its efficacy on large tables.
Acknowledgement
S.-A. Chen and H.-T. Lin are partially supported by the National Taiwan University Center for Data Intelligence via NTU- 13U-1919 and the Ministry Science and Technology Taiwan via NSTC 1 113-2628- -028--0002--03.
10

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12

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A TableRAG Example
Tabular Query
Relevant Information
Program Aidedd LLM Solver
Expansion
Retrieval
Question
Question
What &s the average price for wallets?
price
Bbin
3648" "B209" "3500"
Schema Query
Thought
|need fiter out the prices from the given vollmee
product rome price, cenegery
desoistion
Baing
Lpatec... "Miro....
Action
d7 p[prise]] ]
Alprice dfprioo perreptec S "Lastypsteow))
Call Quary
Obsarvation sucrnoo
sollet order status
Thought
Now. cfdTdesscrppooo
bother wallets only
wother
Build Database
Cell Retrieval
Action
stronntene Vhrolleensoiallll (
Column
Cell
Observation
puccesal
Schema Database
otdcr_stalus
Delvc to Buyer
Table
Name
Type
Example Valuea
description
neceer Wallet
Thought
average prico
deserption
Furse
Action
kalll pricee.
Cell Database
dosol plon
Moris Walled with
Obssrvation
75100099000007
Colurn
Call Value
Thought
Belore grvng
pink about the ossonvatios
ahould
Anawwe
442 7010008050667
Figure 8: Example of TableRAG in ArcadeQA.
13

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B TableRAG Pseudo Code
See Alg. 1.
Algorithm TableRAG Algorithm
Data: T: table, Q: questions, L: LM, K: number of retrieval results, B: token encoding budget,
fene: encoder
Result: Å predicted answer to the question
Function TableRAG (T, Q, L, K, B, fene:
schema_db - BuildSchemaD@((I fene);
cell db + Bui Bu
foreach q E Q do
Quchemar Quell TabullarrQuerryxyyynnnnon....,
Rachema <- Nul Quhemus K, schema_dbp
Reel t MultiiQuryyRetrieeeeiiee Quell, K, cell db);
Á + Proorgramiiddddollvrr//rr q, Rychema, Reel):
end
return A
10
Function Bui (duihdSSceea (T, fenc):
12
corpus {- 6; Dj,
13
foreach Dj Ej) do
14
corpus + corpus U (nnn(CC)) (C,) Dj. E,));
end 15
return corpus
17
Function Bui (TT
corpus - 6;
V getDisttincttouuuumcellaattrrrrr
19
20 V <
Vl: B);
21 foreach (Cj, Mby) E V do
22
corpus + corpus (SEmo((C) Vig)), (C,s mn))
end
24
return corpus
Function Mu Qtt.:/uuuureeeeerrree....CC.... corpus):
26
res Φ;
27
foreach g E Q do
28
res + res Retiieeeeeeeeeecoorrooooe
29
end
return res
14

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C Dataset
Here we describe the generation of each datasets. The statistics of each dataset are shown in Table 7.
ArcadeQA The ARCADE dataset [26] consists of collection of annotated data science notebooks with natural language to code annotations for every cell. The notebooks executes on large scale Kaggle ML datasets'. We extract the examples where there is executed result, and that result is either a single primitive value or a list of values (single column table). In contrast, examples that focus data wrangling without returned value, where the result is table skipped. In total we obtain 130 examples with questions such as "How many countries are south of the equator?" and "What the city that had cash delivery as the most common payment method?".
BirdQA We leverage a subset BIRD dataset [7], a collection for large-scale database grounded text-to-SQL. It also contains annotations for columns descriptions, and external knowledge evidence in some examples. To convert the task to table- QA, we extract 308 examples where a single table was used. We only the table and question without additional helper annotations such descriptions and external knowledge evidence. The tables have 40 thousand rows average and it comprises on single multiple value answers.
Synthetic TabFact To complement existing datasets and better evaluate LMS on larger table struc- tures, have synthesized extension of the TabFact dataset to include tables of significantly greater sizes. This synthesis involves a series of steps designed to expand original small tables into larger ones without losing the integrity of the data or the logical consistency required for correct query answering. We utilize LM-generated column names, sampling functions, and solution programs to create tables and validate them through rigorous process where solution programs verify the correctness of answers, ensuring the synthetic tables maintain the challenge and integrity of the original dataset.
Table Dataset statistics. Values are presented in averages, except for the total counts of tables and questions.
# of tables
of questions
# of rows
of cols (M)
# of cells (NM)
# of distinct values (D)
# of categorical columns
Data
(N)
ArcadeQA
48
130
79,376.2
20.5
1,247,946.6
50,609.4 .
9.4
BirQQA
53
308
62,813.1
11.1
721,077.6
39,649.5
4.9
Synth-TabFact--n orrgial
288
288
14.1
6.3
88.8
41.1
4.4
Symth-TabFFact-50
288
288
50.0
50.0
2,500.0
149.1
27.0
Synth-TabFact-100
288
288
100.0
100.0
10,000.0
268.1
54.1
Syuth-TabFace TabFact-500
288
288
500.0
500.0
250,000.0
1.140.5
267.2
Synth-TabFact- TabFact-IK
288
288
1,000.0
1,000.0
1.000.000.0
2.196.8
539.9
"https://wwwkagglee com htt.:/tao///ttttt/eeeeee
15

---

D Prompt of Query Expansion for Schema Retrieval
Prompt
Given Large table regarding "amazon seller order status prediction orders data", want to answer question: "What is average price for leather wallets?" Since cannot view the table directly, please suggest some column names that might contain the necessary data to answer this question. Please answer with list column
in JSON format wi without any additional explanation
Example:
["columni", "column2", "colunn3"]
aeneoenneanannnn seg.aaaaeeeeteeeeeeaenae.......e Completion
("product_name", "category", "price"]
Figure 9: Prompt of Query Expansion for Schema Retrieval
E Prompt of Query Expansion for Cell Retrieval
Prompt
Given large table regarding "amazon seller order status prediction orders data", want answer question: "What is the average price for leather wallets?" Please extract some keywords which might appear in the table cells and help answer the question.
The keywords should be categorical values rather than numerical values. The keywords should be contained in the question.
Please answer with list of keywords JSON format without any additional explanation. Example:
("keyword1", "keyword2", "keyword3")
Completion
["leather wallets", "average price", "amazon seller", "order status prediction", "orders data"]
Figure 10: Prompt of Query Expansion for Cell Retrieval
16

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Prompt of TableRAG Solver
Prompt
You are working with pandas dataframe regarding "amazon seller order status prediction orders data" Python. The name dataframe 'df'. Your task use repl_as" to answer the question: "What is the average price for leather wallets?"
Tool description:
pyython_repl_astt A Python interactive shell. Use this to execute pythen commands. Input should be valid single line python
Since you cannot view table directly, here are some schemas cell values retrieved from the table.
Schema Retrieval Results:
("colurn_name": "item_ "item_total", "dtype": "object", $549.00']}
['$449.00', '$399.00',
("column_nanee "quantity", "dtype": "int64", "min": 1, "max"; 4} ""column_name"; "order_no", "dtype"; "object", "cell_examples" ['402-884560--9411211 1405- 9763961-5221533 1D903396498--785722-11
Cell Retrieval Quertes: leather wallets, average price, order status, prediction, amazon seller
Cell Retrieval Results:
("column_name": porder_status" "cell_value"; "Delivered to buyer")
("column_name"" : "description", =eell_aale"; "Pure Leather Camel Color Gent's Wallet with Coin Compartment and Card Holders Nen's 's Ultra Slim Noney Organiser (1 pc)")
Strictly follow given format respand: Thought: you should always think about what to do Action: the single Line Python command to execute Observation: the result of the action (this mbought/AAttoon/bberratttt
can repeat N times).
Thought: before giving the final answer, you should think about observations Final Answer: the final answer to the original input question (Answer1, Answer2,
Notes:
Do not use markdown or any other formatting in your responses.
Ensure the last line is only "Final Answer: Answer1, Answer2, form, other form.
Directly output the Final Answer rather than outputting by Python. concluding thought question before giving the final answer
Ensure
fies the table, observations the
Now, given table regarding "amazon seller order status prediction orders data", please use "python_repl__st" with the column names and cell values above to answer the question: What is the average price for leather wallets?"
Begin!
Completion
Thought: need to remove the comma in the item_total values before converting them float.
Action: ofl'iten_total") float)
st).ssrreellacceee astyyyp
Observation: success!
Thought: Now, can filter out the rows with leather wallets and calculate the average price.
wstrr..ontaass"waallee.. Fasee (" mean(() Observation: 442, 7916666666667
Action: df[df[' descrti'desriittiionn
Thought: before giving the final answer, should consider the observations that the calculations are accurate.
Final Answer: average price for leather wallets $4.2.79.
Figure 11: Prompt of TableRAG solver
17

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Reviews:
[{"review_id": "Ynu9glVnZN", "reviewer": "Reviewer_YK6m", "summary": "The paper introduces TableRAG, a novel framework that improves LM-based table understanding by incorporating advanced query expansion and retrieval mechanisms. TableRAG addresses the critical scalability challenges associated with large-scale tables by efficiently encoding data and employing precise retrieval techniques. Through a comprehensive evaluation on self-constructed benchmarks sourced from real-world datasets and synthetic data from TabFact, TableRAG demonstrates superior performance and reduced token consumption across various table sizes.", "questions": "1. Why not evaluate on more models?\n2. Why not evaluation on commonly used TableQA benchmarks like WikiTableQA?", "limitations": "Yes, the paper discusses the limitations.", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. **Scalability**: The framework is scalable for larger tables, as demonstrated by new benchmarks sourced from real-world datasets and synthetic data from TabFact.\n2. **State-of-the-Art Performance**: TableRAG achieves the highest retrieval quality and new state-of-the-art performance on large-scale table understanding tasks.", "weaknesses": "1. **Lack of Novelty** Many papers about RAG reveals that the accuracy of the evidence is crucially important for the performance, and the idea of this paper is to is another practice in the TableQA domain.\n2. **Lack of evaluation robustness** The paper only evaluates the model on closed models gpt-3.5 and gemini, but fails to evaluate on open models like Mistral, Llama etc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Novelty\n>Many papers about RAG reveals that the accuracy of the evidence is crucially important for the performance, and the idea of this paper is to is another practice in the TableQA domain.\n\nThank you for highlighting the importance of evidence accuracy in RAG systems. While this principle is well-established for unstructured data, its application to structured data like tables remains an open question. Prior retrieval-based methods for table understanding haven't consistently outperformed non-retrieval methods, primarily due to the challenges of efficient retrieval from tabular data. Our work addresses this gap by introducing a novel cell-schema retrieval method. Additionally, since existing benchmarks only focus on small tables, we contribute two large-scale TableQA datasets to facilitate further research in this area.\n\n\n\n## Evaluation on open models\n>The paper only evaluates the model on closed models gpt-3.5 and gemini, but fails to evaluate on open models like Mistral, Llama etc.\n>Why not evaluate on more models?\n>Why not evaluation on commonly used TableQA benchmarks like WikiTableQA?\n\nWe follow reviewer’s suggestion and include results obtained using Mistral Nemo, the latest model with a 128K context length from Mistral, as shown below.\n\n| Method          |          | ArcadeQA |          |          |  BirdQA  |          |\n|-----------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n|                 |   small  |   large  |   full   |   small  |   large  |   full   |\n| ReadTable       |   21.2   |    0.0   |    5.4   |   33.8   |    0.0   |    8.4   |\n| ReadSchema      |   36.4   |   30.9   |   32.3   |   41.6   |   33.8   |   35.7   |\n| RandRowSampling |   45.5   |   22.7   |   28.5   |   49.4   |   28.1   |   33.4   |\n| RowColRetrieval |   39.4   |   26.8   |   30.0   |   48.1   |   32.5   |   36.4   |\n| **TableRAG**    | **51.5** | **44.3** | **46.2** | **53.2** | **42.4** | **45.1** |\n\nThe results demonstrate that TableRAG remains the most effective table prompting technique when working with Mistral Nemo, consistent with our previous findings using GPT 3.5 and Gemini 1.0 Pro. Furthermore, even with its 128K context length, Mistral Nemo struggles to handle large tables in ArcadeQA and BirdQA.\n\n\n\n## Evaluation on WikiTableQA\n> Why not evaluation on commonly used TableQA benchmarks like WikiTableQA?\n\nIn our paper, we have evaluated TabFact, a commonly used TableQA benchmark, and extended it to studying the behavior of TableRAG at different scales. In addition, we follow the reviewer's suggestion and perform experiments on WikiTableQA with GPT 3.5 as shown below:\n\n| Method                              |  Accuracy |\n|-------------------------------------|:---------:|\n| TaBERT (Yin et al., 2020)           |    52.3   |\n| Text-to-SQL (Rajkumar et al., 2022) |    52.9   |\n| Binder (Cheng et al., 2022)         |   56.74   |\n| Dater (Ye et al., 2023)             |   52.81   |\n| **TableRAG (Ours)**                 | **57.03** |\n\nIn this benchmark, the entire table content often fits within the context window of the language models, which is not representative of real-world large-scale industrial scenarios.\n\nNevertheless, the results clearly indicate that TableRAG maintains its superior performance even on smaller tables. It's important to highlight that the baseline methods require that LLMs process the entire table, thus limiting their scalability for large tables.", "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": "OcqloO7fsf", "reviewer": "Reviewer_yX7T", "summary": "This paper introduces a novel approach to retrieving cell values and database schema within the table question answering domain. The method operates as follows:\n1) It expands the initial question by identifying potential queries for column retrieval or cell value retrieval.\n2) For each query generated in the first step, the method identifies the top-K columns using their embeddings.\n3) Within a given encoding budget for cell retrieval, it proposes embedding the most frequent B distinct values for each column and retrieves them based on their similarity to each query.\nAdditionally, the authors have developed two new datasets for large-scale table question answering. Their approach demonstrates improved cost efficiency and effectiveness on these large-scale datasets compared to previous methods.", "questions": "Not applicable", "limitations": "Not applicable", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) The proposed approach significantly outperforms the baseline models, particularly in retrieving cell values, showing a substantial improvement in recall metric.\n\n2) Unlike previous works in the table question answering domain, which focused on small-scale tables that do not accurately represent real-world databases, this approach tackles a more challenging setting. It addresses scenarios where tables can contain millions of rows and numerous columns. The authors also developed two new datasets specifically designed for large-scale table question answering.\n\n3) The paper includes well-conducted ablation studies and experiments that compare their method against their baseline methods. These studies demonstrate the effectiveness of each component of their approach.", "weaknesses": "1) My primary concern pertains to the comprehensiveness of the baselines used. While the paper correctly mentions that the table schema is predominantly considered in the text-to-SQL domain, cell values also play a crucial role in SQL generation. Notably, papers like CodeS [1] have proposed using BM25 retrieval to find cell values, which would serve as an excellent baseline for comparison. Additionally, schema linking—the process of identifying the correct rows and columns—is well-explored. Approaches proposed in studies like TaBERT [2] could also be utilized as baselines.\n\n2) Given an encoding budget, the strategy of retaining only the most frequent distinct cell values seems problematic, especially for queries that require data from columns containing names, addresses, etc. Heuristic-based methods, such as those using syntactic similarity with edit distance, can effectively filter most of the cell values and might be more appropriate.\n\n[1]: CodeS: Towards Building Open-source Language Models for Text-to-SQL\n[2]: TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Baselines\n> Papers like CodeS [1] have proposed using BM25 retrieval to find cell values, which would serve as an excellent baseline for comparison. Additionally, schema linking—the process of identifying the correct rows and columns—is well-explored. Approaches proposed in studies like TaBERT [2] could also be utilized as baselines.\n\nWe thank the reviewer for the valuable references. It's important to note that our work focuses on general TableQA, which often involves complex understanding and reasoning beyond simple table manipulation. This distinguishes our approach from Text2SQL methods, which have a narrower scope. While CodeS primarily focuses on Text2SQL, its BM25 retrieval approach offers a potential enhancement to our TableRAG framework. Thus, we implement BM25 and [hybrid retrieval](https://python.langchain.com/v0.1/docs/modules/data_connection/retrievers/ensemble/) as a replacement for embedding-based retrieval to better understand the impact of different retrieval methods.\n\n| Method               |          | ArcadeQA |          |          |  BirdQA  |          |\n|----------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n|                      |   small  |   large  |   full   |   small  |   large  |   full   |\n| ReadTable            |   18.2   |    0.0   |    4.6   |   36.4   |    0.0   |    9.1   |\n| ReadSchema           |   48.5   |   41.2   |   43.1   |   49.4   |   37.2   |   40.3   |\n| RandRowSampling      |   42.4   |   40.2   |   42.3   |   49.4   |   29.9   |   34.7   |\n| RowColRetrieval      |   39.4   |   37.1   |   37.7   |   49.4   |   36.4   |   39.6   |\n| TableRAG (BM25)      |   45.5   |   35.1   |   37.7   |   46.8   |   32.0   |   35.7   |\n| TableRAG (Hybrid)    |   51.5   |   46.4   |   46.2   |   54.5   |   41.1   |   44.5   |\n| **TableRAG (Embed)** | **54.5** | **47.4** | **49.2** | **55.8** | **42.0** | **45.5** |\n\nThe results demonstrate that TableRAG with embedding-based retrieval still outperforms BM25 and hybrid retrieval consistently.\n\nRegarding TaBERT and other LM-based methods, their effectiveness is indeed constrained by the context window of language models, making them less suitable for large-scale tables. Nevertheless, we recognize the benefit of comparing TableRAG against representative baselines, and we evaluate TableRAG on additional WikiTableQA benchmark  with GPT 3.5 as shown below:\n\n| Method                              |  Accuracy |\n|-------------------------------------|:---------:|\n| TaBERT (Yin et al., 2020)           |    52.3   |\n| Text-to-SQL (Rajkumar et al., 2022) |    52.9   |\n| Binder (Cheng et al., 2022)         |   56.74   |\n| Dater (Ye et al., 2023)             |   52.81   |\n| **TableRAG (Ours)**                 | **57.03** |\n\nIn this benchmark, the entire table content often fits within the context window of the language models, which is not representative of real-world large-scale industrial scenarios.\n\nNevertheless, the results clearly indicate that TableRAG maintains its superior performance even on smaller tables. It's important to highlight that the baseline methods require that LLMs process the entire table, thus limiting their scalability for large tables.\n\n\n## Limitation of retaining most frequent distinct cell values\n> Given an encoding budget, the strategy of retaining only the most frequent distinct cell values seems problematic, especially for queries that require data from columns containing names, addresses, etc. Heuristic-based methods, such as those using syntactic similarity with edit distance, can effectively filter most of the cell values and might be more appropriate.\n\nWe appreciate the reviewer highlighting this potential limitation of the cell value filtering strategy. We were indeed aware of this trade-off during the design process and opted for LM-based retrieval with an encoding budget for the following reasons:\n1. **Semantic Understanding vs. Speed**: While heuristic-based methods offer computational efficiency, they often fail to capture subtle semantic relationships that are crucial for accurate retrieval in complex TableQA tasks. We have presented results from replacing embedding-based retrieval with BM25 and a hybrid approach above. While BM25 and the hybrid approach excel at retrieving all cells from the table, their inferior semantic understanding capabilities result in poorer overall performance.\n2. **Retrieval as Guidance, not the Final Answer**: Even with a limited encoding budget, our retrieval mechanism effectively guides the solver agent (PyReAct in our case) to the relevant columns. As demonstrated in Figure 8, the solver agent can detect the existence of the “description” column through retrieval and then use basic string matching techniques (e.g., `df['description'] .str.contains('Wallet')`) to pinpoint the precise cell within those columns. Thus, while retrieval may not always return all relevant cells, it remains crucial for directing the solver's attention to the right areas of the table.\n3. **Empirical Evidence from the Study of Encoding Budget**: The ablation study in Figure 6 shows that TableRAG is robust to changes in the encoding budget. We varied the encoding budget from 100 to 10,000 and found that TableRAG's performance remained near optimal on both ArcadeQA and BirdQA. In contrast, Row-Column Retrieval is sensitive to the encoding budget, with performance significantly dropping when the encoding budget is either increased or decreased. This study validates the earlier statement that even if not all relevant cells are retrieved, the solver agent can still obtain the necessary information through programs using the retrieved information in most cases.\n\nWe will make these clearer in the final version.", "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": "b5iuzDCvvk", "reviewer": "Reviewer_6sEk", "summary": "The paper introduces TableRAG, a framework that enhances LM-based table understanding by incorporating query expansion and retrieval mechanisms. TableRAG aims to improve the performance of large-scale table understanding tasks by efficiently encoding data and utilizing precise retrieval techniques. The framework achieves state-of-the-art results on various benchmarks and datasets.", "questions": "N/A", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The overall method is relatively simple and intuitive.\n2. Experimental results on three datasets show the effectiveness of the proposed method.", "weaknesses": "1. The contribution of the paper is relatively small. The author mainly builds upon the original Row Column Retrieval and further reduces the retrieval cost by Scheme Cell Retrieval. The original work has included column selection, but this paper only further reduces the returned row to only return specific cells.\n2. The baseline of the experimental comparison is relatively weak, and the advantage of finer grained retrieval is obvious.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Contribution\n> The contribution of the paper is relatively small. The author mainly builds upon the original Row Column Retrieval and further reduces the retrieval cost by Scheme Cell Retrieval. The original work has included column selection, but this paper only further reduces the returned row to only return specific cells.\n\nWe thank the reviewer for the constructive feedback. We would like to emphasize that the existing Row-Column Retrieval requires encoding all rows and columns, which is infeasible for large-scale tables without significant truncation. Our work directly addresses this critical limitation and is significantly differentiated in 3 aspects:\n1. **New Benchmarks**: We introduce two real-world large-scale TableQA benchmarks, ArcadeQA and BirdQA, filling a critical gap in the field. These benchmarks enable rigorous evaluation of TableQA systems on tables with millions of tokens, pushing the boundaries of research in this domain.\n2. **Scalable Framework**: We propose TableRAG, a comprehensive framework that combines novel techniques such as query expansion, frequency-based filtering, and schema-cell retrieval. This framework enables LM-based TableQA to scale to tables with millions of tokens, a significant advancement over existing methods.\n3. **Rigorous Evaluation**: We provide thorough complexity analysis and extensive empirical studies, demonstrating TableRAG's effectiveness and efficiency. Our work paves the way for broader applications of TableQA in real-world scenarios involving large tables.\n\nWe will make these clearer in the final version.\n\n\n## Baselines\n> The baseline of the experimental comparison is relatively weak, and the advantage of finer grained retrieval is obvious.\n\nTo the best of our knowledge, TableRAG represents the first dedicated effort to address LM-based large-scale table understanding. We are eager to include any specific baselines the reviewer may recommend that we might have overlooked. While we acknowledge the effectiveness of existing methods on smaller tables, our analysis and studies clearly demonstrate their limitations in scaling due to context length constraints. Furthermore, we have shown that previous attempts at scalable retrieval methods for tabular data encountered inherent limitations. Our carefully designed TableRAG framework overcomes these challenges, achieving superior performance and scalability, as validated through comprehensive complexity analysis and extensive ablation studies.\n\nTo better understand TableRAG’s performance compared against state-of-the-art baselines, perform experiments on WikiTableQA with GPT 3.5 as shown below:\n\n| Method                              |  Accuracy |\n|-------------------------------------|:---------:|\n| TaBERT (Yin et al., 2020)           |    52.3   |\n| Text-to-SQL (Rajkumar et al., 2022) |    52.9   |\n| Binder (Cheng et al., 2022)         |   56.74   |\n| Dater (Ye et al., 2023)             |   52.81   |\n| **TableRAG (Ours)**                 | **57.03** |\n\nIn this benchmark, the entire table content often fits within the context window of the language models, which is not representative of real-world large-scale industrial scenarios.\n\nNevertheless, the results clearly indicate that TableRAG maintains its superior performance even on smaller tables. It's important to highlight that the baseline methods require that LLMs process the entire table, thus limiting their scalability for large tables.", "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": "9i0JIc057W", "reviewer": "Reviewer_aMG9", "summary": "This paper primarily concentrates on the scalability challenges associated with encoding entire tables as input for LLM reasoning. It introduces a retrieval-augmented generation (RAG) framework, named TableRAG, which utilizes query expansion along with schema and cell retrieval to identify essential information effectively. Furthermore, this study contributes to establishing new benchmarks for million-token-sized tables, significantly enhancing the understanding of LLM performance in large-scale table analysis.", "questions": "- for figure 8, I am slightly confused by the results obtained from the solver. It appears the query intends to request the average price for all types of wallets. Yet, in the provided example, the third action only yields the average price of leather wallets (leather_wallet_prices.means()), rather than the average for all wallets (wallet_prices.means()). I'm uncertain if this discrepancy stems from a misrepresentation or if there's potential confusion in the example.\n- for controlling the budget in cell retrieval, how does the paper manage a column like \"description,\" which consists of long sentences that might exhaust the entire budget, preventing other cells from being processed?\n- for schema/cell retrieval, if the query expansion driven by LLMs fails to generate keywords that align with the schema/cells in the table, what will the outcomes be, or are there any solutions proposed in the paper? Additionally, I am curious about how the paper attempts to align the schema mentioned in the table with the knowledge from LLMs themselves, or does it simply rely on the generalizability of LLMs?", "limitations": "- the proposed method seems to primarily support the questions that can be answered by manipulating the tables, such as \"What is the average price of the wallet?\" Here, queries can be transformed into specific table operations (e.g., df['item_total'] = df['item_total'].str.replace('$','').str.replace(',','').astype(float)). However, it may not perform as well with more complex reasoning tasks. For instance, in the Tabfact dataset, where one must determine from a Wikipedia table and its caption which statements are supported or contradicted, the method might not show promising results for such reasoning tasks.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- the idea of schema-cell retrieval seems reasonable and shows better performance compared to baselines on some types of questions.\n- this paper seems contribute to new benchmarks concerning million-token sized tables and enhances the understanding of LLM performance in large-scale table comprehension.", "weaknesses": "- while the idea of schema-cell retrieval seems reasonable, the generalizability of the method is not very clear, especially for some questions that cannot be directly addressed by manipulating the tables (also noted in the limitation section)\n- the experiments only consider GPT-3.5-turbo and Gemini-Pro as LLMs which are limited, further testing on more open-sourced and close-sourced models is necessary to prove the consistency of the proposed method.\n- the performance in Table 4 appears effective solely for the ArcadeQA dataset, with the retrieval of schema and cells proving useful. However, for BirdQA, the enhancements seem limited. I'm little concerned about the method consistency when adding more datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Generalizability\n\nWe appreciate the reviewer's question regarding the generalizability of our schema-cell retrieval method. It's important to note that our work focuses on general TableQA, which often involves complex understanding and reasoning beyond simple table manipulation, as shown in the TabFact example in the general response. This distinguishes our approach from Text2SQL methods, which have a narrower scope. \n\nThe generalizability of TableRAG is inherently tied to the capabilities of the underlying solver agent. TableRAG can be easily integrated with any downstream LLM-based solver agents. In our implementation, we utilize ReAct, a powerful LLM-based solver with demonstrated effectiveness and generalizability in TableQA tasks [1, 2]. TableRAG's primary role is to efficiently and accurately provide the necessary information to the solver, enabling it to perform the required reasoning and extract relevant details.\n\n1. [Rethinking Tabular Data Understanding with Large Language Models](https://aclanthology.org/2024.naacl-long.26) (Liu et al., NAACL 2024)\n2. [ReAcTable: Enhancing ReAct for Table Question Answering](https://dl.acm.org/doi/10.14778/3659437.3659452)  (Zhang et al., VLDB 2024) \n\n## Evaluation on more models\n\nWe follow reviewer’s suggestion and include results obtained using Mistral Nemo, as shown in the general response.\nThe results demonstrate that TableRAG remains the most effective table prompting technique when working with Mistral Nemo, consistent with our previous findings using GPT 3.5 and Gemini 1.0 Pro.\n\n## Ablation study\n\nThanks for the insightful feedback. The accuracy improvement on BirdQA ranges from 0.7% to 2%, which is in line with the improvements observed in prior work such as Binder[3] and Dater[4]. \n\nWe observe an even more substantial accuracy boost on ArcadeQA, ranging from 2.3% to 6.6%. This is likely attributable to the larger scale of the input in this dataset. As detailed in Table 5, ArcadeQA has approximately twice the average number of columns and cells compared to BirdQA, suggesting that cell retrieval is particularly beneficial when handling larger tables. Nonetheless, both schema and cell retrieval components enhance performance across all tested datasets, and they contribute orthogonal advantages to the overall improvement.\n\n3. [Binding Language Models in Symbolic Languages](https://openreview.net/forum?id=lH1PV42cbF) (Cheng et al., ICLR 2023)\n4. [Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning](https://dl.acm.org/doi/10.1145/3539618.3591708) (Ye et al., SIGIR 2024)\n\n## Figure 8\n\nThanks for pointing out the typo. The variable `leather_wallet_prices` should indeed be `wallet_prices` as obtained in the previous step. We will correct this in the final version.\n\n## Description column\n\nThank you for raising this important point about handling long text columns within our encoding budget. We acknowledge this limitation and designed TableRAG to mitigate its impact in the following ways:\n1. **Prioritization of Categorical Columns**: Our frequency-based encoding prioritizes columns with more distinct categorical values, ensuring that critical information for indexing is processed first. This helps reduce the likelihood of long text columns exhausting the entire budget.\n2. **Solver Agent's String Matching Capability**: Even if the retrieval doesn't cover all cells in long text columns like \"description\" or \"address,\" our solver agent (ReAct in our case) can still perform basic string matching within those columns to identify relevant information. As demonstrated in Figure 8, the solver agent can detect the existence of the “description” column through retrieval and then use basic string matching techniques (e.g., `df['description'] .str.contains('Wallet')`) to pinpoint the precise cell within those columns.\n3. **Empirical Evidence from the Study of Encoding Budget**: The ablation study in Figure 6 shows that TableRAG is robust to changes in the encoding budget. We varied the encoding budget from 100 to 10,000 and found that TableRAG's performance remained near optimal on both ArcadeQA and BirdQA. In contrast, Row-Column Retrieval is sensitive to the encoding budget, with performance significantly dropping when the encoding budget is either increased or decreased. This study validates the earlier statement that even if not all relevant cells are retrieved, the solver agent can still obtain the necessary information through programs using the retrieved information in most cases.\n\nWhile we recognize that there might be more optimized ways to handle long text columns, our current work prioritizes the implementation and evaluation of query expansion and schema-cell retrieval, which we believe are foundational for scaling LM-based TableQA. Addressing the specific challenge of long text columns will be a focus of our future research.\n\n## False alignment\nThank you for raising these important questions about the robustness of our retrieval and the alignment between schema and LLM knowledge.\n\n**Handling Query Expansion Mismatches**: In TableRAG, we employ embedding-based retrieval, which inherently captures semantic similarities. Even if the LLM-generated keywords don't perfectly match the schema or cell values, the retrieval process will still return the most semantically relevant ones. This provides a degree of robustness against potential mismatches in query expansion.\n\n**Schema Alignment and LLM Generalizability**: TableRAG, having access to both the query and the retrieved context, is expected to leverage its understanding of language and world knowledge to determine which information is pertinent and how to combine it to solve the problem at hand. While we don't explicitly enforce schema alignment within the LLM, we rely on its inherent ability to reason and connect information, even when the schema terms might not have been directly encountered during pre-training.", "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:
## General Response

We thank the reviewers for their constructive feedback. They found our solution addresses the critical scalability challenge with superior performance in comprehensive evaluations. The work contributes to a better understanding of LLM capabilities in large-scale table analysis.

Our primary focus is to improve the scalability of LM-based table understanding. We achieve this through TableRAG, a novel table-specific retrieval technique incorporating query expansion, schema-cell retrieval, and frequency-based filtering. Additionally, we introduce ArcadeQA and BirdQA, two large-scale TableQA benchmarks specifically designed to evaluate and advance our understanding of LLM performance on large-scale tables.

We carefully address individual questions below and welcome further discussions of this work.

## Additional LLM evaluation

In addition to GPT 3.5 and Gemini 1.0 Pro, we have extended our evaluations to include Mistral Nemo, the latest model from Mistral with a 128K context window.

| Method          |          | ArcadeQA |          |          |  BirdQA  |          |
|-----------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
|                 |   small  |   large  |   full   |   small  |   large  |   full   |
| ReadTable       |   21.2   |    0.0   |    5.4   |   33.8   |    0.0   |    8.4   |
| ReadSchema      |   36.4   |   30.9   |   32.3   |   41.6   |   33.8   |   35.7   |
| RandRowSampling |   45.5   |   22.7   |   28.5   |   49.4   |   28.1   |   33.4   |
| RowColRetrieval |   39.4   |   26.8   |   30.0   |   48.1   |   32.5   |   36.4   |
| **TableRAG**    | **51.5** | **44.3** | **46.2** | **53.2** | **42.4** | **45.1** |

- TableRAG outperforms all baselines, consistent with previous results observed in GPT 3.5 and Gemini 1.0 Pro.
- Despite their large 128K context window, LLMs continue to encounter challenges when dealing with large-scale tables. These challenges are largely mitigated by the proposed TableRAG


## Evaluation on common TableQA benchmark

We follow reviewer’s suggestion and evaluate TableRAG with GPT 3.5 on the additional WikiTableQA benchmark, comparing its performance against representative baselines:

| Method                              |  Accuracy |
|-------------------------------------|:---------:|
| TaBERT (Yin et al., 2020)           |    52.3   |
| Text-to-SQL (Rajkumar et al., 2022) |    52.9   |
| Binder (Cheng et al., 2022)         |   56.74   |
| Dater (Ye et al., 2023)             |   52.81   |
| **TableRAG (Ours)**                 | **57.03** |

- While TableRAG is primarily designed to address the challenges of large-scale tables, it also exhibits superior performance on smaller tables.


## Evaluation of Different Retrieval Methods

In response to the questions about the limited encoding budget of embedding-based retrieval methods, we follow the reviewer’s suggestion and implement BM25 and hybrid retrieval. 
| Method               |          | ArcadeQA |          |          |  BirdQA  |          |
|----------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
|                      |   small  |   large  |   full   |   small  |   large  |   full   |
| ReadTable            |   18.2   |    0.0   |    4.6   |   36.4   |    0.0   |    9.1   |
| ReadSchema           |   48.5   |   41.2   |   43.1   |   49.4   |   37.2   |   40.3   |
| RandRowSampling      |   42.4   |   40.2   |   42.3   |   49.4   |   29.9   |   34.7   |
| RowColRetrieval      |   39.4   |   37.1   |   37.7   |   49.4   |   36.4   |   39.6   |
| TableRAG (BM25)      |   45.5   |   35.1   |   37.7   |   46.8   |   32.0   |   35.7   |
| TableRAG (Hybrid)    |   51.5   |   46.4   |   46.2   |   54.5   |   41.1   |   44.5   |
| **TableRAG (Embed)** | **54.5** | **47.4** | **49.2** | **55.8** | **42.0** | **45.5** |

- Although BM25 is faster and capable of retrieving all cells, its lack of semantic understanding makes embedding-based retrieval a superior choice for TableRAG. For a more detailed discussion, please refer to our response to Reviewer yX7T.

Meta-review: LM-based table retrieval suffers from scalability challenges in case of large schema and tables.  This submission introduces TableRAG reduces these scalability challenges by query expansion and retrieval mechanisms.  The submission and rebuttal include a thorough  evaluation on benchmarks sourced from real-world datasets and synthetic data from TabFact.  TableRAG shows better performance and lower LM token costs.

This submission represents a nice systems-level work that seems effective and intuitive. Because of the nature of systems built around LMs, the paper does not fit the template of algorithmic ML papers with high creativity or intellectual depth.  In aggregate, reviewers found the paper borderline.  The authors reported on substantial additional experiments during rebuttal, and resolved several issues reported by reviewers.

The authors must publish code and data if the paper is accepted.
Decision: Accept (poster)