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arxiv: 2403.20208 · v8 · submitted 2024-03-29 · 💻 cs.LG · cs.AI

Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining

Yazheng Yang , Yuqi Wang , Yaxuan Li , Sankalok Sen , Lei Li , Lin Qiu , Qi Liu This is my paper

Pith reviewed 2026-05-24 02:21 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords large language modelstabular datapretrainingclassificationregressionimputationzero-shot learningfew-shot learning
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The pith

Training LLMs on annotated tables improves their results on classification, regression, and imputation tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models excel at natural language but lag on structured tabular data because their initial training rarely includes tables. The paper tests whether this gap can be closed by assembling a large corpus of tables paired with task instructions and using it to continue training Llama-2. Experiments then evaluate the resulting model on zero-shot prediction, few-shot prediction, and in-context learning for the standard tabular problems of classification, regression, and missing-value imputation. The reported gains over prior benchmarks indicate that targeted exposure to tables during pretraining can adapt LLMs to routine data-science workloads.

Core claim

Compiling a corpus of tables annotated with instructions and performing large-scale continued training of Llama-2 on this corpus produces significant improvements on predictive tabular tasks, allowing the model to handle zero-shot, few-shot, and in-context learning scenarios for classification, regression, and imputation more effectively than existing approaches.

What carries the argument

Table-specific pretraining on an instruction-annotated corpus of tables, which supplies the missing exposure to tabular structures during model training.

If this is right

  • The trained model supports zero-shot prediction on new tabular datasets without task-specific fine-tuning.
  • The same model also improves few-shot and in-context learning performance on the same tasks.
  • The approach creates a new performance reference point for applying LLMs to table-based data science problems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the pretraining effect generalizes, similar corpora could be built for other structured formats such as time series or graphs.
  • The method could be combined with existing tabular-specific architectures to test whether gains compound.

Load-bearing premise

The main reason LLMs underperform on tabular data is that they simply did not see enough tables during their original pretraining.

What would settle it

Running the same downstream evaluation suite on a Llama-2 model that received only generic continued training, with no table corpus, and obtaining equivalent gains would show that the table-specific data is not the cause of the reported improvements.

Figures

Figures reproduced from arXiv: 2403.20208 by Lei Li, Lin Qiu, Qi Liu, Sankalok Sen, Yaxuan Li, Yazheng Yang, Yuqi Wang.

Figure 1
Figure 1. Figure 1: Illustration of our methodology for the training of Large Language Models (LLMs) with tables and the subsequent applica￾tion of our model to downstream tasks. hend tabular data for improving the efficiency of processing tasks related to tables. The essence of tabular data resides in its complex, multi￾dimensional interactions and structural intricacies, which present formidable challenges in capturing the … view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the initial pretraining phase of a LLM applying the Mask-Then-Predict strategy (on the left), followed by the multi-task training phase customized for downstream tasks such as classification and regression (on the right). Through the former phase, the LLM acquires unstructured knowledge embedded within tables. Subsequently, during the latter phase, it enhances its capability for reasoning b… view at source ↗
Figure 3
Figure 3. Figure 3: The unified prompt template used for combining the instruction with tables to form the model input in both pretraining and finetuning in downstream tasks. 3.1. Unified Serialization Motivated by the findings of recent research (Shin et al., 2023), which demonstrates the superior efficacy of the Mark￾down format over conventional tabular formats including CSV and HTML, we choose to serialize tables in Mark￾… view at source ↗
Figure 5
Figure 5. Figure 5: The data type distribution: the percentages of numerical columns and textual columns in our collected Kaggle tables [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: An illustration of our approach to learning in contexts of extreme length, with each example being sequentially organized using the uniform prompt template before being concatenated into vided instructions and tables, leading to accurate prediction the sequence of texts for model input. of the desired output. Note that for these tasks, the “Answer” placeholder, as shown in the referenced figure of the unif… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of prediction results for missing values: the number of missing values ranges from 1 to 4. This range reflects the scenarios encountered in real-world applications, where a table may contain multiple missing entries. compared to GPT-4 with an overall improvement of around 27%. This improvement in performance provides additional experimental support for the effectiveness of our pretrained model i… view at source ↗
Figure 8
Figure 8. Figure 8: Radar chart illustrating the performance of few-shot prediction in 4 classification tasks. The evaluation metric is ROC-AUC. Our method demonstrates superior performance, achieving higher scores in most of the directions (number of shots) on the chart, showing its effectiveness and competitiveness. 0 4 8 16 24 32 40 48 Number of Examples in context 0.0 0.1 0.2 0.3 0.4 0.5 0.6 ROC-AUC 0.542 0.560 0.571 0.57… view at source ↗
Figure 9
Figure 9. Figure 9: Analysis of extremely Long context learning. We adopt the Llama-2 7B 80K here as a good comparison that is capable of processing 80K tokens as context. The x-axis represents the number of examples included in the context, ranging from 0 to 48. an average improvement of 18.8%, reveals that our model not only achieves higher scores, but also consistently sur￾passes the Llama-2 80K model as the context size e… view at source ↗
Figure 10
Figure 10. Figure 10: Analysis of predicting target value in the manner of filling in missing value. The CoT (Chain-of-Thought) prompting method is also integrated into models to provide detailed reasoning or explanations for each step. Our model demonstrates the consis￾tent performance improvement with CoT across all tasks. 0 1 2 Gini Index Bucket XGBoost NODE AutoInt Tapas TaBERT TabTransformer FT-Transformer TabNet TUTA Tab… view at source ↗
Figure 11
Figure 11. Figure 11 [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt for the pretraining task of Mask-Then-Predict. The concealed cells are marked with specific sentinel tokens. The model is expected to predict all masked contents with corresponding sentinel words. Yes Input: Output: ### Instruction: Given the table below, predict the mortality from heart failure using the data related to cardiovascular health and lifestyle risk factors. Options: [Yes or No]. ### In… view at source ↗
Figure 13
Figure 13. Figure 13: Prompt for the classification task. The model is asked to predict the target class according to the given instruction and tabular content. In this demonstration case, the model is required to learn to predict the mortality from the give table. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt for the regression task. The model is asked to predict the target value according to the given instruction and tabular content. In this demonstrated case, the model is required to learn to predict the sale price of a house. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
read the original abstract

In the domain of data science, the predictive tasks of classification, regression, and imputation of missing values are commonly encountered challenges associated with tabular data. This research endeavors to apply Large Language Models (LLMs) towards addressing these predictive tasks. Despite their proficiency in comprehending natural language, LLMs fall short in dealing with structured tabular data. This limitation stems from their lacking exposure to the intricacies of tabular data during their foundational training. Our research aims to mitigate this gap by compiling a comprehensive corpus of tables annotated with instructions and executing large-scale training of Llama-2 on this enriched dataset. Furthermore, we investigate the practical application of applying the trained model to zero-shot prediction, few-shot prediction, and in-context learning scenarios. Through extensive experiments, our methodology has shown significant improvements over existing benchmarks. These advancements highlight the efficacy of tailoring LLM training to solve table-related problems in data science, thereby establishing a new benchmark in the utilization of LLMs for enhancing tabular intelligence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript claims that LLMs underperform on tabular predictive tasks (classification, regression, imputation) due to insufficient exposure to structured data during pretraining. It proposes compiling a corpus of annotated tables, performing large-scale pretraining of Llama-2 on this corpus, and applying the resulting model to zero-shot, few-shot, and in-context learning scenarios, with the abstract asserting that extensive experiments demonstrate significant improvements over existing benchmarks.

Significance. If the experimental results were substantiated with proper controls and reporting, the work could be significant for establishing that targeted pretraining on tabular data can adapt LLMs to structured prediction tasks, potentially providing a new paradigm for applying LLMs in data science beyond natural language.

major comments (1)
  1. [Abstract] Abstract: The central claim that 'our methodology has shown significant improvements over existing benchmarks' is unsupported by any evidence. The abstract supplies no quantitative results, no baselines (LLM or tabular), no metrics, no dataset details, no description of the annotated table corpus (size, sources, annotation scheme), no pretraining objective or instruction format, and no evaluation protocol (e.g., train/test splits, statistical testing). This absence renders the key premise—that table-specific pretraining remedies the performance gap—unevaluable.
minor comments (1)
  1. [Abstract] Abstract: The model is referred to only as 'Llama-2' without specifying parameter count (7B/13B/etc.), which affects reproducibility and comparison to other work.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for highlighting the need for greater specificity in the abstract. We address this point directly below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'our methodology has shown significant improvements over existing benchmarks' is unsupported by any evidence. The abstract supplies no quantitative results, no baselines (LLM or tabular), no metrics, no dataset details, no description of the annotated table corpus (size, sources, annotation scheme), no pretraining objective or instruction format, and no evaluation protocol (e.g., train/test splits, statistical testing). This absence renders the key premise—that table-specific pretraining remedies the performance gap—unevaluable.

    Authors: We agree that the current abstract is too high-level and does not provide enough concrete information to substantiate its claims. The full manuscript contains the requested details (corpus construction, pretraining objective, evaluation protocols, baselines, metrics, and statistical results), but these are not summarized in the abstract. We will revise the abstract to include key quantitative highlights (e.g., performance deltas on zero-shot/few-shot tasks), a brief description of the table corpus, and the main evaluation settings. This change will make the central premise directly evaluable from the abstract. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pretraining claim with no derivation chain or self-referential steps

full rationale

The provided abstract (full text unavailable) describes an empirical procedure: compile a table corpus with instructions, train Llama-2 on it, then evaluate zero-shot/few-shot performance. No equations, parameters fitted to subsets and renamed as predictions, self-citations, uniqueness theorems, or ansatzes are present. The central premise (insufficient pretraining exposure as the bottleneck) is stated as motivation rather than derived; improvements are asserted via 'extensive experiments' without any reduction of outputs to inputs by construction. This matches the default case of a non-circular empirical ML paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not specify any free parameters, background axioms, or new invented entities; the approach relies on standard LLM pretraining techniques applied to a new dataset.

pith-pipeline@v0.9.0 · 5690 in / 1176 out tokens · 34034 ms · 2026-05-24T02:21:12.464299+00:00 · methodology

discussion (0)

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Reference graph

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