Griffin: Towards a Graph-Centric Relational Database Foundation Model
Authors: Yanbo Wang
Xiyuan Wang
Quan Gan
Minjie Wang
Qibin Yang
David Wipf
Muhan Zhang
Venue: ICML 2025 poster (2025)
Keywords: Relational Database
Foundation Model
Primary area: deep_learning->foundation_models

Abstract: We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller models focused on single RDB tasks, Griffin unifies the data encoder and task decoder to handle diverse tasks. Additionally, we enhance the architecture by incorporating a cross-attention module and a novel aggregator. Griffin utilizes pretraining on both single-table and RDB datasets, employing advanced encoders for categorical, numerical, and metadata features, along with innovative components such as cross-attention modules and enhanced message-passing neural networks (MPNNs) to capture the complexities of relational data. Evaluated on large-scale, heterogeneous, and temporal graphs extracted from RDBs across various domains (spanning over 150 million nodes), Griffin demonstrates superior or comparable performance to individually trained models, excels in low-data scenarios, and shows strong transferability with similarity and diversity in pretraining across new datasets and tasks, highlighting its potential as a universally applicable foundation model for RDBs. Code available at https://github.com/yanxwb/Griffin.

Griffin: Towards a Graph-Centric Relational Database Foundation Model
Yanbo Wang Xiyuan Wang' Quan Gan
Minjie Wang?
Qibin Yang
i David Wipf? Muhan Zhang'
Abstract
An RDB foundation model is defined as predictive model that works actoss diverse RDBs with varying sizes, schemas, and domains. Integrating RDBs into the foundation model paradigm presents challenges, including structural complex- ity, lack of processing pipelines, and unique computational patterns that differ from single-table data. While traditional machine learning and deep learning methods could han- dle single-table data pretty well (Chen & Guestrin, 2016; Ke et al., 2017; Prokhorenkova al., 2018; Huang et al., 2020; Arik & Pfister, 2021; Gorishniy et al., 2021), they fail to address RDB-specific complexities. On the other hand, flattening relational data into single table often results in significant information loss (Cvitkovic, 2020; Chepurko et al., 2020). Recently, graph-based methods using Graph Neural Networks (GNNs) (Kanter Veeramachaneni, 2015; Cvitkovie, 2020; Bai et al., 2021; Zhang al., 2023a; Wang et al., 2024; Robinson et al., 2024) attempt to capture these relationships but are limited to task-specife models rather than proposing a universal RDB foundation model.
We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller mod- els focused on single RDB tasks, Griffin unifies the data encoder and task decoder to handle di- verse tasks. Additionally, we enhance the archi- tecture by incorporating a cross-attention mod- ule and novel aggregator. Griffin utilizes pre- training on both single-table and RDB datasets, employing advanced encoders for categorical, nu- merical, and metadata features, along with inno- vative components such as cross- cooss-ateention mod- ules and enhanced pessaae-passingg neural net- works (MPNNs) to capture the complexities of relational data. Evaluated lcrge-scale, het- erogeneous, and temporal graphs extracted from RDBs across various domains (spanning over 150 million nodes), Griffin demonstrates superior or comparable performance to individually trained models, excels in low-data scenarios, and shows strong transferability with similarity and diversity in pretraining across new datasets and tasks, high- lighting its potential as a universally applicable foundation model for RDBs. Code available at github. wwt.///wwwwww/............
In this work, we introduce Griffin, a Graph-centric RelatIonal database FoundatIoN model attemptation, which integrates pretraining on both single-table and RDB datasets. Griffin is designed for broad generalization across diverse tasks and demonstrates superior performance over task- specific GNN approaches through several key innovations.
The first challenge is how to handle different task types (classification and regression) and different RDBs with dis- parate input feature spaces (categorical, numerical, textual, etc. of diverse semantic meanings). Griffin unifies input data encoders and task decoders. Unlike earlier methods that apply single embedding layer to all categorical fea- tures and directly input raw numerical values, Griffin uses a pretrained text encoder for categorical inputs and a pre- trained float encoder for numerical ones. also incorporates RDB metadata, including table names, column names, and edge types, to distinguish tasks and capture the structure that connects them. In contrast to prior work that uses sepa- rate prediction heads for each task, Griffin applies shared float decoder (pretrained jointly with the float encoder) for all regression tasks, and unified classification head that integrates the text embeddings of target categories. This allows Griffin to manage classification tasks with varying numbers of categories and regression tasks with different ranges using consistent architecture.
1. Introduction
Foundation models have revolutionized domains such as natural language (Brown, 2020; Touvron et al., 2023), vision (Wang et al., 2023; Yuan et al., 2021), tabular data (Zhang et al., 2023b; Yang et al., 2024), and graphs (Liu et al., 2023; Tang et al., 2024), offering scalable and gener- alized frameworks for diverse tasks. However, foundation models for RDBs-where tables are interconnected through complex relationships remain underexplored, despite the practical importance and widespread use of RDBs.
*Wor done during internship Amazon. 'Institute for Ar- tificial Intelligence, Peking University. 2 AAmazon Web Services, Correspondence to: Muhan Zhang <muhan pku.edu.en>.
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Griffin: Towards Graph-Centric Relational Database Foundation Model
Another key design consideration is selecting an effective GNN architecture for RDBs. Griffin addresses this by in- corporating a cross-attention module that flexibly gathers information from cells within row (treated as node in the graph), mitigating the loss introduced by mean aggrega- tion in standard GNNs. Additionally, Griffin enhances its message passing neural network (MPNN) by performing intra-relation aggregation before merging features across relation types.
merical values, categorical values, text, or hash values used for indexing.
A key characteristic of an RDB is the relationships between tables, which are defined by Primary Keys (PKs) and For- eign Keys (FKs). A PK is a column within a table that uniquely identifies each row, while a FK is a column in an- other table that references the PK, thereby inheriting values from the corresponding PK. Let R denote the total number of PK-FK pairs across all tables.
A further challenge involves leveraging large-scale data for training. To this end, we constructed a diverse and extensive dataset collection for both single-table and RDB tasks and developed a multi-stage pretraining and fine-tuning pipeline. Griffin is initially pretrained on single-table datasets using a random masked cell completion task that does not require labeled data. This is followed by joint supervised fine- tuning (SFT) on realistic tasks from both single-table and RDB datasets, and finally by task-specific fine-tuning and evaluation on each downstream RDB task. In total, the pretraining and SFT phases covered over 150 million nodes (rows), enabling the formation of large, heterogeneous, and temporal graphs across various domains to support the large model development.
The heterogeneous graph derived from an RDB is formally defined as G = (1V* Jk- " {E"),=)) where the node set y of type k is constructed from the rows of table T with each node corresponding to a row in the table. The feature vector of each node is the corresponding row in the table, and the edge set E" of type r is constructed from the r- th PK-FK pair, which connects rows from the table with the FK to the referenced rows in the table with the PK. In this graph-based representation, almost all hash values used for indexing (such as those for PKs and FKs) are already encoded in the edge connection patterns. Therefore, we use only on the numerical, categorical, and textual features for the node attributes.
A common approach in tabular data prediction is missing value prediction, where the goal is to infer a missing cell value using information from the same table or related tables. In many real-world applications, table rows are associated with a timestamp, denoted as t and the number of rows can be very large. To maintain temporal causality and re- duce memory usage, only rows with timestamps earlier than that of the target row are allowed for use in prediction. In the graph-based formulation of this task, the problem is rephrased as sampling a rooted subgraph that includes only nodes (rows) with earlier timestamps than the queried node.
To assess the effectiveness of the model design and the im- pact of pretraining, Griffin was evaluated on two recent graph-centric RDB benchmarks: 4DBInfer (Wang et al., 2024) and RelBench (Robinson et al., 2024). The evalu- ation led to the following key findings: (1) Even without pretraining, the Griffin architecture achieves significant im- provements downstream tasks, demonstrating the advan- tages of its advanced design. (2) Pretraining and SFT solely on single-table datasets enables Griffin to outperforms its non-pretrained counterpart. (3) With SFT on RDBs of sim- ilarity and diversity to downsteam tasks, Griffin achieves even better results, particularly in scenarios with limited downstream task samples, highlighting its potential as a foundation model capable of transferring to downstream tasks with limited downstream supervision.
Let the target column value be represented as T as- sociated with the node V/s This node serves as the root of a temporal, rooted computation subgraph: T(4) ( (DV {=="))/)') where D(0) = {(V \ T is the root node, which does not include the target column's feature. The set y² contains nodes hop / from the root and only includes nodes satisfying that t < th.
In summary, Griffin represents a significant step forward in the development of foundation models for RDBs by com- bining robust generalization capabilities with architectural innovations that address the complexities of relational data.
Each edge set g(I) include edges connecting nodes in ((++)) to parent nodes in VLO, and satisfies: A vvf++) D(+1), ( E v(t) with (2/(+ v(()) E g().
2. Preliminary
A Relational Database (RDB) is formally defined as a col- lection of tables, denoted by R = {T*} Ik=1 where K represents the total number of tables, and each table T is structured as a matrix with N₂ rows (instances) and M₁ columns (features). The individual entry in the i-th row and j-th column of table The is represented by The- In our setting, these entries can take various forms, including nu-
Through this transformation, the RDB task of predicting a column value from multiple related tables converted into a graph-based prediction problem over a temporally constrained rooted subgraph, defined as follows:
Input: A sampled rooted subgraph (L) constructed for the target column T
Output: Predicted value of the target column T.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
Raw RDB
Sample Subgraph
Model Architecture
User Tabl
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Attarnce
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Figure I: Overview of the Griffin Model Framework. The framework first transforms RDBs into graph structure by representing each row as node and using primary key-foreign key relationships edges. Given target column, temporally constrained subgraph is sampled and processed using unified encoder module before being passed to MPNN. Finally, the unified task decoders generate predictions based on whether the task is classification or regression.
3. Model Design
measure the similarity between different texts.
In this section, we present the model design of Griffin, il- lustrated in Figure 1. The framework consists of three main components for processing a sampled subgraph and gener- ating prediction values: a unified data encoder, a MPNN tailored for RDBs, and unified task decoder.
Numerical Feature For numerical data, to avoid issues with extreme values, we first apply quantile normalizer (Bolstad et al., 2003) to standardize the numerical values, transform- ing the distribution normal. We then pre-trained Multi-Layer Perceptron (MLP) to convert the normalized values into the same d-dim embedding vectors. To train the MLP, we sample x from normal distribution:
3.1. Unified Data Encoder
core innovation of Griffin is the unification of different RDB input data. Previous RDB models typically use sep- arate embedding layers for categorical features and direct numerical inputs, which makes them difficult to generalize to new data. In contrast, Griffin handles all categorical and text features using pre-trained text encoder, while numeri- cal features are normalized and processed with a pre-trained float encoder. This approach ensures that input distributions are more consistent across tasks. Additionally, Griffin uses RDB metadata and task-specific information to create task embeddings, allowing the same model to perform different tasks based the task embedding provided at the input.
w = ENC(2) E R
DEC(w) E R.
(I)
where ENC encodes the float into an embedding, and DEC decodes the embedding back to a float value. The model is trained using LI loss (ly - x(), and we apply LayerNorm (without affine weights) to the encoder's output to prevent collapse. After pretraining. the encoder and decoder are fixed and do not participate in the training of the Griffin model. During inference, the numerical input is first nor- malized, then passed through the encoder.
Metadata Information Griffin also incorporates flexible encoding of RDB metadata, such as table names, column names, and edge types. This metadata encoded with the text encoder provide additional node and edge features.
Categorical and Textual Feature For categorical features, we first convert them into text representations using the meta- data from the RDB. Both categorical and text features are then passed through a single, pre-trained text encoder (Nuss- baum al., 2024). Each feature (or "cell") encoded into fixed-length vector, which captures rich semantic informa- tion. Cosine similarity between these vectors allows to
Task Representations These unified input encoders map inputs from different tasks to the same space. However, the model also needs to account for the syntactic differences between tasks. For example, if two missing cells the same row are predicted without additional task-specific embed- dings, the model's 's input for both tasks would be identical,
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Griffin: Towards Graph-Centric Relational Database Foundation Model
leading to the same output representation and poor expres- sivity. To address this, we introduce task embedding. This embedding is generated by the text encoder using the col- umn name of the cell to be predicted as imput, allowing the model to produce distinct embeddings for different tasks.
The keys for cross-attention are the metadata my E R RLxxd (column names) of node and the values are 2; Rlxxd the input node features. The result, v/g is added to u; to update the node representation. This cross-attention module overcomes the information loss typically seen in traditional GNNs, which often aggregate different columns using sim- ple methods like mean aggregation. By focusing on specific cells row and attending to their contextual relationships. the module improves the model's ability extract nuanced information, enhancing task performance.
With all components unified across different types of data, the sampled subgraph is extended as follows:
Input: A sampled rooted subgraph TI)) constructed for the target column T
Output: An enriched rooted subgraph in which each node i is associated with a feature tensor 2; E R4 xd and a metadata tensor m; E R44xd, where L; is the number of cells in the node (which may vary between nodes). Each edge (i of relation type r carries a relation-specific metadata vector e, E IRS, Additionally, task embedding vector E Rd is provided to represent task-specific information.
Hierarchical Aggregation Along with the cross-attention module, Griffin enhances its MPNN to reduce information loss. Instead of aggregating all neighbors uniformly, Grif- fin first aggregates information within each relation type and then combines features across different relations. This hierarchical aggregation helps preserve the structure of rela- tional data by ensuring that information is aggregated within each relation (e.g., a specific table or type of relationship) before being combined across multiple relations. This ap- proach prevents the loss of important relational context and helps the model learn more informative representations.
3.2. MPNN Architecture
The MPNN of Griffin is composed of multiple layers, each containing two key components: cross-altention module that extracts information from node features and a message- passing module that facilitates information exchange be- tween nodes. The intermediate embedding of node across layers is maintained as ui, while the final output of the model is the representation of the target node, denoted as z.
Specifically, in cross-table modeling, Griffin uses tempo- ral heterogeneous graph representation of the RDB, where rows are modeled as nodes. These node embeddings are propagated and updated via GNN. The embedding for node at the 1-th layer, denoted as 14, is updated as follows:
Cross Attention Module The node feature vector 2; E xl presents three challenges:
h = Mean; (AMLP) (w,) (()))EE))
(3)
h = Maxi (1g+ Oe, IrE
The number of cells L, varies across nodes, so the encoder must handle variable-length inputs.
(4)
where AMLP is an MLP that transforms the aggregated node representations. First, the representations of neigh- boring nodes are averaged within each relation. Then, the maximum aggregation is applied across all relations. This step ensures that the representations from each relation not overwhelmed by others, which can be problematic when the number of neighbors across relations is unstable. h, is further added to u, to update node representations.
Ny contains rich information, some of which may not be relevant to the task. The encoder must selectively focus task-relevant information.
In RDB data, the column order is meaningless, so the encoder should be invariant to column permutation for better generalization.
To address these, Griffin introduces a cross-attention mod- ule that allows the model to selectively focus on relevant information from individual cells within a row (treated as node in graph). This enables the model to capture in- teractions between columns and rows, modeling complex dependencies critical for relational data analysis.
The subgraph is encoded to one vector by MPNN:
Input: The enriched rooted subgraph and task vector. Output: A fixed-length vector E Rdd.
3.3. Unified Task Decoder
Each row in a table is represented as an attention-based aggregation of its column data:
Given a fixed-length embedding from the MPNN output, we apply single task decoder per task type.
Attention; (QMLP,(w; (), m;)
(2)
Classification tasks We directly use the text embeddings of the target labels as the classification head. For example, when predicting the value of the (i.j)-th cell in a table, let 21, 22,22.... .2, &e E Rd denote the text embeddings of all candidate categories, and let 2 E R&d be the output vector
where / is the layer index, of is the output of the attention mechanism for node i, and QMLP, is an MLP that takes the node representation 24 E Rd and task representation t E d as inputs to produce the query for cross-attention.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
from Griffin. The prediction probability distribution is: softmax(((2, 2.) | .,,,.....).. .c),
Formally, for a row Ty with target column j' be pre- dicted, the pretraining objective is defined
(5)
loss = 1 - cos (Modele (Th)s) Encoder(TT.)))) (6)
where the logit for each category is obtained by the inner product between the output vector & and the corresponding category embedding 2i-
where Modelg (T) generates the row embedding and Encoder provides the true embedding for the masked column. The objective minimizes the cosine distance be- tween the predicted and true embeddings.
Regression tasks The output vector is passed through pretrained number decoder, denoted as DEC, to produce the predicted value. The final output can then be denormalized according to the specifications of the downstream task.
Joint Supervised Fine-Tuning Following completion pre- training, Griffin is jointly fine-tuned on selected realistic tasks to align it more closely with real-world tabular tasks. This stage utilizes both labeled single-table datasets and carefully selected RDB datasets, ensuring no data leakage into downstream evaluations.
Different tasks may share similar label embeddings or num- ber distributions, allowing the model to better capture task- specific characteristics and adapt to new tasks. Given the decoder design, the final prediction step is defined as:
The fine-tuning process optimizes the model for a of re- lated tasks, leveraging pretrained knowledge while adapting to the specific needs of tabular tasks. Griffin's 's task mod- eling framework, which supports both classification and regression in a graph-based RDB representation, employs a unified decoder (as in Section 3.3) to map output embed- dings to task predictions. Cross-entropy loss is used for classification tasks, while L2 loss is for regression tasks.
Input: A fixed-length vector E Rdd
Output: The predicted value for the target column TV
4. Training Pipeline
In this section, we describe the training pipeline of Griffin, which consists of pretraining and downstream task fine- tuning stages. The pretraining phase includes two compo- nents: Completion Pretraining and Joint Supervised Fine- Tuning (SFT), Both are designed to remain independent of the downstream tasks to avoid task-specific bias. The final stage involves task-specific fine-tuning, where Griffin adapted individual downstream tasks.
4.2. Downstream Task Fine-Tuning
A ffter completing pretraining and joint SFT, Griffin is fine- tuned on each individual downstream task for evaluation. This process follows the specific pipeline requirements of each benchmark to ensure fair comparison with baselines.
4.1. Pretraining
4.3. Model Variants Considered
Griffin is pretrained on a diverse set of datasets to ensure effective generalization across various RDB tasks. The pretraining process has two main components:
In practice, we consider three model variants based on the datasets used during training:
Griftio-unpretrainee refers to the model without any pre- training. This variant differs from other GNN baselines only in its architectural design, with no exposure to exter- nal data prior to downstream training, aiming to reveal the ad advantages of Griffin model design.
Single-Table Datasets: These are used to train the model on tasks involving individual tables, providing a founda- tional understanding of tabular data.
RDB Datasets: Griffin is also pretrained on large-scale, heterogeneous, and temporal graphs derived from multi- ple RDB domains. These graphs capture data structures.
refers the model pretrained exclu- sively on single-table datasets. We use a single pretrained checkpoint for fine-tuning across all downstream tasks. This checkpoint is strictly disjoint from any downstream tasks, ensuring there is no data leakage. This configuration iso- lates the effect of pretraining and highlights the potential for building a general-purpose foundation model for RDBs.
By using both single-table and relational data, Griffin learns to generalize across different types of RDBs, making it adaptable to a wide variety of tasks. To fully use rich sourced datasets, we include two stages for pretraining: completion pretraining and joint SFT.
Completion Pretraining We first use a completion task similar to language modeling but adapted for the tabular domain. The model learns to predict masked values within a row based on the remaining data. For a given row where one column is randomly masked, a column-invarian row encoder is used to generate the masked row's ''s embedding, which is used to predict the masked value.
Griffin- RDB-SFT is trained on a combination of single- table and RDB datasets. Since RDB datasets are used for both pretraining and downstream fine-tuning, we maintain multiple checkpoints, each trained on subsets of the RDB data that are non-overlapping with the downstream tasks. This setup also enables us to investigate the transferability of knowledge across different RDBs.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
5. Related Work
2023), and PytorchFrame (Hu et al., 2024) propose complete pipelines for converting RDBs into graph structures that can be used for GNN-based models. More recent efforts (Yuan et al., 2024; Chen et al., 2025) aim to design more expres- sive GNN architectures for relational data. These models perform well on individual RDB tasks, whereas Griffin is designed towards a foundation model that aims to generalize across wide range of relational tasks.
5.1. Tabular Predictive Tasks
Tabular predictive tasks involve learning to estimate missing or target values in structured tables. These tasks typically include classification and regression, using available fea- tures from the same table from related tables. Models are trained to capture statistical patterss within rows, across columns, and across multiple tables when relational data is present. Our model focuses specifically on this type of task.
5.2. Table QA Tasks
Single Table Models Research on single table data has evolved through various approaches. Traditional methods, such as XGBoost (Chen & Guestrin, 2016), LightGBM (Ke et al., 2017), and CatBoost (Prokhorenkova et al., 2018), have been widely adopted due their scalability and strong performance on structured data. More recently, transformer- based methods like Tab TabTrrasformmer (Huang et al., 2020), TabNet (Arik& & Pfister, 2021), FT-Transformer (Gorishniy et al., 2021), and SAINT (Somepalli et al., 2021) have lever- aged attention mechanisms to capture complex relationships within rows and columns. Additionally, graph-based meth- ods such as GRAPE (You et al., 2020), TabularNet (Du et al., 2021), TabGNN (Guo et al., 2021), and CARTE (Kim et al., 2024) represent tabular data graphs, incorporating multiplex and hypergraph structures to model interactions between rows and columns more effectively. Other works have explored improved encoding strategies for numerical features (Gorishniy et al., 2022: Yarullin & Isaev, 2023), while some have highlighted the benefits of incorporating nearest-neighbor information (Gorishniy et al., 2023; Ye et al., 2025). Although these models enhance feature in- teraction modeling, they primarily focus on single-table datasets and typically fail to model relational dependencies across multiple tables.
Table question answering (QA) tasks focus on answering natural language queries by reasoning over tabular data, Given question and a table (or a set of tables), the model must interpret the query, identify relevant cells, and ei- ther extract or compute the correct answer, or generate an executable SQL query. These tasks require both nat- ural language understanding and structured data reasoning. TaPas (Herzig et al., 2020) enhances BERT with a table- aware encoder. Tapex (Liu et al., 2021) explores learning neural SQL executor. OmniTTbb (Jiang et al., 2022) intro- duces pretraining using both synthetic and natural datasets. TableGPT2 (Su et al., 2024) treats tabular data as distinct modality for building general-purpose models. Numerous benchmarks have been proposed for comprehensive evalua- tion (Yu et al., 2018; Lei et al., 2024; Chen et al., 2019; Wu et al., 2024; Li et al., 2023; Qiu et al., 2024).
5.3. Foundation Models for Predictive Tasks
Graph Foundation Models (GFMs) aim to pretrain large models that generalize across multiple graph datasets and tasks. Many GFMs, such as OFA (Liu al., 2023) and Tape (He et al., 2023), integrate Large Language Models (LLMs) to enhance feature spaces assist in training GNNs, Other methods, like UniGraph (He & Hooi, 2024), adapt graph data for better LLM integration. While some GFMs, such as GraphTTxt (Zhao et al., 2023), convert graph struc- tures into language-like representations for processing by LLMs, others focus on novel GNN architectures, such as GraphAny (Zhao al., 2024). Griffin builds on the GFM paradigm but adapts it to RDBs by pretraining both single-table and multi-table data, incorporating advanced tabular-specins data encoders and graph-based components such as cross- attention to model table meta-information, making Griffin more suitable for RDBs compared to GFMs.
RDB Models RDBs extend the concept of single-table models by incorporating multiple interrelated tables, requir- ing models to capture both intra- and inter-table relation- ships. Early approaches, such as DFS (Kanter & Veera- 2020),
machaneni, 2015) and RDBTOGRAPH(Cvikee attempt to flatten RDBs into single table or apply GNNs to model relationships between tables. Other works, like ATJ- Net (Bai et al., 2021) and KEN (Cvetkov-Iliev et al., 2023), use hypergraphs and knowledge graphs to model tnter-table dependencies, while GFS (Zhang et al., 2023a) integrates differentiable single-table models as embedding functions to preserve table structures. Some methods that convert structured data into unstructured embeddings can still retain structural information (Grover& & Leskovec, 2016), such as EmbDi (Cappuzzo et al., 2020) and RDF2Vec (Ristoski & Paulheim, 2016). As RDB tasks have attracted increasing at- tention (Fey et al., 2024), more comprehensive benchmarks and toolboxes have emerged. For example, 4DBInfer (Wang et al., 2024), RelBench (Robinson et al., 2024; Fey et al.,
Tabular Foundation Models (TFMs) aim to generalize across tabular data, often leveraging transformer- ar- chitectures. Models such as TaBERT (Yin et al., 2020) and TabLLM (Hegselmann et al., 2023) integrate text and tab- ular data to enhance table structure understanding, while TransTab (Wang & Sun, 2022) and XTab (Zhu et al., 2023) explore transfer learning across tables with varying column structures. UniTabE (Yang et al., 2024) and TPBerta (Yan et al., 2024) employ specialized tabular encoders to better
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Griffin: Towards Graph-Centric Relational Database Foundation Model
SAGE GAT
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Figure 2: Performance Comparison of Fully Fine-Tuned Models on Individual Tasks. This figure compares the performance of four GNN baselines, four single-table baselines with DFS, and two Griffin variants, each fine-tuned on individual tasks. The leftmost subfigure presents the average rank across all tasks. The remaining subtigures group tasks by evaluation metric, with results averaged accordingly. All values are positive; higher values indicate better performance for Accuracy and ROC-AUC, while lower values are better for left ones.
align transformers with tabular formats. TabPFN (Hollmann et al., 2022; 2025) takes different approach by avoiding the use of text models and instead pretraining on a large num- ber of synthetic datasets. It achieves strong performance in few-shot settings. However, these models primarily focus on single-table data and lack mechanisms to capture inter- table relationships in RDBs. While Griffin incorporates transformer-basee and tabular techniques, it extends beyond existing TFMs by explicitly modeling relational structures across multiple tables, addressing the complexities in RDBs.
Single-Table Datasets: Over 200 datasets were curated from TPBerta (Yan et al., 2024) and CARTE (Kim et al., 2024), comprising approximately 10 million rows. These datasets were used for completion pretraining, enabling scalable learning without human-labeles data. Only 50 datasets contained labels for joint SFT. While additional large-scale datasets from diverse domains were collected, they were excluded from pretraining for two key reasons: (I) many were subsets of RDBs, making single-table pretraining ineffective, and (2) their distributions diverged significantly from downstream RDBs.
6. Experiments
RDB Datasets: We sourced large-scale temporal RDBs from two leading benchmarks, 4DBlnfer (Wang et al., 2024) and RelBench (Robinson et al., 2024), covering wide range of domains, scales, and tasks. A total of 24 tasks were selected for SFT and downstream evaluation.
In this section, we aim to address the following questions:
Q1: Can Griffin, with its advanced design, outperform existing models under the same training settings?
Q2: Can utilizing single pretrained checkpoint universally enhance predictive performance?
Baselines To ensure a fair comparison across benchmarks, we standardized valuation-related settings, which led to cer- tain modifications in the reported results. These adjustments include aligning preprocessing steps, normalization strate- gies, and other evaluation procedures. As result, some baseline results may differ from those originally reported in the respective benchmarks. We include four GNN baselines: S SAGE, GAT, PNA, and HGT. Additionally, we evaluate four single-table models enhanced with the Deep Feature Synthesis (DFS) method (Kanter & Veeramachaneni, 2015) to incorporate multi-table information. For evaluations that involve only single-table data without any relational context,
Q3: Can joint SFT with RDB improve transferabilityy, and under what conditions does provide the most benefit?
6.1. Experimental Setup
The experimental setup is designed to evaluate Griffin across diverse tasks and datasets, leveraging the pretraining and fine-tuning pipeline described in Section 4.
Datasets The selected datasets include both single-table and RDB datasets, with details provided in Appendix A.
7

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Griffin: Towards Graph-Centric Relational Database Foundation Model
wnitinn
br stttitttiiiioonn
Gentr-men--SN
completion pretraining joint SFT, both conducted exclu- sively on single-tabular datasets. Notably, RDB datasets are included in pretraining, ensuring no data leakage while demonstrating the adaptability of the pretraining framework across different domains.
Rank
ROC
Logloss
22
0.06
0.44
0.55-
Figure presents the performance comparison between Prrffin-retrriiee and Griffin unffii-uupretrained without pretrain- ing across multiple tasks. The results show that Griffin- Pretrained outperforms its non- con-pretrained counterpart, val- idating the universal benefits of pretraining. These find- ings confirm that pretraining a single checkpoint on diverse single-tabular datasets can significantly improve predictive performance, even for tasks in RDBs, despite the absence of RDB-specific data during pretraining.
1.5
0.04
0.76
0.54
144 0.6
3.08
0.52
1. 0.60
0.50
0.50
Figure 3: Ablation Study on Different Model Design Choices. This figure compares the performance of Griffin- unpretrained and two ablated variants, with cross-attention and aggregation removed, respectively. The leftmost subfigure presents the average rank across all tasks. The remaining subfigures group tasks by evaluation metric, with results averaged accordingly. All values are positive; higher values indicate better performance for ROC-AUC, while lower values are better for left ones.
6.4. Reply to Q3: Joint SFT with RDB Enhances Transferability, Driven by Similarity or Diversity
To analyze the factors influencing transferability, we pro- pose two key hypotheses, building on recent research on transferability (Ehrig et al., 2024).
we recommend referring to the original experimental results reported in 4DBInfer and RelBench, which have already demonstrated significantly weaker performance in the ab- sence of multi-table information. Further details on these modifications and baseline configurations are provided in Appendix B.
Similarity, Pretraining on datasets similar to the down- stream task improves transferability by providing aligned feature representations and task structures.
Diversity. Pretraining on a broader and more diverse set of datasets enhances transferability by improving the model's ability generalize across different domains.
pyperaarameters and Training Griffin was trained with fixed hyperparameters across all experiments to ensure ro- bustness. During pretraining, all single-table datasets were used for completion pretraining. while subsets of single- table and RDB datasets were selected for joint SFT based on specific experimental objectives. Further details about model settings are provided in Appendix C.
To test these hypotheses, we categorized datasets into two broad domains: commerce and others, each containing a diverse set of tasks. We further split each domain into two subsets, leading to four final groups: Commerce-l, Commerce-2, Others- 1, and Others-2.
Commerce-1 and Commerce-2 originate from e-commerce datasets, covering tasks such as user churn prediction and purchase rate estimation. These datasets are highly similar.
Commerce:
6.2. Reply to Q1: Meta-Information and Advanced Architecture Design Enhance Model Performance
Figure 2 presents the performance comparison of differ- ent models fully fine-tuned on individual tasks. Griffin- unpretrained outperforms all other models in average rank and demonstrates significant improvements.
Others: Others-1 1 and Others-2, despite belonging the same broad domain, exhibit significant internal diversity due to their inclusion of sports, social networks, flight records, and clinical data, leading to varying distributions.
To analyze the impact of key design choices, we conducted an ablation study on the cross-attention module and aggrega- tion functions in MPNN, with results presented in Figure 3. Replacing these components with plain average of column features and a mean-only aggregator for both intra-type and inter-type nodes results in a significant performance drop.
To evaluate transferability, we performed joint SFT on one group and tested it on each task of another group using limited-sample fine-tuning, making transfer effects clearly visible. To ensure robustness, each task was evaluated across five different random seeds for split selection. We compared two types of models to assess transferability:
No-Pretrain Model - OOll trained on downstream tasks. Griffin-RDD RDB-SFT Griffin pretrained on both single- tabular and selected RDB datasets.
6.3. Reply to Q2: Single-Table Pretraining Universally Enhances Model Performance
To answer Q2, we introduce PretriiP,e, a variant of Griffin that undergoes a pretraining stage before task- specific fine-tuning. The pretraining process consists of
The results, presented in Figure 4, provide insights into how similarity and diversity influence transfer learning.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
Commerce-1 1
Commerce 2
Others 1
Others
Performanc
Noltcten Oiher
NoPhcral
One were
Dataset Size
Dataset Size
Dataset Size
Dataset Size
Figure 4: Evaluating Transferability Across Different SFT Domains. This figure compares the impact of different SFT strategies on transferability. Each subfigure presents four models: a no no-pretraining baseline and three models pretrained on single-table data followed by SFT on different domains. By comparing performance relative to the no- no-pretraining baseline, we observe positive transfer effects when the SFT and downstream tasks are similar, as seen in commerce- cittnerrc----commeee settings. Additionally, SFT on the more diverse "Others" group improves performance on the "Commerce" domain.
Impact of Similarity on Transferability To evaluate the role of similarity, first analyze transfer performance between the commerce groups. The results show that both Commerce-1 I to Commerce-2 and Commerce-2 to Commerce-1 I benefit significantly from pretraining. Notably, pretraining on 2ommerce-2 and transferring to Commerce- outperforms all other pretraining domains, indicating that pretraining on highly similar datasets results in stronger transfer benefits. Conversely, when transferring from com- merce to non-commerce domains, the lack of similarity leads to poor transfer performance. In these cases, pre- training often underperforms compared to the no-pre no-pretrain baseline, resulting in substantial performance gap. This trend confirms our Similarity Hypothesis- the closer the pretraining dataset is to the downstream task, the stronger the transferability.
joint SFT strategies, including SFT with limited samples and mixed SFT with single-tabular datasets, detailed in Appendix D. The results show that full SFT achieves the best performance, reinforcing the benefits of complete pre- training. Furthermore, despite variations in SFT strategies, the observed transferability patterns across domains remain similar. This further validates the robustness of our domain transferability hypothesis and highlights the fundamental role of similarity and diversity in effective transfer learning.
We also conducted few-shot experiments compared with TabPFNv2 combined with DFS as a reference, as detailed in Appendix E. TabPFNv2 (Hollmann et al., 2025) is a powerful single-table model that supports few-shot learn- ing and even outperforms some state-of-the-art models on certain datasets. Although DFS is not ideal for few-shot scenarios and involves substantial preprocessing time, we include it for comparison. The results show that Griffin and TabPFNv2 each excel under different conditions.
Impact of Diversity Transferability Next, amine the role of diversity in transfer learning. When transferring from others to commerce, pretraining consis- tently outperforms the no-pretrain model, and in some cases, even surpasses the results of similar-domann commerce-to- commerce transfer. This suggests that diverse pretraining can sometimes be as effective as, or even better than, pre- training on similar datasets.
7. Conclusion
In this work, we proposed a graph-centric foundation model for RDBs that effectively incorporates meta- meta-information, leverages pretraining strategies, and supports diverse down- stream tasks. The proposed model demonstrates strong performance across various tasks by unifying single-table and cross-table embeddings using graph-baset representa- tions. Through extensive experiments, we addressed three key questions: (1) the model's ability to outperform existing approaches by encoding ieetaainformation, (2) its general- izability across multiple domains with a single pretrained model, and (3) the benefits of similarity and diversity on improving transferability. Our results highlight the impor- tance of aligning model design with the RDB structure and leveraging pretraining to enhance performance across tasks. It provides a robust foundation for advanced RDB research.
For transfering between Others-1 and Others-2, where similarity is low, we observe a one-directional transfer Cenefit-Othere effectively transfers to Others-2, but not vice versa. We hypothesize that this is due to greater dataset diversity in Others-1, which provides a broader pretraining foundation that improves generalization when fine-tuned Others-2. These findings confirm our Diversity Hypothe- sis the more diverse the pretraining dataset, the stronger the model's ability to generalize.
Additionally, we conducted further experiments on different

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Griffin: Towards Graph-Centric Relational Database Foundation Model
Acknowledgements
national conference on knowledge discovery and data mining, pp- 785-794, 2016.
This work is supported by the National Key R&D Program of China (2022ZD016030)), National Natural Science Foun- dation of China (62276003) and Beijing Natural Science Foundation (QY24046).
Chen, T., Kanatsoulis, C., and Leskovec, J. Relgon: Com- posite message passing for relational deep learning, arXiv preprint wrXiv:2502.06784 2025.
Chen, W., Wang, H., Chen, J., Zhang, Y., Wang, H., Li, S., Zhou, X., and Wang, W. Y. Tabfact: A large-scale dataset for table-based fact verification. arXiv preprint arXiv: 0209/099992200 024 2019.
Impact Statement
This paper advances the field of Machine Learning for rela- tional databases (RDBs) by exploring pretraining and trans- ferability in structured data. Our findings contribute to improving model generalization and adaptability across do- mains such as finance, healthcare, and e-commerce, where relational data is widely used.
Chepurko, N., Marcus, R., Zgraggen, E., Fernandez, R. C., Kraska, T., and Karger, D. Arda: automatic relational data augmentation for machine learning. arXiv preprint arXiv:2003.0975.. 2020.
While our work enhances general predictive performance and transfer learning, it may raises considerations regarding data biases and privacy. The effectiveness of pretraining de- pends on data quality and representativeness, and improper adaptation could introduce biases in downstream applica- tions. Additionally, working with large-scale relational data may pose privacy risk, particularly in sensitive domains. However, all datasets used in this study are publicly avail- able and have undergone privacy filtering to remove sensi- tive information, ensuring ethical research practices.
Cvetkov- -liev, A., Allauzen, A., and Varoquaux, G. Rela- tional data embeddings for feature enrichment with back- ground information. Machine Learning, 112(():687-720, 2023.
Cvitkovic, M. databases with graph neural networks. arXiv preprint arXiv:2002.02046 2020.
Supervised learning on relational
Du, I.., Gao, F., Chen, X., Jia, R., Wang. J., Zhang, J., Han, S., and Zhang, D. Tabularnet: A neural network architec- ture for understanding semantic structures of tabular data. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 322-331, 2021.
As our research is based entirely on publicly available datasets with no personally identifiable information, we believe no new ethical concerns should be introduced.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
A. Datasets
This section details the information of single tabular datasets and RDBs that we use in Griffin.
A.1. Single Tabular Datasets
We use approximately 200 single tabular datasets from the pretraining datasets of TPBerta (Yan et al., 2024) and datasets of CARTE (Kim et al., 2024) from Hugging Face. The row count distribution these datasets is shown in Figure 5 and Figure 6, while the column count distribution is shown in Figure and Figure These single tabular datasets cover wide range of domains, including healthcare, finance and business, social sciences, science and technology, entertainment, media, marketing and
Histogram of Row Counts of TeBeta
49
42
40
Frequency
20
20
T6
10
9
8
4
4
0
0
0-1
1-2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
9-10
10+
Row Count Ranges (x101)
Figure 5: Histogram of row counts of TPBerta
Histogram of Row Counts of CARTE
24
Frequency
15
10
2
2
2
2
0
0
0-1
1-2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
9-10
10+
Row Count Ranges (x (x10*)
Figure 6: Histogram of counts of CARTE
A.2. RDBs
The RDBs that we use are from two benchmarks, 4DBInfer (Wang et al., 2024) and RelBench (Robinson et al., 2024). covering a wide range of domains, scales, and tasks. The detailed information shown in Table 1.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
Histogram of Column Counts of TPBerta
59
59
60
Frequency
40
29
20
16
1
0-5
5-10 10-15 15-20
Column Count Ranges
Figure 7: Histogram of column counts of TPBerta
Histogram of Column Counts of CARTE
26
20
Frequency
17
10
6
0
0
0-5
5-10 10-15 15-20
35-3330-33554 40-45
Column Count Ranges
Figure 8: Histogram of column counts of CARTE
Dataset
Tables
Columns
Rows
Seznam
4
14
268 2681983
Airbnb
4
10800000
Amazon
3
24291489
Diginetica
5
3672396 28
Outbrain
8
31
4778954
Retailrocket
3
11
23033676
Stackexchange
7
49
5399818 18
Virus
3
38
145000
Telstra
5
12
136000
Talkingdata
3
36600000
Rel-avito
8
20679117
Rel-fl
9
77
97606
Rel-hm
3
37
33265846
Rel-trial
15
140
5852157
Table Statistics of relational database datasets.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
B. Baseline Settings
This section details the experimental settings of the baselines used 4DBInfer, RelBench, and Griffin. While these benchmarks share similarities in graph construction and sampling strategies, they differ in data processing methods and hyperparameter selection, which can impact comparability, particularly for regression tasks.
B.1. Comparison of 4DBInfer, Relßench, and Griffin Experiment Settings
Table 2 summarizes the key differences among the three baselines.
Table 2: Comparison of Baseline Settings in 4DBInfer, RelBench, and Griffin
Setting
4DBInfer
RelBench
Griffin
Graph Construction
R2N/ / R2NE
R2N
R2N
Numerical Processing
Quantile normalization
No normalization
Quantile normalization
Time Processing
Categorized and scaled per column
Cyclic encoding
Text description and scaling
Text Processing
GloVe embeddings
GloVe embeddings
Sentence model embedding
Category Processing
One-hot encoding
One-hot encoding
Text description encoding Fanout per node type
Sampling Strategy
Fanout is total neighbors across all node types
Fanout per node type
Hyperparameter Selection
Individually searched per task
Shared across tasks
Shared across tasks
Graph Construction and Sampling All three methods adopt the row-to-node (R2N) approach, with 4DBInfer also
extending it to wwwto-node-or-edge (R2NE). RelBench and Griffin define fanout per node type, whereas 4DBInfer treats fanout the total number of neighbors across different node types.
Data Preprocessing
The benchmarks differ in how they process numerical, temporal, textual, and categorical data:
Numerical Data: 4DBInfer and Griffin use quantile normalization, while RelBench does not apply specific normaliza- tion.
Temporal Data: 4DBInfer scales time values at the column level, while Griffin incorporates text descriptions alongside scaling. RelBench applies a cyclical encoding method.
Text Features: 4DBInfer and RelBench use GloVe embeddings (Pennington et al., 2014), while Griffin employs sentence model embeddings for richer representations. Specifically, the model used is Nomic (Nussbaum et al., 2024).
Categorical Data: 4DBInfer and RelBench use one-hot encoding, whereas Griffin encodes text descriptions instead of
static categories.
Hyperparameter Selection 4DBInfer performs independent hyperparameter searches per task, while RelBench and Griffin use shared hyperparameter configuration for consistency across tasks.
B.2. Metric Adjustments for Regression Tasks
While most differences preprocessing and sampling strategies reflect model-specifie preferences, they generally do not affect comparability across benchmarks. However, for regression tasks, differences in numerical processing can lead to significant variations in results.
4DBInfer applies quantile normalization to all numerical data and predicts normalized values instead of raw targets. To ensure consistency, we adopt similar approach by implementing unified number decoder that operates on normalized outputs. In contrast, RelBench's 's original regression tasks predict raw numerical values, which can range from 0.01 to 100. While our model can make predictions in this format through an additional denormalization step, we follow 4DBlnfer's normalization strategy for better robustness and stability in target value distributions. The updated Relbench results are shown at Table
15

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Griffin: Towards Graph-Centric Relational Database Foundation Model
B.3. Baseline Implementation Details
The baseline models are primarily adapted from the 4DBInfer framework, as most lacked native implementation in RelBench. For the 4DBInfer datasets, report the results directly from the original publication. For the RelBench datasets, while the Sage results are taken from the original report, all other baselines were re-evaluated. This process involved processing the RelBench datasets through the 4DBInfer pipeline. To ensure methodological consistency, we adhered to RelBench's hyperparameter tuning strategy and incorporated a ResNet architecture into the encoder as specified in their design.
Table 3: Comparison of RelBench Original Results and Aligned Griffin Results
Model
rel-avito/ad-ctr
rel-f1/position rel-hn/item--sals
rel-triallsite-success
rel-triallstudy-adveess
RelBench Original Results
0.041
4.022
0.056
0.400
44.473
Aligned Griflin Results
0.7686
0.5945
4.440
0.853
2.199
This decision ensures more reliable evaluation of model performance while maintaining consistency across tasks and datasets.
C. Experiment and Model Details
This section provides a comprehensive overview of the experimental setup, including model updates and hyperparameter configurations. To ensure consistency and robustness, experiments were conducted using a fixed set of model design choices and hyperparameters.
C.1. Model Updates: Improved Cross-Attention for RDBs
In our initial model design, we applied cross-attention between task embeddings, column names, and column values to aggregate information. However, for relational databases (RDBs), we observed that in the first layer, this approach might not provide sufficient information for retrieving task-relevant columns. For example, in an RDB containing user, product, and purchase tables, predicting user-related information may be difficult without first aggregating data from the user's purchase history. This limitation was particularly evident in retrieval-related tasks, where the first-layer cross-altention often degraded to mean aggregation, as shown in Figure 9.
To address this issue, we modified the first-layer cross-attention to self-attention mechanism over column names and column values. This adjustment allows the model to capture column dependencies before applying task-conditioned aggregation in later layers, improving its ability identify relevant features. Based on experimental results, this modified approach led better retrieval performance and overall stability, and
the default configuration for all experiments.
C.2. Hyperparameters
To ensure reproducibility, we used a fixed set of hyperparameters across all experiments. These configurations span optimization settings, model architecture, graph sampling strategies, and pretraining procedures.
For optimization and training, employed the AdamW optimizer with a learning rate of 3e-4 and an L2-norm regularization of 2e-4. A batch size of 256 was used for all training runs. Early stopping was applied with patience of 10 epochs to prevent overfitting, ensuring stable convergence. No additional learning rate scheduler or gradient clipping was used.
The model architecture was designed with a hidden dimension of 512, maintaining consistency between different components. The sentence embedding model was based on Nomic embeddings, truncated to 512 dimensions. The cross-attention module included attention heads and dropout rate of 0.1, allowing for effective feature extraction while preventing overfitting. SiLU was chosen the activation function across all layers.
For graph construction and sampling, we adopted a 4-layer message-passing neural network (MPNN) with 2-layer uniform sampling on temporal neighbors. The fanout was set to 20 per layer to ensure a balanced trade-off between computational efficiency and capturing structural information. Additionally, reversed edges were incorporated into the sampled subgraph improve relational modeling.
16

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Griffin: Towards Graph-Centric Relational Database Foundation Model
Shape: (1536, 5121
Shape: (1536, 5121
83
203
1.0
632
632
65
-00
000
-005
3373
3373
1396
1336
2275
2213
1997
1957
2474
Layas Atton Han Whighhts
Layer 3 Aliuntion Weightts Shape: 11535, 5121
Shape: 11335, 5:21
-1.0
630-
6c4
3305
3033
1:39
3233
-0.
1009
1226
1340
3390
3454
3454
Figure 9: Cross-attention weight visualization across four layers. The heatmap shows that in the first layer, query and value activations are near zero, resulting in nearly uniform attention weights. This indicates that first-layer cross-attention effectively reduces to mean aggregation.
Regarding pretraining and fine-tuning, the completion pretraining phase used single-tabular datasets with early stopping signals derived from specific task performance. The same early stopping strategy was applied to supervised fine-tuning (SFT) using a combination of single-table and RDB tasks. The loss functions were cross-entropy loss for classification tasks and L2 loss for regression tasks.
The experiments were conducted on an AWS g6.48x instance, ensuring sufficient computational resources for large-scale graph training. Mixed precision (FP16) was not used, and gradient checkpointing was not applied.
These hyperparameter settings were selected to ensure stable and scalable training process while maintaining compatibility across different relational database tasks.
D. Extended Experiments on Joint SFT Strategies
In this section, we provide additional experiments to analyze the impact of different SET strategies on transferability. Specifically, we investigate two key aspects: (1) The performance of different SFT strategies in transfer learning. (2) Whether domain-driven transferability conclusions remain valid across different SFT strategies.
D.1. Experiment 1: Performance Different SFT Strategies
To evaluate the impact different SFT strategies, we compare five baselines:
A no-pretrain baseline, trained directly on downstream tasks.
A single-table-only pretrained baseline, without any RDB pretraining.
Three joint SFT baselines, all using the same SFT domain but differing in their SFT strategies:
Full SFT: Standard supervised fine-tuning using all available samples.
Limited-Sample SFT: Fine-tuning with a restricted subset of 4096 samples.
17

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Griffin: Towards Graph-Centric Relational Database Foundation Model
Mixed SFT: Joint fine-tuning with both single-tabular datasets and RDB datasets.
These basclines were evaluated on three verified transferable settings:
Commerce-2 to Commerce-1
Commerce-1 to Commerce-2
Others-1 1 to Others-2
The results are presented Figure 10.
Commerce-2-44 Saaee-e---------re-eeeee 1ommomcccc-1
Commerce- Cemmercce-ltt--ooe 1-to-Commercce
Shters-1-oo-Othera
Performance
Performance
Modeb
Nocels
Models
SFT
SFT SFT limitedd
SPT
SIT Iithed
SFT lriite
SH1 mesel
SF insecd) Mo-Phetrain
5P1 insoo)
Singlelztee-Glly
Senple-lttee
Sengle Pretoan
Dataset Size
Dataset Size
Dataset Size
Figure 10: Performance comparison of different SFT strategies. The figure presents the results for five baselines, including no-pretraining, single-table-only pretraining, and three joint SFT strategies with varying settings. Full SFT consistently achieves the best performance across different domains, emphasizing the benefits of sufficient training.
Observing the results, we find that full SFT consistently achieves the best performance across all settings, demonstrating its superiority in transfer learning. Other baselines occasionally perform worse than the no-pretrain model, indicating their limitations effective knowledge transfer. This further underscores the importance of sufficient training for successful adaptation.
D.2. Experiment 2: Evaluating Domain Impact Across SFT Strategies
To determine whether domain-drivee transfcrability conclusions remain consistent, we evaluate each SFT strategy across different SFT domains. The goal is to analyze whether transfer performance is significantly influenced by the pretraining domain. The results are presented in Figure 11 and Figure 12.
The results indicate that domain transfer effects notably weakened under the SFT-limited strategy, suggesting that restricted sample size hinders adaptation. In contrast, for the SFT-mixed strategy, domain transfer differences remain clearly visible, potentially indicating that SFT-mixed enables a more comprehensive adaptation compared to SFT-limited.
To further quantify the effect of domain transferability, we compute a critical difference ranking (Terpilowski, 2019) to measure average rank improvement across different settings. This serves as straightforward yet effective method to analyze domain impact on transferability. The results are shown Figure 13.
In general, observe that similarity and diversity play crucial role in transferability to "commerce" domains, reinforeing our original hypothesis. For the "others" domain, the trend remains similar but with a weakened effect. We hypothesize that incomplete SFT adaptation prevents full alignment with the pretraining domain, leading to weaker yet general transfer effect.
These results confirm that while different SFT strategies can influence absolute transfer performance, the underlying domain-driven transferability trends remain robust.
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Griffin: Towards Graph-Centric Relational Database Foundation Model
Commerce-1 1
Commerce 2
Others 1
Others
Perfomance
Perforance
Perfomance
Nothiren Others and
Kochicte Oiher
RoPheeen Ore vore 1P leane
RoPhires Orrmonal SFT
Dataset Size
Dataset Size
Dataset Size
Datasel Size
Figure 11: Impact of domain transferability under limited-sane SFT. The figure compares transfer performance across different SFT domains when fine-tuning is restricted to 4096 samples.
Commerce-1
Commerce-2
Others-1
Others-2
Modelk Phoren
Piccel No Prtui
bet
Dataset Size
Datasot Size
Dataset Size
Dotaset Size
Figure 12: Impact of domain transferability under mixed SFT with single-tabular datasets. The figure evaluates whether incorporating single-tabular tasks during SFT affects transferability trends.
E. Comparison with TabPFNv2 + DFS
This section presents a comparison between TabPFN v2 with DFS and Griffin, shown in Figure 14. Although DFS can require several hours of preprocessing, reported in 4DBInfer, we include it for comparison because TabPFN v2 is a strong single-table foundation model, particularly effective few-shot settings. The results suggest that Griffin performs better on the Commerce-2 and Others-2 tasks, while TabPFN v2 shows superior results on Commerce-1 I and Others-I tasks.
F. Raw Results
This section presents the raw experimental results for figures.
Table 4 correspond to the Figure 2. Table 5 corresponds to Figure 3. Table 6 7 8 9 correspond to Figure 4. Table 10 11 12 13 correspond to Figure 11. Table 14 15 16 17 correspond to Figure 12. Table 18 19 20 21 correspond to Figure 14.
19

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Griffin: Towards Graph-Centric Relational Database Foundation Model
SFT
SFT nixed
limitedd
928
130
65
Figure 13: Critical difference ranking of domain transfcrability across different SFT strategies. The three subfigures represent heatmaps for Full SFT, Mixed SFT, and Limited-Sample SFT. Each cell denotes the relative gain in transfer performance from the row's 's domain to the column's 's domain, compared to the no-pretrain baseline. The diagonal cells do represent SFT on the target domain but rather pretraining using only single-tabular datasets.
Commerce-1
Commerce-2
Others-1
Others-2
Nepeeten usu srt Maans-ds
Hoyenturs
Neymmbe's Conmnic
Gan secua
Doteset Size
Detaset Size
Dotaset Size
Doteset Size
Figure 14: Evaluating Few-shot Performance: Comparison Between Griffin and TabPFNv2. This figure compares the transfcrability of different supervised fine-tuning strategies and TabPFNv2 in few-shot settings. Each subfigure shows the performance of five models: no-pretraining baseline, three Griffin variants pretrained on single-table data with SFT applied to different domains, and TabPFNv2. For datasets with more than 10 classes, TabPFNy2 is not applicable; therefore, such tasks are excluded from the comparison.
20

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Griffin: Towards Graph-Centric Relational Database Foundation Model
Table 4: Raw Results of Griffin and All the Baselines
Oreril Scrre Rank
Sermomihhony
Sonaminnqpy
Airbubslledination ROCAUCT
Amannaichura ROCAUC
Peginelicakte ROCALC
Outhrainiet ROCAUCT
Metric
Ascuraty
Accuracy
ROCAECT
ROCAUC
Sige
4.792
0.7917
0,5765
0/844
0.7158
0.7273
0.52.9
0,5090
0.663
Out
5.216
0.8053
015054
0/428
0,3410
0.674]
0.5146
B15638
0/6417
Phu
5:355
0.910
018211
05454
0/1565
0.20:1
0,829
0.878
0.667
Hgt
5,338
0.265
0.8035
08152
00551
0.6733
0.530
0.556
0.214
DE SLMM
6333
0.3554
0.224
07643
06315
0.9244
0.5456
0.539
0.521
1,053
0
0.7561
0.6922
0.7219
0,5421
0.5025
0,566
Grlfle ougretaaeee
1.716
0.755
0.894]
03615
0.3307
0.757
0.5246
0.5639
0/666
Grittle protuited
1/042
038133
0.9055
0.661
0.417
0.7111
0.6253
0,6590
0.646
Mlddl
Rel-Ehlogd
Rebhmbase-hurr
Retailnacaaiier
Strkschungskhem
Virudous
Ammonn/ratin
Metric
ROCAUC 1
ROCALCT
ROCALEC1
ROCADC1
ROCAUC?
ROCALC
ROCALLC"
ROCAECC
RMSSE I
Sege
0.7202
0.6100
0.840
03358
0.5561
0,5610
0:9639
Out
0.7945
0,6788
0.6536
08834
036655
0.5853
0.5400
0:9663
ligs
RES-MLP
0.5003
DeSADepffa
0.6205
0:8133
010071
04310
0.113
05212
0.8871
0.6315
0.9546
0.791
0,8710
010000
04178
0.8014
08576
0.87000
0.5000
0.9958
XGB
0.7158
0,9022
0U0785
06621
0.7506
03251
0.5675
0.7142
0.9972
Guiliis axpiciaiedd
0,7052
0.7855
0.6847
0,6722
0,5512
03457
0,5055
0.5600
0.611
Criffie petriited
0.7091
0.7755
0.6004
0.696
0:9543
03435
0,592
0,0975
0.59%l
Madel
Rakasstibddett
Rel-tt/pudlion
Rek-hm//eem-obe
Tidiesrrity
Tulk/lemm-pee
Metric
MAR.
MAR
MAE:
MAY
Lagloss
Eagian
1.3565
0.1911
1.5151
0.151
2.3620
Gui
1.3015
11085
0.8931
1.458
06191
2.3600
Tho
088444
0.6144
14758
04512
1.4116
0,2015
2.16-0
Tigs
0.665
0.6112
1.5455
09180
3.5000
0.625
2,159)
DEM+MLP
0.660
0.644
1.2118
09462
2,1108
08135
2.3640
DeS-De
1,1754
2,5756
1.3540
0.5755
28596
0.118
2.3610
DeS Formfforeee
066375
0.9111
119255
045795
18997
0.650
2.3630
DES-XCIB
0.6233
04130
1.2557
00516
2.155
0.683
2.3000
0.6993
0.5386
0.8279
0.7926
1,1700
0.5644
2,3000
Guitle pestiiied
0.6386
0.5694
0.5902
0.7945
12148
0.538
2,3000
Table 5: Raw Results of Figure 3 on Ablation Study
Model
Avg. Rank
Digineticaktr
Outbrainktr
Rel-IL/DNF
Rel-[I/Tog3
Red-triallsttu)--uueeea
Virus/wa
Rel-avitolad--tr
Rel-II/Posstion
0.7157
0.6246
0.7052
0.7855
0.6722
0.6680
-0.6593
-0.5586
Griffin-sog-atention
277
0.6917
0,625
0.6936
0.7255
0.6797
0.5844
-0. -0.7600
-0.6677
Grifia-mein-GNN
0,7077
0.6269
0,6950
0,7521
0.6857
0.6648
-0,6701
-0,5645
Model
Red-trialistady-aavveee
Telisverity
Grifiin
-1.1700
-0.6844
-1.3380
..54440
Grifinn mean- GNN
-1.2322
0.5691
Table 6: Raw Results of Figure 4 on Commerce-1 Transfer
Dataset/Task
Diginetica/ctr
Rel-hm/item-sales
Rel-hm/uuserchurn
Retailrocket/evr
Seznam/charge
Seznam/prepay
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5001
0.5044
-1.2976
-1.5236
0.5383
0.5592
0.7693
0.8002
0.4250
0.7260
0.5652
0,8180
Commerce-2 SFT
0.5213
0.5904
-1.6385
-1.4594
0.5677
0.6039
0,8452
0,9576
0.5662
0.7070
0.6456
0.7816
Others-1 SFT
0.5294
0.5662
-18005
-1.5055
0.5552
0.5885
0.8231
0,9446
0.6284
0.7197
0.7025
0,8030
Others-2 SFT
0,5503
0.5480
-1.6472
-1.6349
0,5340
0,5693
0,8006
0,9618
0,5902
0.7098
0,7060
0,7969
Table 7: Raw Results of Figure 4 on Commerce-2 Transfer
Dataset/Task
Amazon/churn
Amazon/rating
Outbrain/etr
Rel-avito/ad-ctr
Rel-avvito/ser-diikks
Rel-avito/user-viiitss
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5977
0.6580
-0.7663
-0.7007
0.4951
0.4990
-0.7430
-0.6558
0.5678
0.6187
0.6085
0.6108
1 SFT
0.6396
0.6859
-0.7740
-0,6809
0.5102
0,5892
-0.7267
-0.6938
0,5512
0,5919
0,5779
0.6111
Others-I SFT
0.5723
0.6675
-0.7428
-0.6754
0.5208
0,5096
-0.7134
-0.6550
0,6078
0.6056
0.6129
0.6198
Others-2 SFT
0.6231
0.6645
.0.7513
-0.6797
0.5245
0.6163
-0.7098
-0.6518
0.5481
0.5951
0.5944
0.6275
21

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Griffin: Towards Graph-Centric Relational Database Foundation Model
Table 8: Raw Results Figure on Others-I Transfer
Dataset/Task
Rel-fI/DNF
Rel-Fl/position
Rel-Fi/top3
Stackexchange<chure
Stackexchange/uprote
Virus/wny
Size
4096
512
4096
4096
512
4096
4096
512
4096
No-pretrain
0.6558
0.7176
-0.6152
-0.5746
0.7676
N/A
0.7256
0.7951
0.8433
0.8772
0.6099
0.6652
Others-2 SFT
0.7043
0.7248
-0.7056
-0.5921
0.7900
N/A
0.7188
0.7928
0.8571
0.8731
0.5706
0.6567
Commerce-l SFT
0,6823
0.7233
-0.6221
-0.6043
0.6831
N/A
0.6602
0.7501
0.8107
0,8597
0.6101
0.6461
2 SFT
0.6073
0.7347
-0.6255
-0.5931
0.7679
N/A
0.7600
0.8164
0.8544
0.8745
0.5946
0.6591
Table 9: Raw Results Figure on Others-2 Transfer
Dataset/Task
Airbab/destination
Rel-trial/ste--uucess
Rel-trialistuy--adde
Rel-trial/study-outte
Talk/demo-pre
Tel/severity
Size
512
4096
512
4096
512
4096
$12
4096
512
40%6
512
4096
No pretrain
0,8562
0.8564
0.9327
0.9062
-2,5758
-1,3676
0.6283
0.6656
-24363
-2,4245
.0,7910
-0.5998
Others-I I SFT
0,8470
0.8561
-0.9142
-0.8952
-1.5832
-1.3246
0.6436
0.6781
-2.4407
-2.4177
-0.7716
-0,6052
Commercee SFT
0.8457
0.8543
0.9231
0.9345
-1.9699
-1.5188
0.5583
-24377
-241777
.0.7965
006523
Commerce-2 SPT
0.8007
0.8516
409363
-0.4966
-1,6756
-1,3703
0.6001
0.6567
-24394
-241144
-0.7721
406626
Table 10: Raw Results of Figure 11 on Commerce-1 Transfer
Dataset/Task
Diginetica/ctr
Rel-hm/item-sales
Rel-hm/ussr-churn
Retailrocket/evr
Seznam/charge
Seznam/prepay
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0,5001
0.5044
-1.2976
-1.5236
0.5383
0.5592
0.7693
0,8002
0.4250
0.7260
0.5652
0.8180
Commerce-2 SFT
0.4655
0.4969
-1.6560
-1.4736
0.5376
0.5728
0.8368
0,9577
0.5119
0.7214
0.6478
0.8091
Others-1 SFT
0,5266
0.5744
-1.7979
-1.4878
0,5465
0.5704
0,8512
0.9601
0.5499
0.7237
0.5557
0,8075
Others-2 SFT
0.5425
0.5465
-1.5732
-1.6291
0,5583
0.5719
0.8848
0.9215
0.5641
0.7195
0.5563
0,6780
Table 11: Raw Results Figure 11 on Commerce-2 Tasks
Dataset/Task
Amazon/churn
Amazon/rating
Outbrain/ctr
Rel-avito/ad-ctr
Rel-avitt/user--lickss
Rel-avito/user-vistt
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5977
0.6580
-0.7633
-0.7007
0.4951
0.4990
-0.7430
-0.6558
0.5678
0.6187
0.6085
0.6108
Commerce-1 SFT
0.5970
0.6382
0.7480
-0.6941
0.5126
0,5080
-0.7115
-0.6503
0.5028
0.5741
0.5275
0.6050
Others-1 SFT
0.6118
0.6673
-0.7541
-0.6857
0.4930
0,5008
-0.7097
-0.6522
0.6047
0.6009
0.6090
0.6099
Others-2 SFT
0.6147
0.6624
-0.7540
-0.6783
0,5085
0.5873
-0.7260
-0.6579
0.5568
0.6062
0.6112
0.6081
Table 12: Raw Results of Figure 11 on Others-1 Tasks
Dataset/Task
Rel-fI/DNF
Rel-f1/position
Rel-f1/top3
Stackexchaunge/curn
Stackexchange/upvote
Virus/wny
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.6558
0.7176
-0.6152
-0.5746
0.7676
N/A
0.7256
0.7951
0.8433
0.8772
0.6099
0.6652
Others-2
0.7098
0.7275
-0.6680
-0,5739
0.7710
N/A
0.6592
0.7898
0.8530
0.8761
0.6148
0.6737
1 SFT
0.6752
0.7226
-0.6089
-0,5813
0.7375
N/A
0.6477
0.7337
0.8406
0.8770
0.6397
0.6603
Commerce-2 SFT
0.5808
0.7137
-0.6433
-0,5752
0.7536
N/A
0.6526
0.7839
0.8605
0.8779
0.6265
0.6676
Table 13: Raw Results of Figure 11 on Others-2 Tasks
Dataset/Task
Airbnb/destination
Rel-trial/sie-success
Rel-trial/stud--adeer
Rel-trial/sudd--outcomm
Talk/demo-pre
Tel/svvrrtty
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pectrain
0,8562
0,8564
-0.9377
-0.0622
-2.5758
-1.3676
0,6283
0.6656
-243363
-2.4245
-0,7910
-0.5998
Others-1 1 SFT
0,8489
0.8550
-0.9166
-0.9155
-1.6351
-1.3448
0.6163
0.6730
-24309
-2.4160
-0.8007
-0.6114
Commercce- SFT
0,8480
0.8546
0.9220
0.8820
-1.5500
-1.3571
0.6020
0.6639
-24318
-2,1442
.0.8000
40.6001
Commerce-22 SFT
0.8427
0.8534
0.9332
0.8927
-1,3329
-1.3321
.2,2222
.0.8755
00.6008
22

---

Griffin: Towards Graph-Centric Relational Database Foundation Model
Table 14: Raw Results of Figure 12 on Commerce-1 Tasks
Dataset/Task
Digineticw/etr
Rel-hm/item-sale
Rel-hm/user-churn
Retailrocket/evr
Seznam/charge
pezmam/prepay
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5001
0.5044
-1.2976
-1,5236
0.5383
0.5592
0.7693
0.8002
0.4250
0.7260
0.5652
0.8180
Commerce-2 SFT
0.4786
0,5145
-1.6061
-1,5209
0.5492
0.5835
0.8302
0,9675
0,5689
0.7089
0.6101
0.7952
Others-1 SFT
0,4808
0,5316
-1.5613
-1,4722
0,5525
0.5834
0,8130
0,9612
0.6408
0.7231
0.6749
0.7958
Others-2 SFT
0.4500
0.5539
-1.4179
-1,5491
0.5528
0.5938
0.8009
0.9630
0.6092
0.7026
0.6139
0.7776
Table 15: Raw Results of Figure 12 Commerce-2 Tasks
DataseU/Task
Amazzo//churn
Amazon/rating
Outbrain/ctr
Rel-avitofad-ctr
Rel-avito/user-ciiks
Relavitto/use--viitss
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5977
0.6580
-0.7663
-0.7007
0.4951
0.4990
-0.7430
-0,6558
0.5678
0.6187
0.6085
0.6108
Commerce-1 SFT
0.6220
0.6730
-0.6688
-0.6861
0.4966
0.5659
-0,7428
-0.6630
0,5812
0,5794
0.6000
0.6162
Others-1 SFT
0.6241
0.6634
-0.5533
-0.6824
0.5208
0.6134
-0.7418
-0.6723
0.5858
0.6229
0.6046
0.6130
Others-2 SFT
0.6278
0.6592
-0.7864
-0.7019
0.5553
0.6206
-0.7070
-0.6614
0.5724
0.6262
0.6212
0.6238
Table 16: Raw Results of Figure 12 on Others-1 Tasks
Dataset/Task
Rel-fI/DNF
Rel-tt/positio
Rel-ft/top3
Stackexchange/churn
Stackexchange/upvvat
Virus/wny
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.6558
0.7176
-0.6152
-0.5746
0.7676
N/A
0.7256
0.7951
0.8433
0.8772
0.6099
0.6652
Others-2 SFT
0.6820
0.7123
-0,6000
-0.5955
0.7521
N/A
0.6448
0.7691
0.8414
0.8670
0.6041
0.6335
Commerce-1 SFT
0.6492
0.7131
-0.2286
-0.6978
0.6441
N/A
0.7188
0.7552
0.8384
0.8599
0.5259
0.6075
Commerce-2 SFT
0.7123
0.7229
-0.6226
-0.5982
0.7698
N/A
0.7429
0.8111
0.8491
0.8726
0.6315
0.6564
Table 17: Raw Results of Figure 12 on Others-2 Tasks
Datased/Task
Airbab/destinatiinn
Rel-triallsit-ssucces
Rel-triallstudy-adveees
Rel-trialstudd--nttom
Talk/demo-pred
Tellvverity
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pectrain
0,8562
0.8564
-0.9327
-0.9062
-2.5758
-1.3676
0.6283
0.6656
-2.4363
-2.4245
-0.7910
-0.5998
Others-1 1 SFT
0,8512
0.8548
-0.9245
-0.9246
-1.6203
-1.3474
0.5977
0.6637
-2.4396
-2.4225
-0.7844
-0.6151
Commerce-- SFT
0,8388
0.8541
-0.9272
-0.3662
-1,6600
-1,4631
0.5729
0.6460
-2.4710
-2,4219
00.7749
-0.6815
Commerce 2 SFT
0.8477
0.8518
0093377
-0.9177
-1,6238
-1.3327
0.6143
0.6599
-2.4373
-2,4139
.0.7966
.0.6112
Table 18: Raw Results of Figure 14 on Comparison with TabPFN on Commerce-1 Transfer
Dataset/ Task
Diginetica/ctr
Rel-hm/item-sales
Rel-hmm/use-chhrn
Retailrocket/evr
Seznam/charge
Seznam/prepay
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5001
0.5044
-1.2976
-1.5236
0.5383
0.5592
0.7693
0,8002
0.4250
0.7260
0.5652
0.8180
Commerce-2 SFT
0.5213
0.5904
-1.6385
-1.4594
0.5677
0.6039
0.8452
0,9576
0.5662
0.7070
0.6456
0.7816
Others-I SFT
0.5294
0.5662
-180025
-1.5055
0.5552
0.5885
0.8231
0.9446
0.6284
0.7197
0.7025
0.8030
Others-2 SFT
0.5503
0.5480
-1.6472
-1.6349
0,5340
0.5693
0.8006
0,9618
0.5902
0.7098
0.7060
0.7969
DDFS
0.6696
0.7588
-1,8877
-1.3855
0.6647
0.6746
0.7769
0.7928
0.7098
0.7289
0.7639
0.7817
Table 19; Raw Results of Figure 14 on Comparison with TabPFN on Commerce-2 Transfer
Dataset/Task
Amazon/churn
Amazon/rating
Outbrain/ctr
Rel-avito/ad-ctr
Rel-avita/user-clickss
Relavitoo/user-vistss
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0.5977
0.6580
-0.7663
-0.7007
0.4951
0.4990
-0.7430
-0.6558
0.5678
0.6187
0.6085
0.6108
Commerce-1 SFT
0.6396
0.6859
-0.7740
-0.6809
0.5102
0.5892
-0.7267
-0.6938
0.5512
0.5919
0.5779
0.6111
Others-1 SFT
0.5723
0.6675
-0.7428
-0.6754
0.5208
0.5096
-0.7134
-0.6550
0.6078
0.6056
0.6129
0.6198
Others-2 SFT
0.6231
0.6645
-0.7513
-0.6797
0.5245
0.6163
-0.7098
-0.6518
0.5481
0.5951
0.5944
0.6275
DDS
0.6283
0.6351
-1.0317
-1.0032
0,5211
0,5382
-0.7034
-0.6913
0.6125
0.6395
0.6380
0.6576
23

---

Griffin: Towards Graph-Centric Relational Database Foundation Model
Table 20: Raw Results of Figure 14 on Comparison with TabPEN on Others-1 Transfer
Dataset/Task
Rel-fI/DNF
Rel-f1/position
Rel-f1/top3
Stackexchangecchurn
Stackexchangclupooee
Virus/wny
Size
4096
512
4096
4096
512
4096
4096
4096
No-pretrain
0.6558
0.7176
-0.6152
-0.5746
0.7676
N/A
0.7256
0.7951
0.8433
0.8772
0.6099
0.6652
Others-2 SFT
0.7043
0.7248
-0.7056
-0.5921
0.7900
N/A
0.7188
0.7928
0.8571
0.8731
0.5706
0.6567
Commerce-1 SFT
0.6823
0.7233
-0.6221
-0.6043
0.6831
N/A
0.6602
0.7501
0.8107
0.8597
0.6101
0.6461
Commerce-2 SFT
0.6073
0.7347
-0.6255
-0,5931
0.7679
N/A
0.7600
0.8164
0.8544
0.8745
0.5946
0.6591
TahPFN+DFS
0.7120
0.7346
-0.6587
-0.5962
0.8003
N/A
0.7886
0.8212
0.8562
0.8649
0.7666
0.7905
Table 21: Raw Results of Figure 14 on Comparison with TabPFN on Others-2 Transfer
Dataset/Task
Airbnb/destination
Rel-triallsite-uucees
Rel-triallttudl-aaveeee
Rei-trialittud--nttoone
Talk/demo-pree
Telsevvrity
Size
512
4096
512
4096
512
4096
512
4096
512
4096
512
4096
No-pretrain
0,8562
0.8564
.0.9277
-0.0662
-2.5758
-1.3676
0,6283
0.6656
-2.3633
-2.2455
.0.7910
.0.5998
Others- I SFT
0.8470
0.8561
-0.9122
-0.8952
-1,5832
-1.3246
0.6436
0.6781
-2.4077
-2.4177
-0.7716
-0.6052
Commerce-1 SFT
0.8457
0.8543
-0.9231
-0.9345
-1,9609
-1.5188
0.6493
-2.4377
-2.4177
-0.9965
-0.6523
Commerce-2 SFT
0.8007
0.8516
-0.9363
-0.9496
-1.6756
-1.3703
0.6001
0.6567
-2.4394
-2.4164
-0.7721
-0.6266
TabPEN-DES
N/A
N/A
-0.9687
-0.9873
-1.9763
-1.6555
0,5861
0.6602
N/A
N/A
-0.8865
+0,8505
24


Reviews:
[{"review_id": "65ZOAyhilo", "reviewer": "Reviewer_zrC4", "summary": "This paper introduces Griffin, which is claimed to be a novel foundation model specifically designed for RDBs. Griffin aims to unify differet tasks from single table to multi table RDBs. To do that, Griffin is pretrained by sampling the sub graphs from RDBs, and use a unified encoder/decoder to generate unified embeddings for different tasks. Experiments shows that Griffin has good performance across different tasks.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer zrC4 for the detailed and constructive feedback. Below, we address each of the main concerns. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Rebuttal\n\n---\n\n**Claim 1: Novelty of the Proposed Approach**\n\nWe appreciate the reviewer’s concern about the novelty of our approach and would like to clarify our contributions. Our goal is to take a step toward building a foundation model RDBs. We focus on three key challenges:\n\n1.\tHow to represent different RDBs in a unified model?\n\n2.\tHow to design a GNN architecture that works effectively for RDBs?\n\n3.\tHow to leverage abundant data, and when does it help?\n\nTo address these challenges, we propose **Griffin**, a unified framework with three main components: (1) encoder/decoder design, (2) GNN architecture updates, and (3) a pretraining pipeline. Then our experiments show consistent improvement through three stages:\n\n1.\tOur base model (even without pretraining) outperforms prior models (initially compared only with SAGE; we have now added 7 more baselines).\n\n2.\tPretraining on single-table data further boosts performance.\n\n3.\tAdditional SFT with similar or diverse RDBs enhances performance, especially in low-resource settings.\n\nThe strength of **Griffin** lies in the integration of these three components under a unified framework aimed at RDB foundation modeling. Each component plays a distinct and necessary role: enabling diverse data usage, improving learning capacity, and supporting practical deployment. Our experiments confirm that each part contributes meaningfully to both performance and transferability.\n\n---\n\n**Claim 2: Discussion of Related Work**\n\nWe thank the reviewer for highlighting missing related work. We have now revised the related work section to include a wider range of relevant literature. The updated can be refered to reply to Reviewer 95m6.\n\n\n---\n\n**Evaluation Concern: Missing Baselines and Alignment with RelBench**\n\nThank you for raising this important point. While we initially prioritized “fair” comparisons, we agree that broader baseline coverage is necessary for a more complete evaluation. In response, we have expanded our baseline set to include:\n\n•\t**Three additional GNN-based baselines**: GAT, PNA, and HGT, as suggested in 4DBInfer.\n\n•\t**Four single-table baselines**: MLP, DeepFM, Feature Transformer, and XGBoost, along with **Deep Feature Synthesis (DFS)**—a strong feature synthesis method that converts multi-table data into a single table by computing meaningful feature combinations.\n\n•\t**Two single-table baselines for few-shot settings**: We include TabPFN and TabLLM as representative few-shot baselines, both of which leverage pretrained models. TabPFN is trained from scratch, while TabLLM uses LLM-based pretraining. Although DFS can be computationally intensive (taking up to 7 hours for optimized pipelines in 4DBInfer and even longer using the original Featuretools implementation), making it less suitable for low-resource few-shot settings, we still include these baselines as a reference.\n\nFor evaluation, we continue to present normalized scores in the main figures to support comparison across tasks with varying scales. To ensure transparency and enable alignment with RelBench, we also provide some unnormalized results which is aligned with normalized scores, both suggesting Griffin outperforms Sage.\n\n---\n\n**W1: Method Description and Figures**\n\nWe thank the reviewer for this helpful feedback. We have made several improvements including Clarified terminology and Improved explanation of subgraph sampling and method pipeline.\n\n---\n\n**W2: Explanation of Results**\n\nThank you for the suggestion to provide more explanation of the results. In general, Griffin performs better when the task benefits from rich table metadata and high-quality text feature embeddings.\n\nHowever, there are still cases where it is difficult to consistently explain why Griffin outperforms SAGE, or vice versa. This challenge also applies to other strong GNN baselines such as GAT, PNA, and HGT. Despite being more expressive in theory, these models sometimes perform significantly worse than SAGE. We believe this may be due to the complexity and variability in data distributions, where simpler models like SAGE may align better with the task’s inductive bias in certain cases.\n\nWe acknowledge this as an open question and an area for future work. We welcome further insights and suggestions on how to better understand and interpret these variations.", "claims_and_evidence": "My major concerns of this paper are (1) the novelty of the proposed approach and (2) the insufficient discussion and comparison with other related works. For the novelty, the major contribution claimed was the introduction of a unified task decoder that eliminates the need for different prediction heads for different tasks. However, this idea seems to be not very novel or even needed. Instead of using the prediction heads for different tasks, Griffin still needs an MLP to be trained by sampling \"x\". It seems like simply merging all different prediction heads into one head. I'd suggest the authors to clarify the novelty of this paper. \n\nFor the discussion of related works, see section \"Methods And Evaluation Criteria\".", "methods_and_evaluation_criteria": "In the experiment section, the authors focuses on the comparison with only one baseline approach SAGE. I understand that the authors may want to do a \"fair\" comparison among different method. However, it is still very necessary to include other recent baselines into the evaluation, such as the ones reported in 4DBInfer or whichever performs well on the reported benchmarks (Figure 2). The authors mentioned that they did some modification due to the normalization etc. IMO, this should not be a reason not to include more baseline result. From the Appendix b.2, the results of Griffin can be transformed back so I did not see a challenge there.", "theoretical_claims": "Not applicable.", "experimental_designs_or_analyses": "See \"Methods And Evaluation Criteria\".", "relation_to_broader_scientific_literature": "This paper introdcues some new ideas to the area but like I mentioned before, I think the novelty of this paper is limited.", "essential_references_not_discussed": "There are multiple methods not mentioned in this paper, which are very relavent to this domain. For example, this paper is also similar to those that talks about embedding over relational table (eg. https://arxiv.org/pdf/1909.01120). Some other LLM methods for tabular questions are also worth discussing: eg. https://arxiv.org/abs/2004.02349  https://arxiv.org/pdf/2107.07653  https://arxiv.org/pdf/2207.03637", "other_strengths_and_weaknesses": "W1: I think some part of this paper is not well-written and handwavy. E.g. for appendix B.1, I think the authors want to talk about the experimental setup difference between the methods in 4DBInfer RelBench and this paper. But is it confusing to say comprision between a benchmark with a model (griffin). E.g. Figure 1 shows a subgraph sampling phase but seems this phase is not described in the methodology section. Also figure 2 also shows a very good running example. I'd suggest the authors use this example in Section 3 while you explain your workflow. \n\nW2: Lack explaination of the results. In figure 2, it is intersting to show that on task \"Retailrocket/cvr\", Griffin is significantly better than SAGE. But for other benchmarks, the gap is much smaller, even on some benchmarks Griffin is worse. These results are interesting and need some explaination.", "other_comments_or_suggestions": "See above", "questions_for_authors": "Could you please clarify the concerns that I have in section \"Claims And Evidence\" and \"Methods And Evaluation Criteria\"?", "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": "fPD1ibNA5o", "reviewer": "Reviewer_w5kU", "summary": "The proposes Griffin, a pretrained model for relational databases. Griffin uses concepts of unified representation of inputs and tasks, cross-attention mechanisms, and graph neural networks (or MPNNs), tasks of cell completion and supervised learning for pretraining. The proposed framework was tested on several datasets of relational database and shows competitiveness compared to baselines.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer w5kU for the careful reading and detailed feedback. We address the concerns and questions below. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Rebuttal\n\n---\n\n**Related Work**\n\nThank you for the suggestions. We have included those references and also in comparison. The updated can be refered to reply to Reviewer 95m6.\n\n---\n\n**C1: What does Griffin stand for?**\n\nGriffin stands for towards a **G**raph-centric Relat**I**onal dataset **F**oundat**I**o**N** model.\n\n---\n\n**C2: Clarifying “Foundation Model” Usage**\n\nWe appreciate your point. While Griffin is trained on specific datasets, we consider it to follow the general trajectory of foundation models [1] —namely, training on broad data for wide applicability. That said, we agree the claim should be stated with more care. We now consistently refer to Griffin as “**towards** a foundation model” or as“ a foundation model **attemptation**”. All inconsistent uses have been revised accordingly.\n\n[1] On the Opportunities and Risks of Foundation Models\n\n---\n\n**C3: Clarifying Data Curation Efforts**\n\nThank you for raising this. The data curation involved collecting, filtering, and improving dataset quality. We started with public datasets from TPBERTa, TabLLM, UniTabE, TableGPT, TaBERT, TabNet, CARTE, and others from HuggingFace. However, many were unsuitable—some tables had very few rows, others were derived from RDBs but had lost context when flattened, and some were noisy or irrelevant (e.g., meaningless string columns without clear semantics).\n\nWe ultimately curated ~200 datasets with over 10M rows. These were selected for having rich metadata and column features not heavily preprocessed (e.g., not just hashed values). We also generated natural language task descriptions using GPT4 to improve the training signals for tasks.\n\n---\n\n**C4 & C13: Ambiguous Adjectives**\n\nWe appreciate this feedback and have revised the paper to remove or clarify ambiguous adjectives. Specifically:\n\n•\tThe word **“seamless”** was used to describe the unified modeling of both single-table and RDBs as graphs using a consistent node structure.\n\n•\tThe term **“stable”** referred to the aggregation mechanism in our heterogeneous GNN, where neighbors are first aggregated within each relation type, followed by max aggregation across types to focus on the most relevant relations. Ablations also verify the effectiveness.\n\nFor them, we have now replaced the adjective with a direct explanation of the method.\n\n---\n\n**C5 & C6: Clarifying Section 3.1 and Decoder Design**\n\nWe revised Section 3.1 to clarify the notion of **“Task Unification”**. Previously, it refers not only to regression/classification unification but also to input unification. Now we split the subsection into two subparts: one describing encoder design and one describing decoder design. For classification tasks, the decoder retrieves the most similar label embedding. These embeddings are aligned with both category and textual feature encodings. For regression tasks, a numerical decoder predicts normalized values.\n\n---\n\n**C7 & C8: Numerical Feature Encoder**\n\nThe ENC module is a 3-layer MLP with SiLU activations (replacing ReLU) and layer normalization. Our current pretraining task about ENC and DEC focuses on accurate numerical recovery, for which this design is sufficient. We included this paper in related work and leave further exploration on numbers to future work.\n\n---\n\n**C9: Masking Strategy for Completion Task**\n\nWe use a simple zero-filling strategy, consistent with the settings used in 4DBInfer and RelBench. Zero is also used as the placeholder during prediction.\n\n---\n\n**C10–C11 & C14–C17: Pretraining Details**\n\nThank you for your careful reading. We now provide a detailed breakdown of the training regimes used in our experiments:\n\n| **Method** | **Completion-pretrain-single** | **Joint-SFT-single** | **Joint-SFT-RDB** | **Finetune** |\n| --- | --- | --- | --- | --- |\n| **Data volume** | ~10M rows | ~1M rows | ~150M total (domain-specific subset) | Task-specific |\n| **Training steps** | ~12k (batch size 4096) | ~6k (batch size 4096) | Domain-dependent | Task-dependent |\n| **Regularization** | L2 (9e-3) | L2 (2e-5) | L2 (2e-4) | L2 (2e-4) |\n\n\nTo improve clarity in the presentation of our experiments, we have renamed the models as follows:\n\n•\t**Griffin-unpretrained**: trained from scratch, no pretraining\n\n•\t**Griffin-pretrained**: pretrained on single-table data (completion + joint SFT)\n\n•\t**Griffin-RDB-SFT**: further pretrained with RDB-based joint SFT\n\n\n---\n\n**C12: DFS + X Baselines**\n\nWe have added these baselines in our updated experiments. A summary of the updated experimental setup is provided in our response to Reviewer zrC4 under Evaluation Concerns.", "claims_and_evidence": "Please refer to the comments or suggestions.", "methods_and_evaluation_criteria": "Please refer to the comments or suggestions.", "theoretical_claims": "Please refer to the comments or suggestions.", "experimental_designs_or_analyses": "Please refer to the comments or suggestions.", "relation_to_broader_scientific_literature": "Relational tabular data are widespread and some parts of the proposed methods can be extended to domain specific problems. Moreover, some techniques (e.g., cell completion) can be extended and used for pretrained neural network models for tabular data in general.", "essential_references_not_discussed": "- TabPFN models in foundation models for tabular data.\n- Some works on relational data (rdf2vec, EmbDI).", "other_strengths_and_weaknesses": "Please refer to the comments or suggestions.", "other_comments_or_suggestions": "- What does Griffin stand for?\n- I am uncertain as Griffin can be considered as a \"foundation model\", as it is trained on specific sets of data. In this sense, the word \"towards\"  in the title to me seems more acceptable and the claim (in introduction) that Griffin is a foundation model designed for RDBs seems debatable.\n- The sentence \"curated a diverse and extensive collection of datasets for both single-table and RDB tasks\" seems to obscure. What are some of the groundwork (good efforts) that have been put to curate these data?\n- Throughout the paper, the paper uses many adverbs or adjectives that are unclear. For instance, in Figure 1, what does \"seamless\" mean? There are several cases in the paper that use such words without much justification.\n- The term (or the title \"Task Unification\" (3.1)) might be misleading. The term task could simply denote regression or classification, while the subsection also deals with unification of the input representation. A clarification would help the understanding.\n- I am a bit confused with the part \"Categorical and Textual Feature\" with the paragraph starting with \"For classfication tasks...\". What is this supposed to describe?\n- For numerical features, have the authors considered numerical embeddings in RealMLP or followups of Gorishniy et al., 2021?\n- What is the structure of ENC (the numerical encoder) in equation 2?\n- How does Griffin mask for the completion task? Does it have a special embedding for this mask?\n- What is the number of epochs (or number of steps) for the pretraining (especially for the mask completion)?\n- Since there is no mention in the number of epochs (or steps), except for employing early stopping at 10, I worry about the overfit of the pretrain of mask completion since it is based on similarity of vectors (eq. 6), and there seems to be no regularization on this (as far as the current content of the paper).\n- It would be good to see comparison with simple baselines, such as DFS + (GBDT or Linear models).\n- (Paragraph Message-Passing Neural Network) What defines a more stable model? Or rather what is less stable model?\n- From my understanding I think there is no pretraining involved. If this is the case, it might be good to state this (hinting that it is to test the architectural choices of Griffin).\n- In Figure 2, it would be really helpful to have an arrow in the metrics to point the direction of better performing. Also, the terms Griffin-Pretrain and Griffin is confusing since I feel Griffin itself is a framework that includes the pretraining. Possibly, without pretrain could be better for understanding.", "questions_for_authors": "Please refer to the comments or suggestions.", "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": "coAknNmXaQ", "reviewer": "Reviewer_yHkP", "summary": "The paper introduces Griffin, the first graph-centric foundation model designed specifically for relational databases. Griffin combines advanced architectural innovations such as unified encoders for categorical and numerical features, cross-attention modules for selective information aggregation, and enhanced hierarchical message-passing neural networks. Pretrained on extensive single-table and multi-table datasets, Griffin demonstrates superior or comparable performance to task-specific models, excels particularly in low-data scenarios, and achieves strong transferability across various RDB tasks.\n\n## No score updates after rebuttal\nThe comparison with single-table methods using single table input is critical from my point of view, but the rebuttal has not provided statistical results.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer yHkP for acknowledging the contributions of our work and for the constructive questions and suggestions. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Rebuttal. We address the key concerns below:\n\n---\n\n**Q1: Performance Comparison with Single-Table Baselines**\n\nGriffin is primarily designed for RDB tasks and is not optimized to outperform state-of-the-art models on purely single-table datasets. However, to better understand its generalizability—and in response to suggestions from other reviewers—we conducted two additional sets of experiments:\n\n1.\t**DFS + Single-Table Models on RDB Tasks**:\n\nWe employed **Deep Feature Synthesis (DFS)** [1], which converts multi-table RDBs into single-table formats by aggregating features. This allows single-table models to be applied to RDB tasks. Building on pipelines from 4DBInfer, we evaluated advanced single-table baselines (including MLP, DeepFM, Feature Transformer, and XGBoost) on RelBench using DFS. Results indicate that even strong single-table models struggle to match Griffin’s performance, particularly on tasks where the multi-table structure plays a critical role.\n\n\n2.\t**Few-Shot Settings with Pretrained Single-Table Models**:\n\nTo evaluate performance in few-shot scenarios, we introduced two representative baselines: **TabPFN** (trained from scratch) and **TabLLM** (leveraging LLM-based pretraining). Both are designed for few-shot tabular tasks. Griffin still achieves higher average performance under this setting, demonstrating its generalization ability and potential as a foundation model for RDBs.\n\n[1] Deep Feature Synthesis: Towards Automating Data Science Endeavors https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf\n\n---\n\n**Q2: Performance Trends with Varying Numbers of Tables**\n\nThank you for the insightful question. Griffin’s framework integrates innovations across encoder/decoder design, GNN architecture, and pretraining strategies—each contributing to improved performance under different structural complexities. Our ablation studies isolate the effect of these components, including cross-attention mechanisms and hierarchical aggregation functions.\n\nRegarding the trend with varying numbers of tables, we do not yet have a fully consistent explanation for all scenarios where Griffin outperforms simpler models. One intuitive observation is that Griffin performs especially well in tasks that involve **high-quality textual features**—for example, predicting review ratings in e-commerce datasets where review content is informative.\n\nThat said, there are still cases where Griffin and simpler models (e.g., SAGE) exhibit unexpected performance patterns. Interestingly, even more expressive GNN models such as GAT, PNA, and HGT occasionally perform worse than SAGE. We believe this may stem from data-specific factors, including noisy features, varying relation types, or mismatched inductive biases—cases where simpler architectures might generalize better.\n\nWe acknowledge this as an open research question and welcome further discussion on better understanding the relationship between RDB schema complexity and model performance.", "claims_and_evidence": "The claims presented in the paper are supported by extensive experimental evidence, clearly demonstrating the model's efficacy in handling relational databases. Griffin’s generalization capabilities are well-substantiated through comprehensive experiments involving benchmarks such as 4DBInfer and RelBench. However, an area that warrants further exploration is whether the method effectively generalizes to single-table datasets, especially compared to established single-table baselines.", "methods_and_evaluation_criteria": "The proposed methods, including unified feature encoding, cross-attention mechanisms, and hierarchical message-passing neural networks, are highly relevant and thoughtfully designed for the targeted relational database applications. The evaluation criteria using existing graph-centric benchmarks (4DBInfer and RelBench) are appropriate and rigorous, making sense for the context of relational data.", "theoretical_claims": "The paper does not primarily focus on theoretical claims or proofs; therefore, no correctness of proofs needed verification.", "experimental_designs_or_analyses": "The experimental designs and analyses appear sound and thorough. The paper includes detailed comparisons across multiple metrics (Accuracy, ROC-AUC, RMSE, MAE, and Logloss). One limitation, however, is the restricted baseline comparison primarily against SAGE. Including comparisons with additional baseline methods tailored for single-table data would strengthen the analysis.", "relation_to_broader_scientific_literature": "The integration of advanced encoding and decoding strategies from related literature on tabular and graph data enriches the paper’s contributions.", "essential_references_not_discussed": "-", "other_strengths_and_weaknesses": "Strengths:\n\nMethodological innovations for relational database-specific foundation model.\n\nExtensive empirical analysis, including comprehensive ablation studies and transferability experiments.\n\nWeaknesses:\n\nPotential for further clarification on generalizability specifically in single-table scenarios.", "other_comments_or_suggestions": "-", "questions_for_authors": "How does Griffin perform specifically in single-table settings compared to state-of-the-art single-table models (e.g., XGBoost, TabNet, TabLLM, TableGPT)? Clarifying this would enhance understanding of its generalizability.\n\nWhat is Griffin’s performance trend with varying numbers of tables in relational datasets? Is there a threshold where Griffin clearly outperforms simpler methods?", "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": "hSmpnOyoUy", "reviewer": "Reviewer_XrAk", "summary": "This paper proposes Griffin, a foundation model specifically designed for Relational Databases (RDBs), which leverages graph neural networks (GNNs) to unify the processing of diverse RDB tasks. Experiments demonstrate that Griffin exhibits superior or comparable performance across multiple benchmarks, especially in low-data scenarios.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer XrAk for the thoughtful and constructive feedback. We address your concerns in detail below. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Rebuttal\n\n---\n\n**Claim 1: Clarifying the Challenges in Developing Foundation Models for RDBs**\n\nThank you for pointing this out. We agree that the challenges could be stated more clearly. We now explicitly outline the key challenges in developing a foundation model for relational databases:\n\n1.\t**Data and task diversity**: RDBs contain various data types (numerical, categorical, textual) and support diverse tasks, including regression and multi-class classification. A unified model must handle all these variations under a single framework.\n\n2.\t**Lack of RDB-specific GNNs**: Existing GNN architectures are not tailored for relational databases and often fail to leverage the rich metadata (e.g., table schema, column descriptions, task semantics) that is critical in RDBs.\n\n3.\t**Missing pretraining pipeline and transferability analysis**: It is not yet clear how pretraining can benefit relational data, nor what kinds of tasks or data lead to effective transferability across RDBs.\n\nThese challenges directly motivate the three components of Griffin: unified encoder/decoder design, GNN architecture enhancements, and a new pretraining pipeline. We have now added a dedicated subsection to the paper to clearly articulate these challenges and align them with our proposed solutions.\n\n---\n\n**Experiment: Including More Baselines & W3: Ablations and Experimental Focus**\n\nThank you for raising this point. While our primary goal is to present a unified foundation model framework for RDBs, we agree that including broader baselines and detailed ablation studies is essential for a comprehensive evaluation.\n\nTo that end, we have expanded our experimental comparisons to include three additional categories of baselines:\n\n• **Three additional GNN-based baselines**: GAT, PNA, and HGT, as suggested in 4DBInfer.\n\n• **Four single-table baselines**: MLP, DeepFM, Feature Transformer, and XGBoost, along with **Deep Feature Synthesis (DFS)**—a strong feature synthesis method that converts multi-table data into a single table by computing meaningful feature combinations.\n\n• **Two single-table baselines for few-shot settings**: We include TabPFN and TabLLM as representative few-shot baselines, both of which leverage pretrained models. TabPFN is trained from scratch, while TabLLM uses LLM-based pretraining. \n\nWe have also reorganized the experimental section to make the ablation studies more prominent. These ablations highlight the contributions of key architectural choices—specifically, the **cross-attention mechanism** and **hierarchical aggregation**—to the overall performance of the model.\n\n---\n\n**W1: Organization and Presentation**\n\nThanks for your suggestions. We have revised the introduction to better connect the discussion of challenges with our method overview. \n\n---\n\n**W2 & Q1: Justification for RDB-Specific Foundation Models**\n\nWe appreciate this important question. While single-table models can be combined with DFS to simulate multi-table input, they inherently lack the ability to capture the complex relational structure that is central to RDBs. This limitation has been highlighted in prior benchmarks such as 4DBInfer, where structure-aware models (e.g., GNN-based) consistently outperform single-table methods with DFS.\n\nTo further support this, we have added experimental comparisons with strong single-table baselines, including XGBoost, DeepFM, TabPFN, and TabLLM (all paired with DFS). The results reinforce the need for RDB-specific model designs like Griffin.\n\n---\n\n**Comments and Q2: Handling Temporal Data**\n\nThank you for this valuable observation. Our approach to handling temporal information follows the setup used in 4DBInfer, where subgraphs are constructed such that all neighboring nodes have timestamps earlier than the target node. This ensures that the model only uses past information, aligning with the temporal nature of real-world relational data.\n\nTo improve clarity, we have **expanded the explanation** in the preliminaries and **reorganized the method section** to better highlight how temporal constraints are applied during subgraph construction.\n\nAdditionally, we have revised the figures and included more baselines to improve presentation.\n\nWe appreciate your feedback on presentation—it has significantly improved the clarity and accessibility of our results.", "claims_and_evidence": "Partially. The authors do not provide a clear description of  the challenges in developing the foundation models for RDBs.", "methods_and_evaluation_criteria": "Partially. The authors highlight that Griffin is capable of handling temporal heterogeneous graphs, a feature that distinguishes it from traditional Message-Passing Neural Networks (MPNNs), as the latter does not inherently support temporal data processing.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "While the experiments comprehensively evaluate Griffin's performance across diverse tasks and datasets, the study primarily focuses on validating the proposed model itself. Notably, the absence of comparative analyses with existing models or methods limits the understanding of how Griffin's capabilities measure against alternative approaches in solving RDB-related problems.", "relation_to_broader_scientific_literature": "The Griffin is built upon the Graph Foundation Models (GFMs) and the Tabular Foundation Models (TFMs) and extend them to the RDB-related problems.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths:\n\n1. The paper presents a foundation model specifically designed for RDB-related problems, effectively addressing a significant gap in the field.\n\n2. Comprehensive experimental results demonstrate the model's robust and comparable performance across multiple RDB-related tasks, showcasing its versatility.\n\nWeakness:\n\n1. The paper's organization and presentation could be improved for better clarity. For example, Section 1 Paragraph 4 introduces the first challenge, while the subsequent paragraph (Paragraph 5) shifts to discussing Griffin's advantages, creating a disjointed flow that may confuse readers.\n\n2. The paper lacks a clear justification for the necessity of developing a specialized foundation model for RDB-related tasks. It does not adequately address whether existing frameworks, such as those based on large language models, could achieve similar or better results.\n\n3. The experimental evaluation is insufficient. The paper fails to compare Griffin's performance against the state-of-the-art (SOTA) methods for RDB-related tasks, relying instead on a single baseline (Sage) that does not represent SOTA. Additionally, the authors do not provide ablation studies or analyses to demonstrate the contribution and necessity of each component in the proposed model framework.", "other_comments_or_suggestions": "In Section 1, Paragraph 2, the connection between graph-based methods and RDB problems is not clearly articulated. Additionally, the visualization of main results in Figures 2 and 3 needs refinement. The current vertical labeling of the x-axis significantly hinders readability, making it challenging to interpret the data effectively. A more user-friendly design, such as horizontal x-axis labels, would greatly enhance the clarity and accessibility of these critical findings.", "questions_for_authors": "1. The authors should address the necessity of developing a foundation model specifically for RDB-related problems. It remains unclear whether existing models or frameworks could effectively solve these tasks. To strengthen the significance of this work, the authors should provide comparative results against commonly used frameworks, as the absence of such comparisons undermines the motivation and relevance of the proposed approach.\n\n2. The relationship between Griffin and temporal data requires further clarification. While the paper highlights Griffin's capability to handle heterogeneous RDB data, it lacks a detailed explanation of how the model processes temporal data. This omission creates ambiguity regarding the model's applicability to time-dependent RDB tasks, which is a critical aspect of real-world database systems.", "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": "QAP4AVoM09", "reviewer": "Reviewer_95m6", "summary": "This paper proposes Griffin, a unified model for Relational DataBase(RDB). Griffin has a classification prediction head and a regression prediction head to handle the classification and regression tasks, correspondingly. The classification head can handle arbitrary classes by comparing the inner product between the generated node representation and the text embeddings of the target categories. Components like cross-attention modules and unified task formats have been proposed to make this one model suitable for different scenarios. Griffin follows a pretrain-finetune paradigm. It first pretrains on a set of single tables and RDBs. On a specific task, it is then further finetuned on the provided data. Experiments show that Griffin, especially the pretrained one, performs better than the GraphSAGE baseline.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer 95m6 for acknowledging the contributions of our work and for the helpful suggestions and questions. We address each concern below. The updated experiments including 7 additional baselines about main experiment and 2 additional baselines on few-shot setting is provided at https://anonymous.4open.science/r/Griffin-Rebuttal\n\n---\n\n**Related Work**\nThank you for the recommended references. In response to your suggestions, along with those from other reviewers, we have revised the related work section to include a broader range of relevant literature. We have added new subsections covering embedding-based RDB models and Table QA approaches, as well as additional works on RDB-specific models and tabular foundation models.\n\nTo better reflect the landscape and clarify our positioning, we now organize the related work into the following categories:\n\t•\tSingle-table and RDB models for tabular predictive tasks\n\t•\tTable QA and SQL generation methods\n\t•\tTabular and graph foundation models, including both LLM-based models and those trained from scratch\n\n---\n\n**W1: Experimental Coverage and Baselines**\n\nThank you for your concern regarding the experimental baselines. We have significantly extended our experiments by including the following additional baseline categories:\n\n1.\t**Other GNN-based models** for RDBs, including **GAT**, **PNA**, and **HGT**.\n\n2.\t**Widely used single-table methods**, such as **XGBoost**, **MLP**, **DeepFM**, and **Feature Transformer**, applied to RDBs via **Deep Feature Synthesis (DFS)** to flatten the schema.\n\n3.\t**Tabular foundation models**, including **TabPFN** and **TabLLM**, also used with DFS.\n\nThese baselines are now incorporated into the experimental section, reinforcing its effectiveness across RDB-related tasks.\n\n---\n\n**W2 & Q1: Design Choices and Ablation Studies**\n\nThank you for raising questions about the justification of specific design choices. We have now addressed these through targeted ablation studies and reorganized the section to improve readability. Specifically:\n\n•\t**Cross-Attention vs. Mean Pooling**: We compare our cross-attention mechanism with simple mean-pooling. The results show improvements, supporting the benefit of selective attention over equal-weighted aggregation.\n\n•\t**Max vs. Mean Aggregation**: We evaluate different aggregation methods over heterogeneous relations. Max aggregation helps highlight the most informative relation types, and the ablation supports this design choice.\n\n•\t**Numeric Encoder/Decoder Pretraining**: Our design choice is based on the goal of enabling transferability across tasks. To ensure that the model outputs remain in a consistent numerical space, we use a fixed numeric encoder and decoder during training. Since our primary focus is not on improving single-task performance but rather on transferability, we did not include ablation studies on single-task performance for this component. That said, we recognize the importance of this question and are planning a more comprehensive ablation study in future work—starting from re-pretraining the model to investigate the impact of this design on cross-task generalization.\n\n---\n\n**Q2 & Q3: Handling Missing Values**\n\nWe appreciate the questions on how missing values are handled, both in the target node and in neighboring nodes:\n\n•\t**For the target node’s missing column (label)**: We mask it using a **zero placeholder** during pretraining.\n\n•\t**For other missing values**:\n\n•\t**Categorical/Text features** are replaced with a \"None\" token.\n\n•\t**Numeric features** are replaced with the **column-wise mean**.\n\nThis strategy generalizes to cases where **multiple features or nodes have missing values**, and aligns with common practices in both tabular learning and graph-based models. We have clarified this in the methodology section.", "claims_and_evidence": "NA", "methods_and_evaluation_criteria": "- The proposed method is properly evaluated on the benchmark datasets.\n- The significance of the proposed Griffin needs to be further validated. Standard deviation needs to be included to verify the significance.", "theoretical_claims": "NA", "experimental_designs_or_analyses": "- For the single-table tasks, more baselines should be included for a comprehensive comparison, like XgBoost, LightGBM and TabPFN[1].\n\n[1] TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter", "relation_to_broader_scientific_literature": "NA", "essential_references_not_discussed": "Past work:\n\nRelational Deep Learning: Graph Representation Learning on Relational Databases. Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec\n\nContemporary work:\n\nRelGNN: Composite Message Passing for Relational Deep Learning. Tianlang Chen, Charilaos Kanatsoulis, Jure Leskovec\n\nContextGNN: Beyond Two-Tower Recommendation Systems. Yiwen Yuan, Zecheng Zhang, Xinwei He, Akihiro Nitta, Weihua Hu, Dong Wang, Manan Shah, Shenyang Huang, Blaž Stojanovič, Alan Krumholz, Jan Eric Lenssen, Jure Leskovec, Matthias Fey", "other_strengths_and_weaknesses": "Strength:\n\n- The task over RDB is a practical problem and has a great impact on the real-world applications.\n\n- The writing of the paper is clear and easy to understand.\n\n- The tailored design of Griffin makes it able to perform arbitrary classification tasks and regression tasks in a single model.\n\nWeakness:\n\n- The experiment part is not comprehensive. There is only GraphSAGE as the baseline.\n\n- Several design choices are not well supported by evidence. For example, what is the advantage of the Cross Attention Module compared to just averaging the embedding? Similarly, during message-passing, why use Max aggregation rather than Mean (Attention) to aggregate messages from different edge types? Why the encoder/decoder for numeric features are trained seperately rather than jointly?", "other_comments_or_suggestions": "NA", "questions_for_authors": "- The encoder/decoder of the numerical features is trained alone, apart from the entire training pipeline. What is the benefit of this? There is no clear reason why it can't be trained end-to-end. To support the claim, an ablation study is needed to validate the design.\n\n- When encoding the target node, how do you generate the embedding of the missing column? I assume there will be a default missing embedding for the missing value.\n\n- What if there are missing values in multiple nodes that are not the target node?", "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:

Meta-review: The paper proposes a novel framework for pre-trained models on relational tabular data. While the original submission contained only limited related work, the authors provided extensive comparison to other models on relational data in the rebuttal, showing a clear advantage for the proposed method. However, the exact selection criteria for the datasets that were included are not provided, potentially raising a methodological concern.
Decision: Accept (poster)