# SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

arXiv: https://arxiv.org/abs/2501.16125 · HTML: https://mlsanity.com/paper/2501.16125/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Privacy-preserving tabular data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 29 of its 68 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Privacy-preserving tabular data synthesis](https://mlsanity.com/synth/privacy-preserving-tabular-data-synthesis/)

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://mlsanity.com/paper/2305.13731/) (2023)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications](https://mlsanity.com/paper/2310.18339/) (2023)
- [Explaining Explanations: Axiomatic Feature Interactions for Deep Networks](https://mlsanity.com/paper/2002.04138/) (2020)
- [Deep Reinforcement Learning for Page-wise Recommendations](https://mlsanity.com/paper/1805.02343/) (2018)
- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning](https://mlsanity.com/paper/1802.06501/) (2018)
- [LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://mlsanity.com/paper/2403.16512/) (2024)
- [NoteLLM-2: Multimodal Large Representation Models for Recommendation](https://mlsanity.com/paper/2405.16789/) (2024)
- [Improving performance of deep learning models with axiomatic attribution priors and expected gradients](https://mlsanity.com/paper/1906.10670/) (2019)
- [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022)
- [Jointly Learning to Recommend and Advertise](https://mlsanity.com/paper/2003.00097/) (2020)
- [Multi-Task Recommendations with Reinforcement Learning](https://mlsanity.com/paper/2302.03328/) (2023)
- [Large Language Model Distilling Medication Recommendation Model](https://mlsanity.com/paper/2402.02803/) (2024)
- [AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://mlsanity.com/paper/2303.06611/) (2023)
- [User Retention-oriented Recommendation with Decision Transformer](https://mlsanity.com/paper/2303.06347/) (2023)
- [ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems](https://mlsanity.com/paper/2403.12660/) (2024)
- [Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation](https://mlsanity.com/paper/2405.12892/) (2024)

## Built on by (most cited first, 4 of 4)

- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [Few-shot LLM Synthetic Data with Distribution Matching](https://mlsanity.com/paper/2502.08661/) (2025)
- [TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation](https://mlsanity.com/paper/2509.09685/) (2025)
- [LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training](https://mlsanity.com/paper/2511.19931/) (2025)

## Abstract (arXiv)

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in capturing complex distributions and understanding feature relationships from sparse and limited data, along with their inability to grasp semantic feature relations. Recently, Large Language Models (LLMs) have shown potential in generating synthetic data samples through few-shot learning and semantic understanding. However, they often suffer from inconsistent distribution and lack of diversity due to their inherent distribution disparity with the target dataset. To address these challenges and enhance tabular data synthesis for recommendation tasks, we propose a novel two-stage framework named SampleLLM to improve the quality of LLM-based tabular data synthesis for recommendations by ensuring better distribution alignment. In the first stage, SampleLLM employs LLMs with Chain-of-Thought prompts and diverse exemplars to generate data that closely aligns with the target dataset distribution, even when input samples are limited. The second stage uses an advanced feature attribution-based importance sampling method to refine feature relationships within the synthesized data, reducing any distribution biases introduced by the LLM. Experimental results on three recommendation datasets, two general datasets, and online deployment illustrate that SampleLLM significantly surpasses existing methods for recommendation tasks and holds promise for a broader range of tabular data scenarios.