Generative Data Transformation: From Mixed to Unified Data

• URL: http://arxiv.org/abs/2602.22743v1
• Date: Thu, 26 Feb 2026 08:30:09 GMT
• Title: Generative Data Transformation: From Mixed to Unified Data
• Authors: Jiaqing Zhang, Mingjia Yin, Hao Wang, Yuxin Tian, Yuyang Ye, Yawen Li, Wei Guo, Yong Liu, Enhong Chen,
• Abstract summary: textscTaesar is a emphdata-centric framework for textbftarget-textbfal textbfregeneration.<n>It encodes cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures.
• Score: 57.84692191369066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing \emph{model-centric} paradigm -- which relies on complex, customized architectures -- struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose \textsc{Taesar}, a \emph{data-centric} framework for \textbf{t}arget-\textbf{a}lign\textbf{e}d \textbf{s}equenti\textbf{a}l \textbf{r}egeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. It employs contrastive decoding to encode cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show \textsc{Taesar} outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, \textsc{Taesar} effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ \textcolor{blue}{https://github.com/USTC-StarTeam/Taesar}.

Related papers

• From IDs to Semantics: A Generative Framework for Cross-Domain Recommendation with Adaptive Semantic Tokenization [3.4546102059619526]
  Cross-domain recommendation is crucial for improving recommendation accuracy and generalization.<n>Many efforts have focused on learning disentangled representations through multi-domain joint training to bridge the domain gaps.<n>Recent Large Language Model (LLM)-based approaches show promise, they still face critical challenges.<n>We propose textbfGenCDR, a novel textbfGenerative textbfCross-textbfDomain textbfRecommendation framework.
  arXiv  Detail & Related papers  (2025-11-11T09:10:40Z)
• GRIP: A Graph-Based Reasoning Instruction Producer [47.80560026838563]
  We present textittextbfGRIP, a textbfGraph-based textbfReasoning textbfInstruction textbfProducer that efficiently synthesizes high-quality and diverse reasoning instructions.
  arXiv  Detail & Related papers  (2024-12-12T01:52:25Z)
• A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap [50.079224604394]
  We present a novel model-agnostic framework called textbfContext-textbfEnhanced textbfFeature textbfAment (CEFA) CEFA consists of a feature alignment module and a context enhancement module. Our method can serve as a plug-and-play module to improve the detection performance of HOI models on rare categories.
  arXiv  Detail & Related papers  (2024-07-31T08:42:48Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Review-Based Hyperbolic Cross-Domain Recommendation [3.4498722449655066]
  Cross-Domain Recommendation (CDR) captures domain-shareable knowledge and transfers it from a richer domain to a sparser one.<n>This paper advocates a hyperbolic CDR approach based on review texts for modeling user-item relationships.
  arXiv  Detail & Related papers  (2024-03-29T17:15:21Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning [112.69497636932955]
  Federated learning aims to train models across different clients without the sharing of data for privacy considerations. We study how data heterogeneity affects the representations of the globally aggregated models. We propose sc FedDecorr, a novel method that can effectively mitigate dimensional collapse in federated learning.
  arXiv  Detail & Related papers  (2022-10-01T09:04:17Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.