Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming

• URL: http://arxiv.org/abs/2504.21304v1
• Date: Wed, 30 Apr 2025 04:26:03 GMT
• Title: Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming
• Authors: Nanxu Gong, Xinyuan Wang, Wangyang Ying, Haoyue Bai, Sixun Dong, Haifeng Chen, Yanjie Fu,
• Abstract summary: We propose a generator-critic duet-play teaming framework to transform feature spaces efficiently and without supervision.<n>The framework consists of three interconnected steps: (1) Critic agent diagnoses data to generate actionable advice, (2) Generator agent produces tokenized feature transformations guided by the critic's advice, and (3) Iterative refinement ensures continuous improvement through feedback between agents.<n>Extensive experiments demonstrate that the proposed framework outperforms even supervised baselines in feature transformation efficiency, robustness, and practical applicability across diverse datasets.
• Score: 37.06543502352577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature transformation involves generating a new set of features from the original dataset to enhance the data's utility. In certain domains like material performance screening, dimensionality is large and collecting labels is expensive and lengthy. It highly necessitates transforming feature spaces efficiently and without supervision to enhance data readiness and AI utility. However, existing methods fall short in efficient navigation of a vast space of feature combinations, and are mostly designed for supervised settings. To fill this gap, our unique perspective is to leverage a generator-critic duet-play teaming framework using LLM agents and in-context learning to derive pseudo-supervision from unsupervised data. The framework consists of three interconnected steps: (1) Critic agent diagnoses data to generate actionable advice, (2) Generator agent produces tokenized feature transformations guided by the critic's advice, and (3) Iterative refinement ensures continuous improvement through feedback between agents. The generator-critic framework can be generalized to human-agent collaborative generation, by replacing the critic agent with human experts. Extensive experiments demonstrate that the proposed framework outperforms even supervised baselines in feature transformation efficiency, robustness, and practical applicability across diverse datasets.

Related papers

• Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
  Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.<n>We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation [13.120930059424975]
  C-3PO is a proxy-centric framework that facilitates communication between retrievers and large language models. Our framework implements three specialized agents that collaboratively optimize the entire RAG pipeline.
  arXiv  Detail & Related papers  (2025-02-10T07:04:32Z)
• EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration [63.112790050749695]
  We introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-20T06:21:56Z)
• Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning [28.673952870674146]
  We develop a measurement-pretrain-finetune paradigm for Unsupervised Feature Transformation Learning. For unsupervised feature set utility measurement, we propose a feature value consistency preservation perspective. For generative transformation finetuning, we regard a feature set as a feature cross sequence and feature transformation as sequential generation.
  arXiv  Detail & Related papers  (2024-05-27T06:50:00Z)
• Learning to Use Tools via Cooperative and Interactive Agents [58.77710337157665]
  Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. We propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. Our experiments on three datasets show that the LLMs, when equipped with ConAgents, outperform baselines with substantial improvement.
  arXiv  Detail & Related papers  (2024-03-05T15:08:16Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection [116.21529970404653]
  We introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Our approach achieves competitive performance when compared to state-of-the-art HOI methods.
  arXiv  Detail & Related papers  (2023-11-03T07:25:57Z)
• Controllable Data Generation Via Iterative Data-Property Mutual Mappings [13.282793266390316]
  We propose a framework to enhance VAE-based data generators with property controllability and ensure disentanglement. The proposed framework is implemented on four VAE-based controllable generators to evaluate its performance on property error, disentanglement, generation quality, and training time.
  arXiv  Detail & Related papers  (2023-10-11T17:34:56Z)
• Feature Decoupling-Recycling Network for Fast Interactive Segmentation [79.22497777645806]
  Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input. We propose the Feature Decoupling-Recycling Network (FDRN), which decouples the modeling components based on their intrinsic discrepancies.
  arXiv  Detail & Related papers  (2023-08-07T12:26:34Z)
• Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective [33.45878576396101]
  Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework.
  arXiv  Detail & Related papers  (2023-06-29T12:29:21Z)
• Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification [9.874360118638918]
  We build a dual-branch network architecture based upon a modified Vision Transformer (ViT) Local tokens output in each branch are reshaped and then uniformly partitioned into multiple stripes to generate part-level features. Global tokens of two branches are averaged to produce a global feature.
  arXiv  Detail & Related papers  (2022-11-22T13:51:17Z)

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.