LLM-FS-Agent: A Deliberative Role-based Large Language Model Architecture for Transparent Feature Selection
- URL: http://arxiv.org/abs/2510.05935v1
- Date: Tue, 07 Oct 2025 13:46:06 GMT
- Title: LLM-FS-Agent: A Deliberative Role-based Large Language Model Architecture for Transparent Feature Selection
- Authors: Mohamed Bal-Ghaoui, Fayssal Sabri,
- Abstract summary: This paper introduces LLM-FS-Agent, a novel multi-agent architecture designed for interpretable and robust feature selection.<n>We evaluate LLM-FS-Agent in the cybersecurity domain using the CIC-DIAD 2024 IoT intrusion detection dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: High-dimensional data remains a pervasive challenge in machine learning, often undermining model interpretability and computational efficiency. While Large Language Models (LLMs) have shown promise for dimensionality reduction through feature selection, existing LLM-based approaches frequently lack structured reasoning and transparent justification for their decisions. This paper introduces LLM-FS-Agent, a novel multi-agent architecture designed for interpretable and robust feature selection. The system orchestrates a deliberative "debate" among multiple LLM agents, each assigned a specific role, enabling collective evaluation of feature relevance and generation of detailed justifications. We evaluate LLM-FS-Agent in the cybersecurity domain using the CIC-DIAD 2024 IoT intrusion detection dataset and compare its performance against strong baselines, including LLM-Select and traditional methods such as PCA. Experimental results demonstrate that LLM-FS-Agent consistently achieves superior or comparable classification performance while reducing downstream training time by an average of 46% (statistically significant improvement, p = 0.028 for XGBoost). These findings highlight that the proposed deliberative architecture enhances both decision transparency and computational efficiency, establishing LLM-FS-Agent as a practical and reliable solution for real-world applications.
Related papers
- A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models [85.30893355216486]
We study how visual token redundancy evolves with different dMLLM architectures and tasks.<n>Our study reveals that visual redundancy emerges only in from-scratch dMLLMs while handling long-answer tasks.<n>Layer-skipping is promising for accelerating AR-to-diffusion dMLLMs, whereas progressive or late-step pruning is more effective for from-scratch dMLLMs.
arXiv Detail & Related papers (2025-11-19T04:13:36Z) - Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey [69.45421620616486]
This work presents the first structured taxonomy and analysis of discrete tokenization methods designed for large language models (LLMs)<n>We categorize 8 representative VQ variants that span classical and modern paradigms and analyze their algorithmic principles, training dynamics, and integration challenges with LLM pipelines.<n>We identify key challenges including codebook collapse, unstable gradient estimation, and modality-specific encoding constraints.
arXiv Detail & Related papers (2025-07-21T10:52:14Z) - Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring [2.1205272468688574]
We propose a cognitive architecture for ML monitoring that applies feature engineering principles to agents based on Large Language Models.<n>Decision Procedure module simulates feature engineering through three key steps: Refactor, Break Down, and Compile.<n> Experiments using multiple LLMs demonstrate the efficacy of our approach, achieving significantly higher accuracy compared to various baselines.
arXiv Detail & Related papers (2025-06-11T13:48:25Z) - LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
arXiv Detail & Related papers (2025-02-15T02:55:22Z) - LLM-Powered Preference Elicitation in Combinatorial Assignment [17.367432304040662]
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in assignment.<n>We propose a framework for LLM proxies that can work in tandem with SOTA ML-powered preference elicitation schemes.<n>We experimentally evaluate the efficiency of LLM proxies against human queries in the well-studied course allocation domain.
arXiv Detail & Related papers (2025-02-14T17:12:20Z) - Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning [14.224921308101624]
We propose a novel Distribution-Aware Robust Learning framework (DaRL) for relevance modeling.<n>DaRL has been deployed online to serve the Alipay's insurance product search.
arXiv Detail & Related papers (2024-12-17T03:10:47Z) - Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making [85.24399869971236]
We aim to evaluate Large Language Models (LLMs) for embodied decision making.<n>Existing evaluations tend to rely solely on a final success rate.<n>We propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks.
arXiv Detail & Related papers (2024-10-09T17:59:00Z) - SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models [8.558834738072363]
Large language models (LLMs) have been widely adopted due to their remarkable performance across various applications.<n>These individual LLMs show limitations in generalization and performance on complex tasks due to inherent training biases, model size constraints, and the quality or diversity of pre-training datasets.<n>We introduce SelectLLM, which efficiently directs input queries to the most suitable subset of LLMs from a large pool.
arXiv Detail & Related papers (2024-08-16T06:11:21Z) - FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications.
FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
arXiv Detail & Related papers (2024-08-15T16:45:16Z) - FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large
Language Models in Federated Learning [70.38817963253034]
This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution.
We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios.
We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
arXiv Detail & Related papers (2023-09-01T09:40:36Z) - From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets.
Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
arXiv Detail & Related papers (2023-08-23T09:45:29Z)
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.