AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment

2025-10-09

AI Summary

Addressing the challenges of strong domain dependency, dynamic pattern evolution, and the difficulty of simultaneously achieving interpretability and cross-domain adaptability in online review quality assessment, this paper proposes AutoQual—a framework leveraging large language model (LLM)-based agents that emulate human research paradigms. AutoQual autonomously discovers explicit, computable, and interpretable features from implicit data knowledge via iterative hypothesis generation, self-directed tool invocation, and persistent memory mechanisms—eliminating manual feature engineering. It supports seamless cross-domain transfer and continuous adaptation to evolving data distributions. Deployed in an A/B test on a billion-user e-commerce platform, AutoQual significantly improved user experience: average review views per user increased by 0.79%, and the conversion rate of review readers rose by 0.27%. The framework advances automated, explainable, and scalable review quality modeling for real-world applications.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Auto ML and Hyperparameter Tuning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Ranking online reviews by their intrinsic quality is a critical task for e-commerce platforms and information services, impacting user experience and business outcomes. However, quality is a domain-dependent and dynamic concept, making its assessment a formidable challenge. Traditional methods relying on hand-crafted features are unscalable across domains and fail to adapt to evolving content patterns, while modern deep learning approaches often produce black-box models that lack interpretability and may prioritize semantics over quality. To address these challenges, we propose AutoQual, an LLM-based agent framework that automates the discovery of interpretable features. While demonstrated on review quality assessment, AutoQual is designed as a general framework for transforming tacit knowledge embedded in data into explicit, computable features. It mimics a human research process, iteratively generating feature hypotheses through reflection, operationalizing them via autonomous tool implementation, and accumulating experience in a persistent memory. We deploy our method on a large-scale online platform with a billion-level user base. Large-scale A/B testing confirms its effectiveness, increasing average reviews viewed per user by 0.79% and the conversion rate of review readers by 0.27%.

Problem

• Automating discovery of interpretable features for review quality assessment
• Addressing domain-dependence and dynamic nature of quality evaluation
• Overcoming limitations of handcrafted features and black-box models

Innovation

• LLM agent automates discovery of interpretable features
• Iteratively generates hypotheses via reflection and tool implementation
• Transforms tacit knowledge into explicit computable features