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%.
SportsGPT: An LLM-driven Framework for Interpretable Sports Motion Assessment and Training Guidance
School of Artificial Intelligence, UCAS, Beijing, China（人工智能学院）；MAIS, NLPR, Institute of Automation, CAS, Beijing, China（自动化研究所）；College of Education, Beijing Sport University, Beijing, China（北京体育大学）；KuTi Sports Technology, Shaanxi, China（KuTi体育科技）；Haoxiong Technology, Shanghai, China（浩鸿科技）
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice
Euler Institute, Università della Svizzera italiana（欧拉研究所，瑞士大学）；National Institute of Informatics（国家信息研究所）；Brown University（布朗大学）；University of Tübingen（图宾根大学）；Insubria University（因斯布里亚大学）
LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing
Department of Linguistics & Institute for Advanced Computational Science, Stony Brook University（语言学系与高级计算科学研究所，石溪大学）；Department of Linguistics, University of Saskatchewan（语言学系，萨斯喀彻温大学）
AlignCheck: a Semantic Open-Domain Metric for Factual Consistency Assessment
Institute of Computational Life Sciences, Department of Life Sciences and Facility Management, Zurich University of Applied Sciences（计算生命科学研究所，生命科学与设施管理系，苏黎世应用科学大学）；Swiss Institute of Bioinformatics（瑞士生物信息学研究所）
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
New York University（纽约大学）；NYU Langone Health（纽约大学兰格医学中心）；Genentech（基因泰克）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Wenzhou Medical University（温州医科大学）；Macau University of Science and Technology（澳门科学大学）
Interpretable, Fairly Evaluated Automated L2 Speaking Assessment that Beats the Single-Human Ceiling and Why Pause Encoding Does Not Change LLM Fluency Scores
Dynamic Summary Generation for Interpretable Multimodal Depression Detection
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Faculty of Engineering, University of the Ryukyus（琉球大学工学部）
Intelligent System for Automated Molecular Patent Infringement Assessment
DP Technology（深势科技）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Sun Yat-Sen University（中山大学）；AI for Science Institute, Beijing（北京科学智能研究院）；Peking University（北京大学）
Nagoya University（名古屋大学）；National University of Singapore（新加坡国立大学）
AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, instead of scoring subjectively, sports judges use a consistent set of criteria - rubric - on multiple actions in each performance sequence. Therefore, we propose IRIS to perform Interpretable Rubric-Informed Segmentation on action sequences for AQA. We investigated IRIS for scorin…
Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment
University of Sheffield（谢菲尔德大学）；Centre for Machine Intelligence (CMI)（机器智能中心）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）；Institute for in Silico Medicine (INSIGNEO)（计算机模拟医学研究所）
The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced …
TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Cairo University（开罗大学）
Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images
UNCPBA, CONICET, Yatiris Group, Pladema Institute（UNCPBA、CONICET、Yatiris集团、Pladema研究所）；Servicio de Oftalmología, Hospital de Alta Complejidad en Red ``El Cruce'' Dr. Néstor Carlos Kirchner（眼科服务，高级复杂性网络‘El Cruce’医院Dr. Néstor Carlos Kirchner）
National University of Singapore（新加坡国立大学）；University College London（伦敦大学学院）
Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This is not scalable to large crowds. We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations - Attribution, Contrastive Attribution, and Counterfactual Suggestions - to feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideations. We co…
The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the explanations provided by transparent ML methods are for humans remains difficult to assess. Most studies evaluate interpretability in qualitative comparisons, they use experimental paradigms that do not allow for direct comparisons amongst methods or they report only offline experiments with no humans in the loop. While there are clear advantages of evaluations with no humans in the loop, such as scalability, reprod…
University of Southampton（南安普顿大学）；University of Bristol（布里斯托大学）；The Alan Turing Institute（阿兰·图灵研究所）
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to capture temporal dependencies in human assessment of trajectories. We show how RM can be approached as a multiple instance learning (MIL) problem, where trajectories are treated as bags with return labels, and steps within the trajectories are instances with unseen reward labels. We go on t…
