这是我们最新精选的以 "提示工程 "为主题的全球出版物和专利，涉及许多科学在线期刊，分类并侧重于提示工程、提示设计、情境化、提示可变性、超参数调整、偏差缓解、零点学习和少点学习。
Systems and methods for machine learning operations
Patent published on the 2026-06-04 in WO under Ref WO2026117696 by FIDELITY INFORMATION SERVICES LLC [US] (Ghosh Ranadhir [us], Kumar Gautam [us], Platais John [us])
Abstract: A computer-implemented method for automatically retraining a machine learning system, the method including: receiving a plurality of data objects, the plurality of data objects corresponding to information technology event data and representing an occurrence of an event; processing the plurality of data objects; evaluating whether to perform hyperparameter tuning of a first model of a first machine learning system based on characteristics of the plurality of data objects; training the first mode[...]
Our summary: The method automates the retraining of a machine learning system using IT event data. It processes data objects to evaluate the need for hyperparameter tuning. Finally, it trains and stores a retrained model based on the processed data.
machine learning, retraining, hyperparameter tuning, data processing
Patent
Input/output-component (io-component) for a control device for controlling a device or system
Patent published on the 2026-04-15 in EP under Ref EP4726483 by SIEMENS AG [DE] (Haneder Thomas [de], Lindemann Lars [de], Rost Julian [de])
Abstract: [0001] The present invention describes an input/output-component (IO-component) for a control device for controlling a device or system, wherein the IO-component is designed and set up - for input and/or output of IO control data into and/or out of the control device via a communication link using a communication protocol, - and for connection to a middleware component of the control device, and wherein the IO-component is designed and set up for converting IO control data received via the commu[...]
Our summary: The invention describes an IO-component for a control device that manages input and output of control data. It converts IO control data into middleware variables independent of the communication protocol. The conversion process includes normalization, standardization, logical structuring, and contextualization of the data.
IO-component, control device, communication protocol, middleware variables
Patent
A Method for 3D Building Individualization Integrating SAMPolyBuild and Multiple Spatial-Geometric Features
Published on 2026-02-03 by Lianshuai Cao, Yi Cheng, Zheng Zhang, Ge Zhu, Kunyang Ma, Xinyue Xu @MDPI
Abstract: Individualization of buildings is one of the key issues in the establishment of three-dimensional (3D) building models. Most existing individualization methods rely on inefficient manual separation, while deep learning approaches require extensive pre-training and are highly influenced by the spatial structure of the models. To address these issues, this paper proposes a novel method for 3D building individualization that integrates SAMPolyBuild with multiple spatial-geometric features. Leveragi[...]
Our summary: This paper presents a method for 3D building individualization that integrates SAMPolyBuild with spatial-geometric features. The approach utilizes zero-shot learning for initial extraction and refines accuracy through statistical parameters. Experiments show effective extraction of building models, achieving an F1-score of about 0.83.
3D Building Individualization, SAMPolyBuild, Spatial-Geometric Features, Zero-Shot Learning
Publication
Deep Reinforcement Learning-Driven Adaptive Prompting for Robust Medical LLM Evaluation
Published on 2026-02-02 by Dong Ding, Wang Xi, Zenghui Ding, Jianqing Gao @MDPI
Abstract: The accurate and reliable evaluation of large language models (LLMs) in medical domains is critical for real-world clinical deployment, automated medical reasoning, and patient safety. However, the evaluation process is highly sensitive to prompt design, and prevalent reliance on fixed or randomly sampled prompt policies often fails to dynamically adapt to clinical context, question complexity, or evolving safety requirements. This article presents a novel reinforcement learning-based framework [...]
Our summary: This article introduces a reinforcement learning-based framework for adaptive prompt selection in medical LLM evaluation. It formulates prompt selection as a Markov Decision Process and utilizes a deep Q-Network to enhance evaluation metrics. Experiments show consistent improvements in accuracy, safety, and medical terminology coverage across multiple datasets.
Reinforcement Learning, Prompt Optimization, Medical LLM, Evaluation Framework
Publication
Weak-to-Strong Honesty Alignment via Group-Relative Policy Optimization
Published on 2026-01-30 by Jie Zhang, Yunfan Xie, Wen Zou @MDPI
Abstract: Ensuring that Large Language Models align with human values of honesty is a critical challenge, particularly due to the scarcity of labeled data for distinguishing known versus unknown knowledge boundaries. We propose a weak-to-strong generalization framework utilizing Group Relative Policy Optimization (GRPO). Unlike standard supervised fine-tuning or prompt engineering, our framework trains a lightweight “honest head” to rank response candidates based on multifacete[...]
Our summary: We propose a weak-to-strong generalization framework for aligning large language models with human honesty values. Our method utilizes Group Relative Policy Optimization to train an "honest head" that ranks response candidates based on honesty scores. Experiments show significant performance improvements over existing methods in honesty alignment across various datasets.
Honesty Alignment, Group Relative Policy Optimization, Large Language Models, Self-labeling
Publication
Tuning for Precision Forecasting of Green Market Volatility Time Series
Published on 2026-01-29 by Sonia Benghiat, Salim Lahmiri @MDPI
Abstract: In recent years, the green financial market has been exhibiting heightened volatility daily, largely due to policy changes and economic shifts. To explore the broader potential of predictive modeling in the context of short-term volatility time series, this study analyzes how fine-tuning hyperparameters in predictive models is essential for improving short-term forecasts of market volatility, particularly within the rapidly evolving domain of green financial markets. While traditional econometri[...]
Our summary: This study analyzes the impact of hyperparameter tuning on predictive models for short-term volatility in green financial markets. It compares machine-learning and deep-learning approaches using various models on daily volatility data. Results show that deep-learning models with optimized hyperparameters significantly improve predictive accuracy.
Hyperparameter tuning, Predictive modeling, Green financial markets, Deep learning
Publication
Comparative Analysis and Optimisation of Machine Learning Models for Regression and Classification on Structured Tabular Datasets
Published on 2026-01-29 by Siegfried Fredrich Stumpfe, Sandile Charles Shongwe @MDPI
Abstract: This research entails comparative analysis and optimisation of machine learning models for regression and classification tasks on structured tabular datasets. The primary target audience for this analysis comprises researchers and practitioners working with structured tabular data. Common fields include biostatistics, insurance, and financial risk modelling, where computational efficiency and robust predictive performance are essential. Four machine learning techniques (i.e., linear/logistic reg[...]
Our summary: This research conducts a comparative analysis and optimization of machine learning models for regression and classification on structured tabular datasets. It evaluates four techniques across 72 datasets, revealing that dataset characteristics significantly influence model performance. The findings emphasize the robustness of linear models and the superior performance of non-linear models in complex environments.
Machine Learning, Regression, Classification, Tabular Datasets
Publication
A Statistics-Driven Framework with Environment-Adaptive Hyperparameter Tuning
Published on 2026-01-23 by Haowen Ge, Ying Li, Yuntao Mao, Jian Li, Ziwei Chen, Pengying Bai, Xueming Peng @MDPI
Abstract: Given the central importance of maritime logistics to global trade, accurate and efficient vessel motion forecasting is essential for strengthening supply chain resilience and improving operational efficiency. However, traditional physical and statistical models often fail to effectively capture the multivariate, noisy, and strongly coupled nature of maritime dynamics. In this manuscript, we adapt the DSformer architecture for ship motion forecasting, leveraging its dual sampling and dual-attent[...]
Our summary: This study adapts the DSformer architecture for vessel motion forecasting to improve operational efficiency in maritime logistics. It achieves a 23% reduction in prediction error and a 70% decrease in training time compared to existing models. The research highlights the relationship between sampling strategies and sea states, optimizing real-time forecasting for global supply chains.
Hyperparameter Tuning, Vessel Motion Forecasting, Maritime Logistics, DSformer Architecture
Publication
