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LIPT: Improving Prompt Tuning with Late Inception Reparameterization

Prompt tuning is a mainstream technique for fine-tuning large language models (LLMs), offering minimal parameter adjustments by learning task-specific prompt vectors. However, it suffers from training costs due to network-wide backpropagation and weaker performance compared to methods like adapters and LoRA, likely due to the limited capacity of so...

Lottery Rank-Pruning Adaptation Parameter Efficient Fine-Tuning

Recent studies on parameter-efficient fine-tuning (PEFT) have introduced effective and efficient methods for fine-tuning large language models (LLMs) on downstream tasks using fewer parameters than required by full fine-tuning. Low-rank decomposition adaptation (LoRA) significantly reduces the parameter count to 0.03% of that in full fine-tuning, m...

Comparative Analysis of Prompt Strategies for Large Language Models: Single-Task vs. Multitask Prompts

This study investigates the effectiveness of prompt engineering strategies for Large Language Models (LLMs), comparing single-task and multitasking prompts. Specifically, we analyze whether a single prompt handling multiple tasks—such as named entity recognition (NER), sentiment analysis, and JSON output formatting—can achieve performance comparabl...

Multimodal Food Image Classification with Large Language Models

In this study, we leverage advancements in large language models (LLMs) for fine-grained food image classification. We achieve this by integrating textual features extracted from images using an LLM into a multimodal learning framework. Specifically, semantic textual descriptions generated by the LLM are encoded and combined with image features obt...

Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent ...

With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landing generation. However, how to achieve intelligent labeling consistency and accuracy and improve lab...

Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting it...

An historical overview of artificial intelligence for diagnosis of major depressive disorder

Published in Frontiers in Psychiatry

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing ...

Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, overwhelming engineers with a deluge of often correlated...

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production an...