Zero-shot learning techniques using expert-curated labels with embedding-based or generative models achieve macro-F1 scores comparable to fine-tuned transformer models for sentiment analysis in software engineering.
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Retrieval with frozen embeddings and k-NN delivers competitive accuracy, high data efficiency, and zero hallucinations on legal multi-label annotation across ECtHR and Eurlex datasets.
Solar-VLM fuses time-series, satellite imagery, and text encoders with graph attention across sites to improve PV power forecasting on real data from eight Chinese stations.
ARIA is a multimodal RAG framework that filters domain-specific questions with 97.5% accuracy and outperforms ChatGPT-5 on pedagogical quality for a university civil engineering course.
CodePori is a multi-agent LLM system for code generation whose participant evaluation identifies practical challenges like memory limits and hallucinations missed by binary benchmarks.
citing papers explorer
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Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?
Zero-shot learning techniques using expert-curated labels with embedding-based or generative models achieve macro-F1 scores comparable to fine-tuned transformer models for sentiment analysis in software engineering.
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Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
Retrieval with frozen embeddings and k-NN delivers competitive accuracy, high data efficiency, and zero hallucinations on legal multi-label annotation across ECtHR and Eurlex datasets.
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Solar-VLM: Multimodal Vision-Language Models for Augmented Solar Power Forecasting
Solar-VLM fuses time-series, satellite imagery, and text encoders with graph attention across sites to improve PV power forecasting on real data from eight Chinese stations.
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ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education
ARIA is a multimodal RAG framework that filters domain-specific questions with 97.5% accuracy and outperforms ChatGPT-5 on pedagogical quality for a university civil engineering course.
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CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
CodePori is a multi-agent LLM system for code generation whose participant evaluation identifies practical challenges like memory limits and hallucinations missed by binary benchmarks.