Tag: AI fine-tuning
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Optimizing LLMs for Polymer-Composite Additive Manufacturing
Explore how retrieval-augmented generation boosts large language models for polymer-composite additive manufacturing accuracy and relevance.
Reducing Hallucinations in Vision-Language Models with Geometric Debiasing
Discover how geometric debiasing and over-alignment reduction improve accuracy and reduce hallucinations in vision-language models without extra training.
FreqAdapter: Efficient Text-Guided Multi-Scale Fine-Tuning
Discover FreqAdapter, a novel text-guided multi-scale frequency fine-tuning method that boosts AI model adaptability and efficiency in one training epoch.
CERSA: Memory-Efficient Fine-Tuning for Large AI Models
Discover CERSA, a novel method for memory-efficient fine-tuning that boosts performance in large AI models using energy-retaining subspace adaptation.
Emergent Semantic Role Understanding in Language Models
Explore how language models develop semantic role understanding during pre-training and fine-tuning for improved NLP performance.