A FLAN-T5 model combining retrieval augmentation and parameter-efficient fine-tuning achieved high-quality structured test-case generation with measurable latency costs.
Researchers propose an SGDFD framework that decomposes AI-generated content detection into semantic and forgery subspaces and splits the single fake ...
Researchers have unveiled MiRAGE, a parameter-efficient AI framework that adapts its computational depth to each satellite image and uses CLIP ...
A rigorously controlled study finds that adding smoothness regularization and chunk-aware auxiliary supervision to a compact vision-language-action model actually worsens ...
A new review finds that Bangla medical question answering lags far behind English systems due to dataset scarcity, translated corpora, ...
Researchers fine-tuned a Llama model with LoRA to extract agronomic triplets and build a GraphRAG-powered knowledge graph that outperforms standard ...
A new federated framework fine-tunes the Segment Anything Model for multi-organ segmentation by training only tiny low-rank adapters on quantized ...
Researchers have developed ValuePEFT, a parameter-efficient fine-tuning method that steers large language models toward explicitly specified human values with measurable ...
Researchers have introduced HGA-Net, a hypergraph-based adapter that connects related examples within each mini-batch and achieves near-perfect fine-grained recognition with ...
The advent of large language models, particularly in the field of biology, has transformed our understanding of complex systems at ...
