5 papers
SFMformer: A Spatial-Frequency Modulation Transformer for Lightweight Image Super-Resolution
Chih-Hsiang Yang, Chia-Min Lin, Ching-Yu Tsai +2
Sparse attention mechanisms, which score all token pairs but propagate only the strongest, now underpin the most efficient Transformers for lightweight image super-resolution. This…
MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26
Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan +4
State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of…
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen +4
Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficie…
Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement
Yu-Che Tsai, Kuan-Yu Chen, Yuan-Chi Li +3
Existing large language model (LLM)-based embeddings typically adopt an encoder-only paradigm, treating LLMs as static feature extractors and overlooking their core generative stre…
Neuron-Level Differentiation of Memorization and Generalization in Large Language Models
Ko-Wei Huang, Yi-Fu Fu, Ching-Yu Tsai +8
We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neur…