Support Vector Generation is introduced, a kernel-based framework that converts a frozen language model into an interpretable, training-free classifier for zero-and few-shot learning and suggests that SVG offers a viable path toward efficient, interpretable NLP systems under compute constraints.
B1ade, an efficient RAG architecture comprising two purpose-built components: a compact embedding model and a purpose-built SLM shows that strategic model composition and reward design suffice for resource-efficient RAG, without large-scale pretraining.
S. Subramanian, M. Gungor, Vikram Elango· arXiv.org· 1 citation
This work introduces Giga-Embeddings, a family of text embedding models designed to combine strong retrieval quality with efficient serving, and trains the compact model using a dimension-agnostic objective that aligns teacher and student similarity distributions.
Egor Kolodin, Egor Krasnoperov, Evgeniy Kosarev et al.· 0 citations
Adaptive Log-Space (AL) quantization for non-negative states is introduced and its results make state topology and update semantics first-class design constraints for optimizer quantization.
Treating supervision format as a first-class hyperparameter for multi-task reasoning SFT in large language models—at least in this benchmark-and-model setting—rather than a mere rendering detail is supported.
Nhat Thanh Vu, M. Rashid, Fariza Sabrina· Electronics· 0 citations
This work proposes distilling the model into a probabilistic classifier, enabling lightweight deployment without repeated LLM calls, and demonstrates that LSR improves macro-F1 scores by an average of 7.0% compared to standard zero-shot classification baselines.
Nathan Vandemoortele, Bram Steenwinckel, F. Ongenae et al.· Discover Computing· 0 citations
Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text retrieval, showing that feature-level distillation strengthens both compositional and general vision-language representation capability.
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao et al.· 0 citations
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