A concept framed off a fresh paper.
BlockRank is a novel method for in-context ranking that uses structured sparse attention and contrastive training to improve LLM efficiency and accuracy.
Semantic Similarity Rating (SSR) maps LLM free-text responses to Likert distributions to improve purchase intent realism and match human response patterns.
Google Research's TimesFM-ICF uses in-context fine-tuning to achieve high-performance time-series forecasting without the need for traditional model training.
A recent patent application describes a method for training AI models to better understand human queries by using LLMs to automatically generate training data.
Self-Supervised Quantized Representation (SSQR) integrates knowledge graphs with large language models by compressing entity information into discrete codes.
A discussion of the Attention Is All You Need paper, covering the Transformer architecture, multi-head attention, and its impact on machine translation.
