stp2y

arXiv:2408.01031v1 Announce Type: new Abstract: Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a certain size at one time. Nevertheless, various computation or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes to deploy. Thus, in this study, we propose a novel tri-branch self-supervised training framework, termed as POA (Pre-training Once for All), to tackle this aforementioned issue. Our approach introduces an innovative elastic student branch into a modern self-distillation paradigm. At each pre-training step,…

arXiv:2408.01122v1 Announce Type: new Abstract: The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications. Existing evaluations mainly focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user's perspective. To bridge this gap, we propose CFBench, a large-scale Comprehensive Constraints Following Benchmark for LLMs, featuring 1,000 curated samples that cover more than 200 real-life scenarios and over 50 NLP tasks. CFBench meticulously compiles constraints from real-world instructions and constructs an innovative systematic framework for constraint types, which includes…

Organizations that have placed early bets on the potential of AI could find their expectations unmet, according to Gartner, one of the leading research and advisory firms. Despite the novelty and excitement surrounding GenAI, many organizations are still grappling with how to effectively leverage its potential.  Organizations are looking to deploy GenAI to transform their businesses and create new business opportunities. However, companies are struggling to justify their GenAI investments in terms of productivity enhancements.  The Gartner report predicts that at least 30% of GenAI projects currently in testing will be abandoned after proof of concept by the end of 2025.…