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The Top ML Papers of the Week (July 15 - July 21): - SpreadsheetLLM - Weak-to-Strong Reasoning - Improving LLM Output Legibility - Distilling System 2 into System 1 - A Survey of Prompt Engineering in LLMs - Context Embeddings improves RAG Efficiency ...
1/n How Self-Taught Rationales Enhance LLMs Large language models have astounded us with their ability to generate human-quality text, their reasoning prowess often falls short, leaving us wanting more. What if, instead of feeding them mountains of data, we could teach them to… https://t.co/aBKwrEL2vU
🚨This week’s top AI/ML research paper: LM - Q-Sparse - SpreadsheetLLM (MSFT) - Questionable practices in machine learning - Accuracy is Not All You Need (MSFT) - Qwen2 Technical Report - Does Refusal Training in LLMs Generalize to the Past Tense? - Prover-Verifier Games improve… https://t.co/RR6vXZoIyn
Researchers from Microsoft and the University of Chinese Academy of Sciences have introduced Q-Sparse, a novel approach aimed at achieving full sparsity of activations in large language models (LLMs). This method enhances the efficiency of training sparsely-activated LLMs, contributing to advancements in artificial intelligence. In addition to Q-Sparse, several other notable papers have emerged in the field of machine learning and AI this week, including 'SpreadsheetLLM' and 'Weak-to-Strong Reasoning.' These developments highlight ongoing efforts to optimize LLM infrastructure and applications, underscoring the importance of model efficiency and reasoning capabilities in the evolution of AI technologies.