MIT-IBM Watson Lab: How AI Scaling Laws Are Transforming LLM Training Efficiency Training large language models (LLMs) is an expensive endeavor, driving the need for strategies that maximize performance while minimizing costs. Researchers at the MIT-IBM Watson AI Lab have develope... AI cost efficiency LLM training machine learning model development research scaling laws
MIT is Making Large Language Model Training Affordable: Insights from AI Scaling Laws Training large language models (LLMs) requires immense computational resources and significant financial investment. For many AI researchers and organizations, predicting model performance while keepi... AI efficiency AI research budget optimization LLM training machine learning model evaluation scaling laws
m-KAILIN Turns Biomedical Literature into AI-ready QA Data Training capable biomedical language models hinges on having the right data: not just more tokens, but high-fidelity question-answer examples grounded in the structure of biomedical knowledge. The pap... Biomedical QA Corpus distillation LLM training MeSH ontology Multi-agent systems Ontology-guided evaluation Preference optimization PubMedQA Retrieval-augmented generation Scientific literature