AI NewsResearchers propose a preconditioning layer that uses polynomial preconditioning to ensure stable weight conditioning throughout large language model (LLM) training, improving pre-training performance.
Large Language Models
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Google fired Timnit Gebru in 2020 for refusing to retract a research paper that warned about the dangers of large language models. Every warning in the paper has now come true, despite the industry's efforts to downplay them.
- 0.It's Not Just X. It's Y (mail.cyberneticforests.com)
The phrase 'It's not x, it's y' has become a common construction in language generated by Large Language Models (LLMs), sparking a debate about its use in writing and automated language production.
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Researchers proposed LLMSurgeon, a method to diagnose data mixture in large language models, by analyzing model outputs and identifying inconsistencies.
- 0.Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders (arxiv.org)
Researchers propose using sparse autoencoders to extract model internals from large language models (LLMs) for post-training data engineering.
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Researchers proposed a method to improve the capacity of multimodal large language models for subject-driven generation, used in text-to-image synthesis applications.
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Researchers propose a method for labeling code changes using large language models, focusing on structure-aware labeling to improve code change analysis accuracy.