Understanding RAG Part IX: Fine-Tuning LLMs for RAG
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Muon Optimizer Significantly Accelerates Grokking in Transformers: Microsoft Researchers Explore Optimizer Influence on Delayed Generalization
Revisiting the Grokking Challenge In recent years, the phenomenon of grokking—where deep learning models exhibit a delayed yet sudden transition from memorization to generalization—has prompted renewed investigation into training dynamics. Initially observed in small algorithmic tasks like modular arithmetic, grokking…
Understanding RAG Part X: RAG Pipelines in Production
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“Periodic table of machine learning” could fuel AI discovery | MIT News
MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up…
Open-Source TTS Reaches New Heights: Nari Labs Releases Dia, a 1.6B Parameter Model for Real-Time Voice Cloning and Expressive Speech Synthesis on Consumer Device
The development of text-to-speech (TTS) systems has seen significant advancements in recent years, particularly with the rise of large-scale neural models. Yet, most high-fidelity systems remain locked behind proprietary APIs and commercial platforms. Addressing this gap, Nari Labs has released…
LLMs Still Struggle to Cite Medical Sources Reliably: Stanford Researchers Introduce SourceCheckup to Audit Factual Support in AI-Generated Responses
As LLMs become more prominent in healthcare settings, ensuring that credible sources back their outputs is increasingly important. Although no LLMs are yet FDA-approved for clinical decision-making, top models such as GPT-4o, Claude, and MedPaLM have outperformed clinicians on standardized…