Venga OpenAI, se que me he demorado pero ya he sacado el análisis de vuestra última tecnología… Sacadme algo nuevo!
LLMS
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GPT-4o Analysis: OpenAI’s Major AI Breakthrough Explained
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¡NUEVO VIDEO! Aunque lo cubrimos en directo, me quedé con ganas de analizar en profundidad el anuncio de GPT-4o por parte de OpenAI. El que es uno de los grandes avances de este año… ¿Qué es? ¿Cómo funciona? ¿Qué futuro nos propone OpenAI? Ya tenéis el análisis 🙂
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Language Models Are Not AI: Clarifying the Terminology
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1. We were NOT talking about "AI". That's a paper about language models, and even though other people are (now) talking about LLMs as if they were AI, we weren't and never would. >>
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LLMs for Financial Analysis and Profitable Trading Strategies
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9/ Financial Statement Analysis with LLMs – claims that LLMs can generate useful insights from its analysis of trends and financial ratios; shows that GPT-4 performs on par with narrowly specialized models; and achieves a profitable trading strategy based on GPT’s predictions.
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SimPO: Reference-Free Preference Optimization for Language Models
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10/ SimPO – a simpler and more effective approach for preference optimization with a reference-free reward; uses the average log probability of a sequence as an implicit reward (i.e., no reference model required) which makes it more compute and memory efficient.
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Are Long-LLMs Really Necessary for Long-Context Tasks?
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8/ Are Long-LLMs A Necessity For Long-Context Tasks? – claims that long-LLMs are not a necessity to solve long-context tasks…
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Aya23: Multilingual Language Model Excelling in 23 Languages
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7/ Aya23 – a family of multilingual language models that can serve up to 23 languages; it intentionally focuses on fewer languages and allocates more capacity to these languages; shows that it can outperform other massive multimodal models on those specific languages.
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GNN-RAG: Combining LLMs with Graph Neural Networks
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5/ GNN-RAG – combines the language understanding abilities of LLMs with the reasoning abilities of GNNs in a RAG style; the GNN extracts useful and relevant graph information while the LLM takes the information and leverages its capabilities to perform question answering over
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Attention as RNN: Parallel Training with Constant Memory Inference
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6/ Attention as an RNN – presents a new attention mechanism that can be trained in parallel (like Transformers) and be updated efficiently with new tokens requiring constant memory usage for inferences (like RNNs).
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Abacus Embeddings Achieves 99% Accuracy on Addition Problems
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3/ Abacus Embeddings – achieves 99% accuracy on 100-digit addition problems by training on only 20-digit numbers with a single GPU; the main challenge this work addresses is the inability of transformers to track the exact position of digits.