Hello everybody, an interesting article about how LLM can work faster if the conversion lo language happens in the final stages. I find interesting the description of how LLM work
Hi Jorge, thank you for sharing this very interesting article. This are the key ideas:
Token Gesture: Large Language Models (LLMs) like ChatGPT operate by converting text into tokens—segments that can be words, subwords, or characters. These tokens are transformed into numerical embeddings, which the model processes through multiple layers to predict subsequent tokens. This iterative process, while effective, can be computationally intensive and may introduce inefficiencies when translating complex ideas into linear text sequences.
Don´t Verbalize: Recent research suggests that compelling LLMs to express their internal computations in natural language might be limiting their reasoning capabilities. By allowing models to operate within their latent (mathematical) spaces without immediate translation into words, they can perform tasks more efficiently. This approach reduces computational overhead and preserves the richness of the model's internal representations.
Getting Loopy: Innovative architectures are being explored to enhance LLM reasoning. One such approach involves "loopy" models that revisit and refine their internal representations before producing output. This iterative refinement enables the model to develop more coherent and contextually appropriate responses, akin to human reflective thinking.
Back to basic: The article concludes by emphasizing a paradigm shift: instead of forcing AI to conform to human language constraints, we should adapt our interaction methods to align with the models' inherent processing strengths. By embracing the models' native computational frameworks, we can unlock more advanced and efficient AI capabilities.
Hi Jorge, thank you for sharing this very interesting article. This are the key ideas:
Token Gesture: Large Language Models (LLMs) like ChatGPT operate by converting text into tokens—segments that can be words, subwords, or characters. These tokens are transformed into numerical embeddings, which the model processes through multiple layers to predict subsequent tokens. This iterative process, while effective, can be computationally intensive and may introduce inefficiencies when translating complex ideas into linear text sequences.
Don´t Verbalize: Recent research suggests that compelling LLMs to express their internal computations in natural language might be limiting their reasoning capabilities. By allowing models to operate within their latent (mathematical) spaces without immediate translation into words, they can perform tasks more efficiently. This approach reduces computational overhead and preserves the richness of the model's internal representations.
Getting Loopy: Innovative architectures are being explored to enhance LLM reasoning. One such approach involves "loopy" models that revisit and refine their internal representations before producing output. This iterative refinement enables the model to develop more coherent and contextually appropriate responses, akin to human reflective thinking.
Back to basic: The article concludes by emphasizing a paradigm shift: instead of forcing AI to conform to human language constraints, we should adapt our interaction methods to align with the models' inherent processing strengths. By embracing the models' native computational frameworks, we can unlock more advanced and efficient AI capabilities.