Al ver grandes cantidades de texto, un modelo de lenguaje aprende la probabilidad de una secuencia de palabras. Un Modelo de Lenguaje Grande (LLM) también aprende ciertos matices del propio idioma. Sin que se le enseñen explícitamente las reglas gramaticales, codifica en los parámetros de su modelo la sintaxis del lenguaje y la semántica de las palabras.
Dado un prompt (entrada), un LLM predice la siguiente palabra más probable. Por ello, los LLM son generativos por naturaleza. Además, los LLM forman parte de la disciplina más amplia conocida como Inteligencia Artificial Generativa.
No existe una definición exacta de qué hace que un modelo de lenguaje sea “grande”. Cualquier modelo entrenado con miles de millones de palabras y que aprende varios miles de millones de parámetros puede considerarse un LLM. A partir de cierto tamaño, se observa que los LLM presentan comportamientos emergentes.
La mayoría de los usuarios utilizan modelos de lenguaje previamente entrenados, que pueden ajustarse (fine-tuning) para casos de uso específicos y luego utilizarse a través de aplicaciones.
By seeing lots of text, a language model learns the probability of a sequence of words. A Large Language Model (LLM) also learns certain nuances of the language itself. Without being explicitly taught the rules of grammar, it encodes in its model parameters the syntax of the language and word semantics.
Given an input prompt, an LLM predicts the next most probable word. Hence, LLMs are generative in nature. LLMs come under the more general discipline of Generative AI.
There’s no definite answer to what makes an LLM large. Any model trained on billions of words and learns a few billion parameters is perhaps an LLM. Above a certain threshold size, LLMs are seen to exhibit emergent behaviour.
Most users will use pre-trained LMs, perhaps fine-tune them for specific use cases and invoke them via apps.
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By seeing lots of text, a language model learns the probability of a sequence of words. A Large Language Model (LLM) also learns certain nuances of the language itself. Without being explicitly taught the rules of grammar, it encodes in its model parameters the syntax of the language and word semantics.
Given an input prompt, an LLM predicts the next most probable word. Hence, LLMs are generative in nature. LLMs come under the more general discipline of Generative AI.
There’s no definite answer to what makes an LLM large. Any model trained on billions of words and learns a few billion parameters is perhaps an LLM. Above a certain threshold size, LLMs are seen to exhibit emergent behaviour.
Most users will use pre-trained LMs, perhaps fine-tune them for specific use cases and invoke them via apps.
By seeing lots of text, a language model learns the probability of a sequence of words. A Large Language Model (LLM) also learns certain nuances of the language itself. Without being explicitly taught the rules of grammar, it encodes in its model parameters the syntax of the language and word semantics.
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