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When More is More? When For an LLM is Enough?
Artificial Intelligence   Data Science   Latest   Machine Learning

When More is More? When For an LLM is Enough?

Author(s): Salvatore Raieli

Originally published on Towards AI.

In-context length is the LLM’s secret weapon, but with long-context is all changing
Photo by Angely Acevedo on Unsplash

It is better to know some of the questions than all of the answers. — James Thurber

In-context learning (ICL) is one of the most fascinating phenomena of large language models (LLMs). Just provide a few examples and the models can understand the task and execute it with surprising accuracy. Moreover, you do not have to alter a parameter because ICL is performed in inference.

What is and how does it work what makes Large Language Models so powerful

We still do not really know why it emerges during the training of LLMs but it is the key to the success of LLMs. With the emergence of the long context model, some researchers are beginning to think that it may be the alternative to fine-tuning.

In other words, why not provide a large number of examples and let the model figure out what it needs to do?

Is it really true that long-context LLMs are killing the RAG?

Although this is an attractive alternative we have no idea if it works. After all, the ICL study so far has been conducted only on models with a small context length (most of the models studied had no more than 4K context length)…. Read the full blog for free on Medium.

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Published via Towards AI

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