LLM2Vec: Unlocking Hidden Power of LLMs

Author(s): Singh Manpreet

Originally published on Towards AI.

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A new idea called LLM2Vec could change how we use big language models in natural language processing (NLP).

Researchers have found a way to turn Large Language Models (LLMs) that are usually used just to generate text into strong tools for understanding and organizing text.

This could change the way we handle all sorts of text-related tasks and it could mean we don’t have to rely on older models like BERT as much.

Let’s take a look at the key findings and how LLM2Vec makes these models better at handling text.

Big models like GPT-3 are amazing at generating text for lots of different tasks. But, when it comes to doing things that need deep understanding — like finding information, grouping similar pieces of text, or understanding relationships between words — these models don’t work as well.

The problem is their causal attention mechanism.

This means that each word can only pay attention to the words that come before it, which makes it harder to understand the whole meaning of a sentence.

This is where LLM2Vec helps.

It fixes this problem by using a simple but powerful three-step plan:

(1) Enabling Bidirectional Attention

(2) Masked Next Token Prediction… Read the full blog for free on Medium.

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Published via Towards AI