Components
Citation
An inline, LLM-style source reference that reveals the source title, link and snippet in a popover.
Large language models are trained on vast text corpora and learn to predict the next token Attention Is All You Need arxiv.org (opens in new tab) , a process that gives rise to surprisingly general capabilities. Scaling both data and parameters has been shown to improve performance across many tasks Scaling Laws for Neural Language Models arxiv.org (opens in new tab) .
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Large language models are trained on vast text corpora and learn to predict the next
token<x-ui.citation
:index="1"
title="Attention Is All You Need"
url="https://arxiv.org/abs/1706.03762"
/>, a process that gives rise to surprisingly general capabilities. Scaling both data
and parameters has been shown to improve performance across many
tasks<x-ui.citation
:index="2"
title="Scaling Laws for Neural Language Models"
url="https://arxiv.org/abs/2001.08361"
/>.
</p>
List
Sources
- Attention Is All You Need arxiv.org (opens in new tab) Attention Is All You Need (opens in new tab)
- Language Models are Few-Shot Learners arxiv.org (opens in new tab) Language Models are Few-Shot Learners (opens in new tab)
- Training language models to follow instructions arxiv.org (opens in new tab) Training language models to follow instructions (opens in new tab)
@php
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['title' => 'Attention Is All You Need', 'url' => 'https://arxiv.org/abs/1706.03762'],
['title' => 'Language Models are Few-Shot Learners', 'url' => 'https://arxiv.org/abs/2005.14165'],
['title' => 'Training language models to follow instructions', 'url' => 'https://arxiv.org/abs/2203.02155'],
];
@endphp
<div class="max-w-prose">
<h3 class="mb-2 text-sm font-semibold text-foreground">Sources</h3>
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With Snippet
Retrieval-augmented generation grounds model output in retrieved documents, reducing hallucination on knowledge-intensive tasks Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks arxiv.org (opens in new tab) We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia. .
<p class="max-w-prose text-sm leading-relaxed text-foreground">
Retrieval-augmented generation grounds model output in retrieved documents, reducing
hallucination on knowledge-intensive tasks<x-ui.citation
:index="1"
title="Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"
url="https://arxiv.org/abs/2005.11401"
snippet="We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia."
/>.
</p>