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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-5RJ4S1SNLG"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-5RJ4S1SNLG');
</script>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title> AlpaGasus: Training a Better Alpaca with Fewer Data </title>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css"
integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
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<link rel="icon" href="alpagasus.jpeg" type="image/jpeg">
<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<script>
function trackLocation(e) {
var rect = videoContainer.getBoundingClientRect(),
position = ((e.pageX - rect.left) / videoContainer.offsetWidth)*100;
if (position <= 100) {
videoClipper.style.width = position+"%";
clippedVideo.style.width = ((100/position)*100)+"%";
clippedVideo.style.zIndex = 3;
}
}
</script>
<style> #video-compare-container {
display: inline-block;
line-height: 0;
position: relative;
width: 100%;
padding-top: 42.3%;
}
#video-compare-container > video {
width: 100%;
position: absolute;
top: 0; height: 100%;
}
#video-clipper {
width: 50%; position: absolute;
top: 0; bottom: 0;
overflow: hidden;
}
#video-clipper video {
width: 200%;
position: absolute;
height: 100%;
}
</style>
</head>
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<h1 class="nerf_title_v2" style="font-size: 3em">AlpaGasus: Training a Better Alpaca with Fewer Data </h1>
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<div class="nerf_authors_list_single w-row">
<!-- <div class="w-col w-col-1"><span class="text-span_nerf">1</span></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a href="https://lichang-chen.github.io/" target="_blank"
class="nerf_authors_v2">Lichang Chen<span class="text-span_nerf_star">*</span><span
class="superscript text-span_nerf">1*</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a href="https://scholar.google.com/citations?user=4zli0KkAAAAJ&hl=en" target="_blank"
class="nerf_authors_v2">Shiyang Li<span class="text-span_nerf_star">*</span><span
class="superscript text-span_nerf">1*</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a href="https://junyann.github.io/" target="_blank"
class="nerf_authors_v2">Jun Yan<span class="text-span_nerf"></span><span
class="superscript text-span_nerf"></span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://home.ttic.edu/~haiwang/" target="_blank"
class="nerf_authors_v2">Hai Wang<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://zhengtang1120.github.io/index.html" target="_blank"
class="nerf_authors_v2">Zheng Tang<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://kalpagunaratna.github.io/" target="_blank"
class="nerf_authors_v2">Kalpa Gunaratna<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://kalpagunaratna.github.io/" target="_blank"
class="nerf_authors_v2">Vikas Yadav<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://kalpagunaratna.github.io/" target="_blank"
class="nerf_authors_v2">Vijay Srinivasan<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://tianyizhou.github.io/" target="_blank"
class="nerf_authors_v2">Tianyi Zhou<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://sites.pitt.edu/~heh45/" target="_blank"
class="nerf_authors_v2">Heng Huang<span class="text-span_nerf">1</span></a></div>
<div class="w-col w-col-2 w-col-small-4 w-col-tiny-6"><a
href="https://scholar.google.com/citations?user=mmJC6pIAAAAJ&hl=en" target="_blank"
class="nerf_authors_v2">Hongxia Jin<span class="text-span_nerf">1</span></a></div>
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<div class="w-col w-col-1"><span class="text-span_nerf">1</span></div>
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<a class="btn btn-primary" href="https://arxiv.org/abs/2307.08701">Paper</a>
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<img width=30% class="center" src="alpagasus.jpeg">
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<p style="text-align: center;"> The name "AlpaGasus" combines two words, Alpaca and Pegasus. The logo is generated by Midjourney. </p>
<div style="text-align: center;">
<!-- <h5 align="center">Residual Error (x5)</h5> -->
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<!-- <p style="margin-top: -5rem;" > <b>Left</b>: Soft prompts selected by InstructZero in three consecutive iterations (2D embedding by t-SNE). Colors denote different iterations and a larger circle refers to a higher objective value (zero-shot validation accuracy). Numbers highlight the best soft prompt per iteration. <b>Right</b>: instructions generated by the best soft prompt per iteration and the associated validation accuracy. </p> -->
<p style="margin-top: 0rem;" >
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<img width=50% class="center" src="videos/animation_v2.gif">
<h3 align="center"> Introducing AlpaGasus: using less data, training faster, and obtaining better performance! </h3>
<div class="section">
<h2 align="left">Overview</h2>
<hr>
<div>
<p style="text-align: left;"> In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce <b>AlpaGasus</b>, which is finetuned on only <b>9k high-quality data</b> filtered from the 52k Alpaca data.
AlpaGasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and its 13B variant could match >90% performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to <b>14 minutes</b>.</p>
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<div style="text-align: center;">
<img width=50% class="center" src="elements/images/overview.svg">
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<p style="text-align: center;"> Performance of AlpaGasus on four test sets when increasing its finetuning data, where the winning score is
\( \frac{ \text{#Win} - \text{#Lose}}{\text{#Testset}} + 1 \)
with #Testset = #Win + #Tie + #Lose to be the test set size and #win/#tie/#lose
to be the number of samples on which \(\text{AlpaGasus}\) wins/draws/loses.</p>
<br>
<p style="text-align: left;"> <b> TL;DR: </b> The first <b>automatic</b> framework to filter bad data in the IFT datasets. </p>
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<h2 align="left"> LLM-based Filtering </h2>
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<p style="margin-top: 0rem;"> <b> Pipeline.</b> We prompt ChatGPT as our auto-grader to score each training triplet on a scale of 0 to 5. We then use exactly the same instruction fine-tuning script of Alpaca to train AlpaGasus on the filtered data with scores higher than a threshold. For the details of our prompt, please check out our paper. </p>
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<h2 align="left">Score Distribution</h2>
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<p style="margin-top: 0rem;"> <b> Histogram of Scores. Threshold=4.5 </b> </p>
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We filter out 9k data from original Alpaca's 52k data.
</p>
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<p>Left: Rendering produced by StegaNeRF. Right: Recovered hidden image from the corresponding rendered frame.</p>
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<h2 align="left"> Main Results</h2>
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<p align="left"> Win-Tie-Lose figures for comparisons of 7B and 13B models instruction-finetuned with different training sets. (Ours: 9k vs. Original Alpaca: 52k) </p>
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<p align="left"> For other experiment results, please refer to our paper. </p>
<!-- <p align="left"> We also visualize the soft prompts explored by InstructZero over three BO iterations. It shows how the score of the best soft prompt improves over time and the efficient exploration-exploitation conducted by the latent space BO <b>step by step</b>, which is shown as follows.</p> -->
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<h2>Misc</h2>
<p> If you are not familiar with "leviOsa", check the video! </p>
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class="embedded-video-16-9" src="https://www.youtube.com/embed/Qgr4dcsY-60" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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https://github.com/gpt4life/AlpaGasus
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<div class="section">
<h2 align="left">Code Release</h2>
<hr>
<p align="left">
1. Full-model finetuning(FSDP): thanks for <a href="https://github.com/gpt4life/alpagasus" target="_blank">@gpt4life</a> reproducing our results and upload the models to <a href="https://huggingface.co/gpt4life/alpagasus-7b" target="_blank">huggingface.</a> (They use Claude as LLM-filter for reproducing so the final results may be different from our paper.) </p>
<p align="left">
2. QLoRA: thanks to @gauss5930 and @YooYunS who implemented the QLoRA version of Alpagasus-7B and 13B, which could be run on the customer-level GPUs. please refer to their repo: <a href="https://github.com/gauss5930/AlpaGasus2-QLoRA" target="_blank">@Alpagasus2-QLoRA</a> They also show that tuning LLaMA-2 could achieve better performance.
</p>
<br>
<h2 align="left">Future Work</h2>
<hr>
<p align="left"> Since IFT datasets could be gathered either via crowdsourcing or generated by machines such as GPT4, we will also test our method on the Dolly training set. </p>
<br>
<div class="section">
<h3>Citation</h3>
<div class="form-group col-12">
<textarea id="bibtex" class="form-control" rows=6 readonly>
@article{Chen2023AlpaGasus,
title={AlpaGasus: Train A Better Alpaca with Fewer Data.},
author={Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin},
journal={arXiv preprint arXiv:2307.08701},
year={2023}
}
</textarea>
</div>
</div>
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<h3>
Acknowledgements
</h3>
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The website template was borrowed from <a href="https://pfnet-research.github.io/distilled-feature-fields/">distilled-feature-fields</a>.
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