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<h1 class="title is-1 publication-title">LLaSM: Large Language and Speech Model</h1>
<div class="is-size-5 publication-authors">
<span class="author-block" style="color:#008AD7;font-weight:normal;">
Yu Shu<sup>2</sup>,</span>
<span class="author-block" style="color:#008AD7;font-weight:normal;">
Siwei Dong<sup>2</sup>,</span>
<span class="author-block" style="color:#ed2f09;font-weight:normal;">
Guangyao Chen<sup>1,3</sup>,
</span>
<span class="author-block" style="color:#cc00d7;font-weight:normal;">
Wenhao Huang<sup>4</sup>,
</span>
<span class="author-block" style="color:#19e706;font-weight:normal;">
Rita Zhang,
<!-- Rita Zhang<sup>5</sup>, -->
</span>
<span class="author-block" style="color:#19e706;font-weight:normal;">
Daochen Shi,
<!-- Daochen Shi<sup>5</sup>, -->
</span>
<span class="author-block" style="color:#19e706;font-weight:normal;">
Qiqi Xiang,
<!-- Qiqi Xiang<sup>5</sup>, -->
</span>
<span class="author-block" style="color:#f68946;font-weight:normal;">
Yemin Shi<sup>1*</sup>
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block" style="color:#f68946;font-weight:normal;"><sup>1</sup>LinkSoul.AI,</span>
<span class="author-block" style="color:#008AD7;font-weight:normal;"><sup>2</sup>Beijing Academy of Artificial Intelligence, China,</span>
<span class="author-block" style="color:#ed2f09;font-weight:normal;"><sup>3</sup>Peking University, China</span>
<span class="author-block" style="color:#cc00d7;font-weight:normal;"><sup>4</sup>01.ai</span>
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<span class="author-block"><sup>*</sup>Corresponding author: ymshi@linksoul.ai</span>
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<span>arXiv</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Multi-modal large language models have garnered significant interest recently. Though,
most of the works are focusing on vision-language multi-modal models providing
strong capabilities in following vision-and-language instructions. However, we claim
that speech is also an important modality through which human interact with the
world. Hence, it is crucial for a general-purpose assistant to be able to follow multi-modal
speech-and-language instructions. In this work, we propose <b>L</b>arge <b>L</b>anguage
<b>a</b>nd <b>S</b>peech <b>M</b>odel (<b>LLaSM</b>). <b>LLaSM</b> is an end-to-end trained large multi-modal
speech-language model with cross-modal conversational abilities, capable of following
speech-and-language instructions. Our early experiments show that <b>LLaSM</b> demonstrates
a more convenient and natural way for human to interact with artificial intelligence.
Specifically, we also release a large Speech Instruction Following data set <b>LLaSM-Audio-Instruction</b>.
</p>
<p>
Our paper makes the following contributions:
</p>
<ui>
<li>
We build a speech-language multi-modal assistant that can understand and follow the speech-language instructions, which provides a more convenient and natural way for humans to interact with artificial intelligence.
</li>
<li>
We construct and release <a href="https://huggingface.co/datasets/LinkSoul/LLaSM-Audio-Instructions" target="_blank">LLaSM-Audio-Instructions</a>, a large scale Chinese and English speech-text cross-modal instruction following dataset.
</li>
<li>
We release the code in <a href="https://github.com/LinkSoul-AI/LLaSM" target="_blank">https://github.com/LinkSoul-AI/LLaSM</a>.
</li>
<li>
We release the models in <a href="https://huggingface.co/LinkSoul/LLaSM-Cllama2" target="_blank">LLaSM-Chinese-Llama-2-7B</a> and <a href="https://huggingface.co/LinkSoul/LLaSM-Baichuan" target="_blank">LLaSM-Baichuan-7B</a>.
</li>
</ui>
</div>
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<h2 class="title is-3">Tips</h2>
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<h4>
Demo 试用教程
</h4>
<ul>
<li>
文本框输入文字，点击最右侧发送按钮即可发送消息，开始聊天。
</li>
<li>
点击语音按钮，开始录音，再次点击，结束录音。点击发送按钮，即可发送语音消息。
</li>
<li>
语音未发送之前可在音频预览区检查，聊天框中的历史语音消息同样支持回放。
</li>
<li>
点击重置按钮可清空历史对话信息。
</li>
<li>
注：本 demo 仅作为 LLaSM 的模型能力展示，对多轮对话中话题切换支持不足。切换聊天话题时，建议清空历史以获得更好的体验。
</li>
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<!--/ Demo Tips. -->
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<h2 class="title">BibTeX</h2>
<pre>
<code>
@misc{shu2023llasm,
title={LLaSM: Large Language and Speech Model},
author={Yu Shu and Siwei Dong and Guangyao Chen and Wenhao Huang and Ruihua Zhang and Daochen Shi and Qiqi Xiang and Yemin Shi},
year={2023},
eprint={2308.15930},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
</code>
</pre>
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</section>
<section class="section" id="Acknowledgement">
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<h2 class="title">Acknowledgement</h2>
<p>
This website is adapted from <a href="https://github.com/nerfies/nerfies.github.io" target="_blank">Nerfies</a>, licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
Commons Attribution-ShareAlike 4.0 International License</a>. We thank the open-source projects for giving us access to their models, including <a href="https://huggingface.co/LinkSoul/Chinese-Llama-2-7b" target="_blank">Chinese-Llama-2-7B</a> and <a href="https://huggingface.co/openai/whisper-large-v2" target="_blank">Whisper</a> and <a href="https://huggingface.co/baichuan-inc/Baichuan-7B" target="_blank">Baichuan-7B</a>.
</p>
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