this post was submitted on 06 Feb 2024
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30
Weaver: New Specialised Writing LLMs Outperform GPT-4 (arxiv.org)
submitted 9 months ago by [email protected] to c/[email protected]
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Weaver introduces a new family of specialised large language models tailored for creative and professional writing. Offering models ranging from 1.8B to 34B parameters, said to outperform larger generalist models like GPT-4 by focusing on human-like text production and diverse content creation capabilities.

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[–] [email protected] 5 points 9 months ago (3 children)

is this an open source AI?

[–] [email protected] 10 points 9 months ago* (last edited 9 months ago)

One of the size classes they mention in the abstract is called "Weaver Pro" so my initial assumption would be that it's not. However, I find that with this sort of thing the most important secret is that something is possible. If Weaver works as advertised we will now know that it's possible fir a 34B model to get better-than-GPT4 performance, which means lots of people will be willing to devote resources to recreating it since they now know those resources won't be wasted.

And if Weaver is meant to be "commercial" I wouldn't be surprised if there's a bunch of censorship baked into it, so the eventual open-source version will have an advantage.

[–] [email protected] 4 points 9 months ago* (last edited 9 months ago)

It doesn't seem to be. Their Chinese website talks about buying AI credits, their English website only has a waitlist but this looks more like a new closed commercial product than anything else.

Also, check the appendix in the paper, I think it's a bit concerning that the second author is responsible for the writebench benchmark they use to make their claims about the model. That is, the evaluation isn't independent from the authors.

I mean, I'm not saying they're not right, just that this is a yellow flag to investigate more.

Second flag is I don't see a journal this will/is published in. Arxiv is not peer reviewed.

A. Appendix A.1. Author Contributions Tiannan Wang is the core contributor of Weaver. Tiannan is responsible for continual pre-training, supervised fine-tuning, and preference optimization. Tiannan is also a main contributor for the data synthesis and the benchmark/evaluation process.

Jiamin Chen is a main contributor of Weaver. Jiamin is responsible for WriteBench and is also main contributor for data synthesis and model evaluation process

[–] [email protected] 2 points 9 months ago

I don't think they've said what the license will be.