this post was submitted on 06 Feb 2024
30 points (89.5% liked)

Technology

59889 readers
2743 users here now

This is a most excellent place for technology news and articles.

Our Rules

  1. Follow the lemmy.world rules.
  2. Only tech related content.
  3. Be excellent to each another!
  4. Mod approved content bots can post up to 10 articles per day.
  5. Threads asking for personal tech support may be deleted.
  6. Politics threads may be removed.
  7. No memes allowed as posts, OK to post as comments.
  8. Only approved bots from the list below, to ask if your bot can be added please contact us.
  9. Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 2 years ago
MODERATORS
L3s
[email protected]
[email protected]
L4s
enu
30
Weaver: New Specialised Writing LLMs Outperform GPT-4 (arxiv.org)
submitted 10 months ago by hexual to c/technology
5 comments fedilink hide all child comments
 

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.

you are viewing a single comment's thread
view the rest of the comments
[–] [email protected] 4 points 10 months ago* (last edited 10 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 Arxiv is not peer reviewed.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