Another similar project: https://github.com/databrickslabs/dolly

> This fine-tunes the GPT-J 6B model on the Alpaca dataset using a Databricks notebook. Please note that while GPT-J 6B is Apache 2.0 licensed, the Alpaca dataset is licensed under Creative Commons NonCommercial (CC BY-NC 4.0).

Thanks for sharing! How does it compare against other models (eg, alpaca or gpt 3.5/4)?

Research done by the industry. Eg, FAIR or MSR.

https://crfm.stanford.edu/2023/03/13/alpaca.html:

> We emphasize that Alpaca is intended only for academic research and any commercial use is prohibited. There are three factors in this decision: First, Alpaca is based on LLaMA, which has a non-commercial license, so we necessarily inherit this decision. Second, the instruction data is based OpenAI’s text-davinci-003, whose terms of use prohibit developing models that compete with OpenAI. Finally, we have not designed adequate safety measures, so Alpaca is not ready to be deployed for general use.

Why only academic research and not industry research? I don't see where that limitation comes from in their 3 factors.

Pretty sure commercial product only. Speechmatics has never opensourced any of their models.

No

Why SOTA? Did they compare against GPT 3.5? Only comparison against GPT 3.5 I found in the LLaMA paper was:

> Despite the simplicity of the instruction finetuning approach used here, we reach 68.9% on MMLU. LLaMA-I (65B) outperforms on MMLU existing instruction finetuned models of moderate sizes, but are still far from the state-of-the-art, that is 77.4 for GPT code-davinci-002 on MMLU (numbers taken from Iyer et al. (2022)).

For summarization: Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto. Benchmarking Large Language Models for News Summarization. arXiv:2301.13848.

> If you want to use the exclusion then I would keep the policy with that date.

Got it, thanks

>You can always have multiple policies.

No thanks, my goal is minimizing expenses.

Not my fault if policymakers are publishing stupid laws.

Yeah I wish we had a clear table of prices for each qualifying LTC insurance. This law and its implementation are very poorly thought-out. Policymakers were either bribed by insurers or profoundly stupid.

Thanks!

> then hold onto it.

so it means one can't change one's policy?

One can get insurance for less than 25 USD/month, eg https://www.reddit.com/r/Washington/comments/112ejat/whats_the_cheapest_longterm_care_insurance_that/j8kayl0

Nice, thanks!

thanks, how much is it?

Got it, thanks!

> ongoing proof that you have an active policy.

Can one change one's LTC insurance and still be tax exempt? I'm guessing so but I couldn't see the info in the law.

Thanks, they are still planning to implement that tax, from July 2023 last time I heard about it but the state legislators keep changing their minds.

Got it, thanks. Given the high price I'm paying currently for the LTC, I'd like to switch ASAP to some cheap insurance and just cancel it if the law change confirms one can drop it.

Why not impressive?

> I was surprised at how much better GPT3 davinci 003 performed compared to AI21's 178B model. AI21's Jurassic 178B seems to be comparable to GPT3 davinci 001.

on which tasks?

> Of course, I didn't expect the smaller models to be on par with GPT-3

You could read Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto. Benchmarking Large Language Models for News Summarization. arXiv:2301.13848.:

> we find instruction tuning, and not model size, is the key to the LLM’s zero-shot summarization capability

Wouldn't ChatGPT inaccuracies be an issue if used for education?

For which downstream application?

> I mean some kind of home assistant that integrates CHATGPT is obvious right?

How do you handle the fact that some answers are inaccurate?

Thanks! How does Speechbox' punctuation restoration compare to other existing models/codebases for punctuation restoration?