Bookmarked Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages by Kelechi Ogueji, Yuxin Zhu, Jimmy Lin, 2021

LLMs usually require loads of training data, the bigger the better. This biases such training, as Maggie Appleton also pointed out, to western and English dominated resources. This paper describes creating a model for a group of 11 African languages that are underresourced online, and as a result don’t figure significantly in the large models going around (4 of the 11 have never been included in a LLM before). All the material is available on GitHub. They conclude that training a LLM with such lower resourced languages with the larger ones is less effective than taking a grouping of underresourced languages together. Less than 1GB of text can provide a competitive model! That sounds highly interesting for the stated reason: it allows models to be created for underresourced languages at relatively little effort. I think that is a fantastic purpose because it may assist in keeping a wide variety of languages more relevant and bucking the trend towards cultural centralisation (look at me writing here in English for a case in point). It also makes me wonder about a different group of use cases: where you have texts in a language that is well enough represented in the mainstream LLMs, but where the corpus you are specifically or only interested in is much smaller, below that 1GB threshold. For instance all your own written output over the course of your life, or for certain specific civic tech applications.

We show that it is possible to train competitive multilingual language models on less than 1 GB of text. .our model … is very competitive overall. … Results suggest that our “small data” approach based on similar languages may sometimes work better than joint training on large datasets with high-resource languages.

Ogueji et al, 2021

Bookmarked Large Language Models are not Models of Natural Language: they are Corpus Models. (PDF) by Csaba Veres (2022)

I think it is a bit of a ‘well-duh’ thing but worth underlining in general conversation still. The name Large Language Model is somewhat misleading and a misnomer as it does not contain a model of how (a) language (theoritically) works. It e.g. doesn’t generate texts by following grammar rules. How LLMs can generate code from natural language prompts because they have been trained with sofware code without the theoretical underpinnings of programming languages leads to this by extension. Veres suggests using the term of Large Corpus Models. I think getting people to write LCMs and not LLMs will be impossible. I can however for myself highlight the difference by reading ‘Large Language usage Model’ everytime I see LLM. As the Corpus is one of language(s) in actual use.

We argue that the term language model is misleading because deep learning models are not theoretical models of language and propose the adoption of corpus model instead, which better reflects the genesis and contents of the model.

Csaba Veres, 2022

Bookmarked Inside the secret list of websites that make AI like ChatGPT sound smart (by By Kevin Schaul, Szu Yu Chen and Nitasha Tiku in the Washington Post)

The Washington Post takes a closer look at Google’s C4 dataset, which is comprised of the content of 15 million websites, and has been used to train various LLM’s. Perhaps also the one used by OpenAI for e.g. ChatGPT, although it’s not known what OpenAI has been using as source material.

They include a search engine, which let’s you submit a domain name and find out how many tokens it contributed to the dataset (a token is usually a word, or part of a word).

Obviously I looked at some of the domains I use. This blog is the 102860th contributor to the dataset, with 200.000 tokens (1/10000% of the total).

Screenshot of the Washington Post’s search tool, showing the result for this domain,

John Caswell writes about the role of conversation, saying "conversation is an art form we’re mostly pretty rubbish at". New tools that employ LLM’s, such as GPT-3 can only be used by those learning to prompt them effectively. Essentially we’re learning to have a conversation with LLMs so that its outputs are usable for the prompter. (As I’m writing this my feedreader updates to show a follow-up post about prompting by John.)

Last August I wrote about articles by Henrik Olaf Karlsson and Matt Webb that discuss prompting as a skill with newly increasing importance.

Prompting to get a certain type of output instrumentalises a conversation partner, which is fine for using LLM’s, but not for conversations with people. In human conversation the prompting is less to ensure output that is useful to the prompter but to assist the other to express themselves as best as they can (meaning usefulness will be a guaranteed side effect if you are interested in your conversational counterparts). In human conversation the other is another conscious actor in the same social system (the conversation) as you are.

John takes the need for us to learn to better prompt LLM’s and asks whether we’ll also learn how to better prompt conversations with other people. That would be great. Many conversations take the form of the listener listening less to the content of what others say and more listening for the right time to jump in with what they themselves want to say. Broadcast driven versus curiosity driven. Me and you, we all do this. Getting consciously better at avoiding that common pattern is a win for all.

In parallel Donald Clark wrote that the race to innovate services on top of LLM’s is on, spurred by OpenAI’s public release of Chat-GPT in November. The race is indeed on, although I wonder whether those getting in the race all have an actual sense of what they’re racing and are racing towards. The generic use of LLM’s currently in the eye of public discussion I think might be less promising than gearing it towards specific contexts. Back in August I mentioned Elicit that helps you kick-off literature search based on a research question for instance. And other niche applications are sure to be interesting too.

The generic models are definitely capable to hallucinate in ways that reinforce our tendency towards anthropomorphism (which needs little reinforcement already). Very very ELIZA. Even if on occasion it creeps you out when Bing’s implementation of GPT declares its love for you and starts suggesting you don’t really love your life partner.

I associated what Karlsson wrote with the way one can interact with one’s personal knowledge management system the way Luhmann described his note cards as a communication partner. Luhmann talks about the value of being surprised by whatever person or system you’re communicating with. (The anthropomorphism kicks in if we based on that surprisal then ascribe intention to the system we’re communicating with).

Being good at prompting is relevant in my work where change in complex environments is often the focus. Getting better at prompting machines may lift all boats.

I wonder if as part of the race that Donald Clark mentions, we will see LLM’s applied as personal tools. Where I feed a more open LLM like BLOOM my blog archive and my notes, running it as a personal instance (for which the full BLOOM model is too big, I know), and then use it to have conversations with myself. Prompting that system to have exchanges about the things I previously wrote down in my own words. With results that phrase things in my own idiom and style. Now that would be very interesting to experiment with. What valuable results and insight progression would it yield? Can I have a salon with myself and my system and/or with perhaps a few others and their systems? What pathways into the uncanny valley will it open up? For instance, is there a way to radicalise (like social media can) yourself by the feedback loops of association between your various notes, notions and follow-up questions/prompts?

An image generate with Stable Diffusion with the prompt “A group of fashionable people having a conversation over coffee in a salon, in the style of an oil on canvas painting”, public domain

I added chatGPT to Obsidian following these steps to install an experimental plugin. I also set up a pay as you go account for OpenAI as I’ve used up my trial period for both Dall-E and GPT-3.

At first glance using the GPT3 plugin works fine, although what seems to be missing is the actual chat part, the back and forth where you build on a response with further prompts. The power of chatGPT as a tool is in that iterative prompting I think.
You can still iterate by prompting the chatGPT with the entirety of the previous exchange but that is slightly cumbersome (it means you’d go into a note delete the previous exchange, and then have the plugin re-add it plus the next generated part).

I think it will be more fruitful to do the entire exchange with chatGPT in the browser and manually grab the content if I want to use it in Obsidian. The MarkDownload browser extension capably grabs the entire exchange with chatGPT and stores it as markdown in my Obsidian notes as well.