Digesting research papers may get easier

Given that two of my posts this year have been on exploring ChatGPT with regard to computational materials science and document figure preparation, I started to look for what other derivatives of OpenAI's GPT-3 model are being used for. Its pretty cool what I'm finding so far,  although I quite don't understand how to use OpenAI's API to fine-tune their already trained models because I'm thinking of a project of my own 😉 (post links above). Well actually to be frank I still don't understand how self-attention layers work (this is the best post so far I found) in a transformed model, or even really how the transformer model works but that's because I'm still actively learning the theory behind many different neural network architectures. Using them is different since its so easy nowadays to implement something like a convolutional neural network in a 10's of lines in python. Going back to the GPT spin-off resources, I found typeset.io or soon-to-be scispace.com. And tieing back to how to better understand self-attention layers I used this resource.

Typeset.io is basically a tuned ChatGPT for research articles that is able to process uploaded pdfs and make it easy for a reader to get clarification. For example, if you ask it to explain "what is the primary information fig. 1 is conveying" it will produce a fairly easy-to-understand response. I played around with Typeset.io for one of my own papers to see how good it was and I was pretty impressed. It basically was able to describe everything one needed to know to understand the primary takeaways from the paper. 

I'm now in the process of using it to read through the Attention is all you need paper, it's pretty helpful, especially in writing down notes. I do wish the integration with Zotero was a bit more featured. It also is really helpful for finding additional papers that are related but maybe not cited. As of now you can have it provide descriptions of selected text, equations, or tables, but not images themselves which makes sense given that's not something, to my knowledge, GPT can do at the moment (i.e. vision to natural language). One other pretty impactful feature, although not so much for me, is that you can prompt the chat bot in another language. So if there is some confusing english that may not be clear to you, it is possible to ask and get a response in your native language about the text in question. 

There are a few downsides that I'm personally finding. For one, its easy to get distracted and go down a rabbit hole with it. Say your reading the introduction and you find some reference or topic that sounds interesting but unfamiliar you can easily get stuck spending 30 mins with the chat bot on it. This make reading so much slower and directly impacts the actual reading comprehension because your breaking up the reading process so much. Therefore, I'm think its better to first read the paper in your default pdf reader or even better physical print. Once your done, then upload it to typeset.io and query the parts you marked-up.

What I'm curious about is if this type of large language model adoption to research articles can be used to propose research directions (e.g. experiments to conduct) based on a set of research articles that a user provides and then prompts. Since I'm interested in multiferroics, it would be cool if based on a list of papers provided, the chat bot could be prompted to suggest what potential composition changes, or processing conditions could be explored that would build off the existing work. The holy grail here would be to just say:

Tell me, for a single phase material, what composition and processing parameters would give me a magnetoelectric coupling coefficient  500  mV/Oe-cm at room temperature and magnetic field strength > 500 Oe. If no known material exist, provide the computational modeling approach and inputs to make theoretical predictions about materials that may satisfy the criteria. Then help inform me on how to design and conduct a synthesis experiment based on the theoretical findings.

This prompt basically sums up the whole materials by design and if the chat bot was tied to other AI systems like self-driving labs, it could basically be serious productivity enhancement resource that assist humans scientist.

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