Marco Galardini
10 October 2024
There are a couple of posts on the topic of large language models and the way we write about research that I have been thinking about for some time.
Now that both the physics and
chemistry Nobel prizes have just been awarded
to this topic and its applications I figured it would be a good time to
dust them off. Here’s the first one!
Anyone who is working in education at any level has probably been thinking on how to respond to the advent of Large Language Models (LLMs),
which really entered in the daily practice of millions of students when ChatGPT was released in early 2023.
Regardless of which side one takes on the use of LLMs for writing essays, manuscripts and other kinds of assignments
or writing-based contents, we have to recognize that they will be used extensively for the foreseable future.
Given how basically impossible it is to buld a reliable detector of text written by a LLM,
there is no easy way
to outright ban its use. Once that realization has sinked in, we can actually start focusing on how these
tools can be used as a way to become better at our job.
Even though these LLMs have progressively gotten better at various tasks, they still tend to produce very
long pieces of texts, and as a result they lack a good “punch”. This is maybe why everyone has fun
prompting these models but almost nobody likes to read the text they produce. This also means that
one way to differentiate ourselves from a LLM is to be very concise and to the point when describing
something. But this is not an easy task! This famous quote from Pascal sums it up nicely:
“I apologize for such a long letter - I didn’t have time to write a short one.”.
With these thoughts in mind we came up with a little “game” during our last lab retreat.
Could we all come up with very concise descriptions of our own research?
We also are mindful of the need to reduce the amount of jargon as much as possible to
make our research understandable by as large an audience as possible.
We therefore started with the following task: “describe your research using only the
1’000 most common words in the English language”. This task is of course inspired
by the Up Goer Five xkcd comic, and we used the great online text editor made by
Theo Sanderson.
Here’s the results:
- We think that different kinds of this tiny animal that lives in our stomach become stronger to stuff more quickly than others. We are checking this by taking these different kinds of tiny animals and giving them more and more of the stuff until they die. We think that those tiny animals that live longer then the others are faster, and so more interesting.
- I am working with the things that remembers how very small animals change over time, which all animals carry inside themselves. My job is to make a computer find positions on these things that are possible attack points for new things that make people not die as fast. I am focusing on the small animals that are very bad for people and try to not hurt the good ones.
- To use computers to understand how really little animals live and move around people and the world, and recognize which things are more important for them to do so.
- Tiny little things have tiny blocks that help them to live in their houses. Computers help to study what those tiny blocks do and that is important to study. Each little thing has its own blocks that do different jobs, and we do not know a lot about them. We can better understand how little things live and do their job if we study their blocks.
- Small living things can grow in different places. One way they can do this is by causing changes within themselves sometimes in areas that are not very obvious. I am looking into these places to see if they can explain why these tiny things act the way they do.
- I made a group of tiny things, and put them with some drinking things to see how tiny things can grow inside group
- During my time joining the group I started with learning how to use a computer to find possible reasons why some things are worse than others for feeling well. After a while, I also started to do this by using a faster thing for that. I was trained me on how to check it in real life.
Notice how hard we had to work to come up with a way to write “bacteria”; we all came up with a different term:
“tiny animal”, “small animal”, “little animal”, “tiny little things”, “small living things”, “tiny things”.
The most accurate definition was Bamu’s “small living things”. We then relaxed the requirements and rewrote these
descriptions to use any word from the English language, but still trying to be as accessible as possible:
- We think that different types of this bug that lives in our intestine become resistant to antibiotics with different speeds, meaning that some types become resistant faster than others. We are checking this by giving these different types of bug more and more antibiotics, until they die. We think that those types that live for longer are able to develop resistance faster, which is interesting.
- My project focuses on the DNA of bacteria and how we can find areas on the DNA that are possible targets for new drugs. We have a need for these new drugs, because we used the old ones too much and now the bacteria have gotten used to them. My computer program will find very specific areas on the DNA that (hopefully) only occur in a few bad bacteria, so we can target them instead of a wide range of bacteria, which is also bad for us.
- I work with my computer and use the information of sequenced DNA - as it was done with the covid variants, to understand which bacteria infect more than others, and use this to prevent further infections.
- Essential genes are required for bacteria to survive and grow under different conditions. Bacteria can die or cannot grow properly if they loss those genes. Using ML* we can predict which gene is essential for bacteria but it is not an easy task. Genes essentiality is context-dependent; we do not have a big amount of labelled data (correct answers for computer) and we do not know which properties of genes make it essential. I build model that can learn genes properties and predict whether they are essential. *ML (black box for fortune telling)
- Bacteria can fine-tune their behaviour to adapt to different environments. But how do they do this? Most research have focused on changes within specific regions known to carry information about these “behaviour”. But what if there is more to the story? My work looks into the surprising role overlooked regions of the bacterial DNA have in regulating how their genes are expressed and how it might affect their response to some drugs used to kill them.
- Antibiotic resistance is the most burning issue in public health, people try to combat this issue by several method, including by trying to find the mechanism. One of them is how bacteria can spread antibiotic resistance genes. My project aims to create synthetic bacterial communities, which can detect antibiotic resistance genes spread among the communities
- In my internship, I learned how to find sections in the DNA of bacteria, that are responsible for making them more likely to cause infections. With this method, one could also try to understand, why infections with some bacteria are worse than others. I also learned how to evaluate the findings by performing experiments in the laboratory.
This exercise was definitely easier than the previous one, and hopefully easier to follow as a reader.
Dilfuza’s definition of machine learning (“black box for fortune telling”) was particularly funny!
However, you may notice how we all ended up writing a longer piece of text now that we could use any word we liked.
We therefore tried one more time with the requirement being that we needed to write a single sentence of maximum 100 characters
(spaces excluded):
- We study how different bugs develop resistance to drugs by giving them more until they die and comparing how long it took
- I use a computer to find specific areas on DNA of bad bacteria, for which we can make new drugs that target these areas
- Use genome data to understand how bacteria behave and prevent transmission and infection
- I make a computer to learn which genes of bacteria are important for their survival that help us to kill them efficiently
- I study how bacteria induce changes within their DNA to adapt to environments and cause antimicrobial resistance
- By evolving synthetic bacterial communities with antibiotics, I aim to detect antibiotic resistance genes spread
- I learnt computational and laboratory methods to find changes in DNA of germs that alter their potential in infections
This last excersize is probably still difficult for a LLM to master, and we think we did a pretty good job!
The machines are not getting us fired yet, and we have an incentive to be more concise and direct.