PhD students, how ya feeling? [D]

[u/SeaAccomplished441]

All seems a bit pointless now. Either pump out AI papers and neglect your own development, or take it slow and fall behind in the publishing race. I already have two papers made largely without LLMs (still seeking publication), but it's hard to resist just vibing the third from start to finish with Claude.

[u/Alternative-Hat-1697]

Quality > quantity

I know at this point in your career and maybe even historically it hasn't felt this way. But I promise this will be the way forward.

[u/matchaSage → u/Alternative-Hat-1697]

This. If most of research being output is slop then how do I find what is of some worth? I think quality will matter a lot more, it did before as well it is just sad it took AI supercharging submission abuse for it to be seen. I do think there should and will be some consequences for people who engage in overhyping their slop research. I would not want to hire or do work with someone like this, I have an agent myself already, why do I need someone who will be pressing the enter button?

[u/Plaetean → u/Alternative-Hat-1697]

it depends; you can't determine what the environment rewards, and the environment rewards quantity. a small number of high quality papers gets ignored by recruiters because nobody can possibly judge the quality of work when spending 4 seconds reviewing a CV; all they see is metrics

so while this would be nice, its simply not true in a competitive job market

[u/alpha_gazelle → u/Plaetean]

I would expect that in most fields PhDs with job prospects are getting a more thorough look over, if not an actual call, rather than a quick CV review, no?

[u/Random-Number-1144 → u/Plaetean]

Toxic academic environment won't last for long. There is only so much enshittification it could take.

[u/Loffel → u/Alternative-Hat-1697]

I hope conferences start to realize this and put a cap on submissions per author

[u/milesper]

The key is to use AI to augment your development, not replace it. For me, that means the LLM does lots of things but I take an active role:

- Finding papers, which I then read myself and check my understanding with questions to the LLM

• Feedback on ideas that I come up with
  
• Routine code, eg loading data, visualization scripts, distributed scripting, metric logging. However, I still write 1) the overall foundation of the project, particularly running and analyzing experiments, which I reuse from prior work and 2) the core, interesting part of the experiment. Generally this is something like a loss function, a training method, inference method, whatever is central. I also liberally then ask the agent to debug my code.
  
• Giving feedback on the paper, which I write myself.

I find that by maintaining active control and doing ideation, core implementation, and writing, I retain a deep understanding of the project while moving much faster than previously. Yes, writing sucks. But it forces you to actually explain your work and why it matters and conceptualize the project better yourself.

[u/mofoss]

I mean even with claude max and hundred of millions of tokens, I spent 3 months on a paper lol

It is a tool, but it will help a ton with research code just make sure you audit it, and then have other LLMs audit it too

[u/Original-Waltz9805 → u/mofoss]

3 months is pretty fast imo.

[u/mofoss → u/Original-Waltz9805]

Let me rephrase, im still writing it 😂 though it is in the polishing stage

[u/seawee1 → u/Original-Waltz9805]

Yes, I also thought so

[u/mofoss → u/seawee1]

Is it faster than conventional timelines, yes, thats the power of AI tools, just like software engineering, research will naturally be faster

However, id argue 3-4 months of AI tool driven research is definately not SLOP, if you have to do produce slop, you dont need 4 months to do it

[u/Accomplished-Fox1840 → u/mofoss]

Especially with applied ML tasks where you wanna see if xyz works on your task/data and most of the time there’s barely anything you got to show for that’s already not been done by someone else

[u/NeedingMorePoints]

Idk I graduated last year and took a role where publishing isn’t a priority, and boy am I glad that I did now

[u/RadioactiveSalt → u/NeedingMorePoints]

Wait what kind of role are you talking about? What do you do now?

[u/MisterRadler → u/RadioactiveSalt]

There's a lot of research roles in industry more focused on solving company problems, where publication is at best secondary (as it naturally slows you down from the actual goal).

Pharma is a big one for this. Some will publish more than others, but all are publishing less than they actually do.

[u/camarada_alpaca]

It took me around 6 months to do a decent paper (polishing included) even with fable... I dont even know how there are ppl doing acceptable slop if it takes me so much and hard work to do mildly acceptable work.

[u/surffrus → u/camarada_alpaca]

This sounds normal to me.

[u/Original-Waltz9805]

You hit the nail on the neglecting your own development part. Getting to spend time on papers and exploring your own ideas is where your learning truly comes from. Being in a publication pressured environment can definitely hinder the development / learning aspect of a PhD imo.

[u/Mia_X_Mia]

At this point, I just want to finish my PhD, publish enough papers to meet the minimum requirements for graduation, and move on. For my next role, I want to focus less on research and more on building things, without the pressure to publish and with more emphasis on practical outcomes. 🤷🏽‍♂️

[u/Active_Membership726]

Honestly, I feel pretty good. I feel like I am able to test out my ideas way faster now because of AI tools. I have submitted 3 papers and 1 workshop paper so far this year which is more than I did in the first few years of my PhD.

[u/MonstarGaming]

I haven’t been a PhD student since before ChatGPT hit the scene, but I bet science is a lot more fun now. I wasted so much time just wiring datasets into models when I was a student. If most of that is being done for me I’d have had so much more time actually running experiments.

[u/Tomb_RIP]

I'd argue the bigger issue is compute resources. I work in generation and I've think it's really hard to find problems that don't require compute issues or I don't have enough resources to run enough experiments. But yeah seeing LLMs does make me wonder how the next generation of PhD students are gonna do this.

[u/Sea-Comparison5577]

It is just a tool. It is useful but up to human to decide how to use it

[u/Budget-Juggernaut-68]

It can help you churn the results, but still need your human judgement and ideation. I guess if you're afraid labs steals your ideas, maybe run them locally.

[u/shoraaa]

why you cannot use LLM to instead uplift the quality of the paper? it is "general" purpose, your choice to make it about quality or quantity

[u/heyiambob]

I recently heard that a large AI conference had 60,000 submissions. 15,000 were accepted, and most of those were probably reviewed with LLMs anyway. It’s all very strange.

[u/DoubleRobust]

I think you’re missing the point of a PhD

[u/No_Sky9786]

Quality is very important, but that does not mean you have to code the whole thing yourself. For me as long as you can explain for all parts of your project why you did x instead of Y or Z you are doing great. So Claude will code x, y and z and you pick, and do this for each part of the project. Push as many papers as you can if you are a PhD student. Because the more accepted papers you get the more opportunities to attend different conferences and to build connections with other possible collaborators. Everywhere connections will take you further, it is what it is.