Prologue

So recently while working on my finetuning project, I came across the #1 paper of the day on Hugging Face then: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights - Liang et al., 2025. At first I didn’t care too much, like I just brushed through it how you would a social media post, but then, I went over a few comments under the Hugging Face post, one particular thread caught my eye:

Flying Cougar:
*where to find so many users to upvote? curious...*
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|   Adventurous Author:
|   *Maybe many people like the idea of customizing LLMs in seconds?*
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|   Flying Cougar:
|   *Dont think so ...*

This immediately made me more intrigued about the paper, especially considering my recent experience with how not all “research” in this LLM space is genuine and usually are just hype or biased by the publish-or-perish culture, I decided to take a deeper look into it. So, in this post, I’m going to talk about this paper, point out its technicalities, novelty, and limitations-and whether this “drag-and-drop” proposal actually holds up, hope you enjoy it, I welcome any constructive discussion.

Table of Content

Core Innovation

This paper introduces a novel approach to the field of finetuning LLMs for specialized tasks. It might even be the first paper to really explore this idea-generating direct LoRA weights based on unlabeled prompts. So from what I understand, its like, say you are collecting a dataset for a text-to-SQL task like:

Sample 1:
    SQL Context
    Natural Language Question
    Label: SQL Query

...

Sample n:
    SQL Context
    Natural Language Question
    Label: SQL Query

Then use this dataset to finetune an LLM via either LoRA or QLoRA, which could take hours or even days, not to mention all the weird quirks like numerical stability when it comes to full-finetuning.

The paper insights draw on the fact that, LoRA actually freezes the underlying LLMs, what matters are the LoRA adapters (which are just matrices). They argue that these adapters are just a function of the training data/finetuning dataset, so, what if we could learn a mapping between the dataset and the adapters-that would bypass gradient-based finetuning entirely.

In order to test their theory, they collected many checkpoints that were obtained through actual full-finetuning with sets of sample -> LoRA adapters. Once they have these pairs, its essentially a supervised learning task, they optimized a neural network to predict from prompts to LoRA adapters on MSE loss.

The idea of using a neural network (NN.) to generate weights for another NN. is not new, in fact the generating NN. is formalized as a hyper-network, and in this case, their particular implementation is a Hyper Convolutional Decoder (HCD).

Also, they utilized an embedding model to be able to turn the prompts to numerical representation in order to feed it to the HCD. So now, they have an end-to-end model that can directly generate LoRA adapters from prompts, the “unlabeled” here basically mean that you dont have to give the pipeline any answer in your samples, for example, instead of:

Sample 1:
    SQL Context
    Natural Language Question

Sample 2:
    Law Question

...

Sample 3:
    Text that is tailored to a certain style

As you can also see, not only are they “unlabeled”, but you can also mix and match many prompts to achieve the most desirable result regarding your model, this kind of multi-task finetuning is notoriously hard to pull off. In the example above, you can see that this set of prompt could be used to get a model that is:

• Specialized in the legal domain.
• Improved SQL parsing capability.
• Answer like a lawyer, or some company.

As you can see, it almost seem like magic, which is why they propose this “drag-and-drop” mental model, which is quite similar to what it feels like, just “drag-and-drop” a couple of prompts and get yourself a finetuned LLM in seconds.

Key Limitations

So far, they seem to only have tested this with relatively small LLMs (could be classified as SLM-Small Language Model) under the 14B param count, most notably on Qwen 2.5 7B. Mind you, in order to obtain the hyper-network to generate the LoRA adapters in seconds, they still have to go through tons of full-finetuning runs to obtain the training for the HCD which still takes hours, days.

This fact especially make me doubt the feasibility for larger models, as LoRA on larger models are what really matters in production LLMs, which can take weeks-months. They state how they generalized even to prompts not in the training data obtained from the full LoRA runs, but that makes me very skeptical, considering how exponentially expensive full LoRA is when model size increases, this “generalization” needs rigorous examination.

A crucial point that one might brush over, is that, each HCD would most likely only be suitable to its family of model, every single model is different, both in terms of architecture and numerical attributes, and when bringing LoRA quirks into the mix, not exactly “drag-and-drop” anymore in my opinion.

Conclusion

This paper is actually quite refreshing in terms of the LLM field, they actually have a novel idea that they did test in a controlled environment and the results do seem promising. However, in order for this innovation to really be meaningful, we need to address the limitations mentioned and the implications of such points. Until then, their analogy of an UX concept: “drag-and-drop”, will continue to not sit right with me.

Epilogue

That was fun lol, no hate to the authors obviously, some of them are even from NUS, an institute that I respect deeply. Its just that, in the LLM and broader AI field, I do believe that we have to be vigilant and think critically of anything we come across, and in case of “Drag-and-drop LLMs”, perhaps its a bit of a stretch, but nothing as bad as ive seen before if we are talking about hype in this field.