Reflections on using LLMs to read a paper

The concept and reflections are good, but without a link to the code/tool or a any example outputs it’s hard to evaluate how well the implementation works, and the value add compared to just reading the paper.

I agree that there is a lot of untapped value in using LLMs to accelerate alignment research, as the project states. I have had similar positive experiences using Claude UI to aid with understanding a paper quickly, and I like the honest exploration of the value provided by this in the project. I would be excited to see a prototype of an application that focuses on the key points and summarises context and then only expands on the points if requested with quotes from the paper and explanations.

I think your clarity on the problem space is excellent, and would love to see any work in future that you do in this area. If you’re not already aware then I’d recommend checking out Elicit, a tool which aims to solve at least some of the problems you identify here.

Very good thoughts on what could and should be done. Given that the potential scope of this is pretty big, I would be curious what the MVP could look like here: What is the easiest significant value-add compared to just reading a paper in a PDF reader that we could aim for?

I think we should definitely figure out how to make LLMs optimal for helping researchers getting the most value they can out of reading an academic paper. To make it optimal for alignment, we should probably design AI agents/prompts that take into account the key questions in AI alignment and the important things we want to keep in mind when extracting insights from a paper. There are many things that researchers know, but it’s hard to keep track of everything (how does it relate to x, y, z project? how does it relate to these two open questions in alignment?). I think that if we’re hyper-specific about niche areas in alignment (SAEs, scalable oversight via weak to strong generalization, etc), we could potentially make the LLMs capable of generating much better readings of papers rather than just prompting the LLM raw and simply asking for a summary.Thank you for sharing your insights of the hackathon!