I defended my PhD at the University of Copenhagen ☺️ What a journey! I want to give massive thanks to my amazing supervisors, @iaugenstein.bsky.social and @neuralnoise.com who were there with me throughout the whole process. Thesis on: osoblanco.github.io/thesis/ The Arxiv version is coming soon!

@dfdazac.bsky.social was an honor to work with someone as amazing as you. The line made me teary 🥹🥹♥️♥️

Hello bluesky! I'm using this first post to share that my PhD thesis is now available online at research.vu.nl/en/publicati... Thanks to all my collaborators who joined me in this journey!

I think given the current weird/awful state of how reviewing is handled in major ML venues we would explicitly need ranking the reviewers even if they are anonymous. This can help (S)ACs at least internally filter out malicious and unqualified ones. Will work on smth like this closer to ~ICML.

The main question about the current LLM “reasoning” research is what to do next. Most go into synthetic generation and training on maybe with self-Refinement in hopes the model becomes better. I think we are missing controlled task formalization, step by step reasoning and strict step verification.

My amazing collaborators will be presenting three papers next week at EMNLP 2024! I wrote a blog post about our EMNLP papers and some of the other projects we're brewing 🚀🙂 neuralnoise.com/2024/nov-res...

👋Psst! Want more faithful, verifiable and robust #LLM reasoning than with CoT, but using external solvers is meh? Our FLARE💫uses Logic Prog with Exhaustive Simulated Search to achieve this.🧵 @pminervini.bsky.social, Patrick Lewis, Pat Verga and @iaugenstein.bsky.social arxiv.org/abs/2410.11900

At #EMNLP2024 we will present our paper on LLM values and opinions! We introduce tropes: repeated and consistent phrases which LLMs generate to argue for political stances. Read the paper to learn more! arxiv.org/abs/2406.19238 Work done Uni Copenhagen + Pioneer Center for AI

Hey! 🙂 we analysed what happens during pre-training, and for causal LMs, intra-document causal masking helps quite a bit both in terms of pre-training dynamics and downstream task performance: arxiv.org/abs/2402.13991