Y’all, I am at #COLM this week, very excited to learn, and meet old and new friends. Please reach out on Whova!

Plus, we generate importance maps showing where in the transformer the concept is encoded — providing interpretable insights into model internals.

SAMI: Diminishes or amplifies these modules to control the concept's influence

With SAMI, we can scale the importance of these modules — either amplifying or suppressing specific concepts.

SAMD: Finds the attention heads most correlated with a concept

Using SAMD, we find that only a few attention heads are crucial for a wide range of concepts—confirming the sparse, modular nature of knowledge in transformers.

How would you make an LLM "forget" the concept of dog — or any other arbitrary concept? 🐶❓

We introduce SAMD & SAMI — a novel, concept-agnostic approach to identify and manipulate attention modules in transformers.

Aligned Multi-Objective Optimization (A-🐮) has been accepted at #ICML2025! 🎉
We explore optimization scenarios where objectives align rather than conflict, introducing new scalable algorithms with theoretical guarantees. #MachineLearning #AI #Optimization

🎉🎉 Our paper just got accepted to #ICLR2025! 🎉🎉

Byte-level LLMs without training and guaranteed performance? Curious how? Dive into our work! 📚✨

Paper: arxiv.org/abs/2410.09303
Github: github.com/facebookrese...

Pro-tip: Use massive black Friday deals at scientific publishing houses to for example buy a copy of @jmtomczak.bsky.social
book on generative modeling (long overdue)

What do you think do we need to sharpen our understanding of tokenization? Or will we soon be rid of it by developing models such as "MegaByte" by
Yu et al?
And add more paper to the threat!

Phan et al, found a method to mitigate some of the tokenization problems Karpathy mentioned by projecting tokens into byte space. The key to their method is to develop a map between statistically equivalent token and byte-level models.

In "The Foundations of Tokenization:
Statistical and Computational Concerns", Gastaldi et al. try to make first steps towards defining what a tokenizer should be and define properties it ought to have.

In "Toward a Theory of Tokenization in LLMs" Rajaraman et al., the authors discuss why we can think of tokenization to cause lower perplexity/ a better entropy bound.

A must watch entry point is @karpathy.bsky.social hy's "Let's build the GPT Tokenizer" video, where he discusses some tokenization problems.

🎉 Exciting News! 🎉
Two papers have been accepted at #NeurIPS2024 ! 🙌🏼 These papers are the first outcomes of my growing focus on LLMs. 🍾 Cheers to Nikita Dhawan and Jingtong Su + all involved collaborators: @cmaddis.bsky.social Leo Cotta, Rahul Krishnan, Julia Kempe