Guy Davidson

Belated update #2: my year at Meta FAIR through the AIM program was so nice that I’m sticking around for the long haul.

I’m excited to stay at FAIR and work with @asli-celikyilmaz.bsky.social and friends on fun LLM questions; I’ll be working from the New York office so we’re sticking around.

Thank you, Ed!!

Tune in tomorrow for belated update #2, on post-PhD plans!

I owe tremendous thanks to many other people, all (or, hopefully, at least most) of whom I mentioned in my acknowledgments. I’m also so grateful my dad could represent my family, and for my wife, Sarah, for, well, everything.

Much, much larger thanks to my advisors, @brendenlake.bsky.social and @toddgureckis.bsky.social , for your guidance and mentorship over the last several years. I appreciate you so much, and this wouldn’t have looked the same without you!

Belated update #1: I defended my PhD about a month ago! I appreciate the warm reception from everyone who made it in-person and virtually. Thanks to my committee, @lerrelpinto.com, @togelius.bsky.social, and @markkho.bsky.social for your feedback and fun questions.

Friends and virtual acquaintances! I’m defending my PhD tomorrow morning at 11:30 AM ET. If anyone would like to watch, let me know and I’ll send you the Zoom link (and if you’re in NYC and feel compelled to join in person, that works, too!)

Wherever good coffee is to be found, the rest of the time. Don't hesitate to reach out!

(also happy to talk about job search in industry and what that looks and feels like these days)

Goal Inference using Reward-Producing Programs in a Novel Physics Environment Author(s): Davidson, Guy; Todd, Graham; Colas, CŽdric; Chu, Junyi; Togelius, Julian; Tenenbaum, Joshua B.; Gureckis, Todd M; Lake, Brenden | Abstract: A child invents a game, describes its rules, and ...

Saturday's poster session (P3-D-44) to talk about our goal inference work, in a new, physics-based environment we developed: escholarship.org/uc/item/6tb2...

Today's Minds in the Making: Design Thinking and Cognitive Science Workshop (Pacific E):

minds-making.github.io

#CogSci2025 friends! I'm here all week and would love to chat. I'd particularly love to talk to anyone thinking about Theory of Mind and how to evaluate it better (in both minds and machines, in different settings and contexts), and about goals and their representations. Find me at:

Cool new work on localizing and removing concepts using attention heads from colleagues at NYU and Meta!

You (yes, you!) should work with Sydney! Either short-term this summer, or longer term at her nascent lab at NYU!

Fantastic new work by @johnchen6.bsky.social (with @brendenlake.bsky.social and me trying not to cause too much trouble).

We study systematic generalization in a safety setting and find LLMs struggle to consistently respond safely when we vary how we ask naive questions. More analyses in the paper!

Guy Davidson Guy Davidson's academic website

Finally, if this work makes you think "I'd like to work with this person," please reach out -- I'm on the job market for industry post-PhD roles (keywords: language models, interpretability, open-endedness, user intent understanding, alignment).
See more: guydavidson.me

Do different prompting methods yield a common task representation in language models? Demonstrations and instructions are two primary approaches for prompting language models to perform in-context learning (ICL) tasks. Do identical tasks elicited in different ways result in similar rep...

If you made it this far, thank you, and don't hesitate to reach out! 17/N=17
Paper: arxiv.org/abs/2505.12075
Code: github.com/guydav/promp...

As with pretty much everything else I've worked on in grad school, this work would have looked different (and almost certainly worse) without the guidance of my advisors, @brendenlake.bsky.social and @toddgureckis.bsky.social . I continue to appreciate your thoughtful engagement with my work! 16/N

This work would also have been impossible without @adinawilliams.bsky.social 's guidance, the freedom she gave me in picking a problem to study, and believing in me that I could tackle it despite it being my first foray into (mechanistic) interpretability work. 15/N

We owe a great deal of gratitude to @ericwtodd.bsky.social d , not only for open-sourcing their code, but also for answering our numerous questions over the last few months. If you find this interesting, you should also read their paper introducing function vectors. 14/N

See the paper for a description of the methods, the many different controls we ran, our discussion and limitations, examples of our instructions and baselines, and other odd findings (applying an FV twice can be beneficial! Some attention heads have negative causal effects!) 13/N

Finding 5 bonus: Which post-training steps facilitate this? Using the OLMo-2 model family, we find that the SFT and DPO stages each bring a jump in performance, but the final RLVR step doesn't make a difference for the ability to extract instruction FVs. 12/N

Finding 5: We can steer base models with instruction FVs extracted from their post-trained versions. We didn't expect this to work! It's less effective for the Llama-3.2 models that are distilled and smaller. We're also excited to dig into this and see where we can push it. 11/N

Finding 4: The relationship between demonstrations and instructions is asymmetrical. Especially in post-trained models, the top attention heads for instructions appear peripherally useful for demonstrations, more than the opposite case (see paper for details). 10/N

We (preliminarily) interpret this as evidence that the effect of post-training is _not_ in adapting the model to represent instructions with the mechanism used for demonstrations, but in developing a mostly complementary mechanism. We're excited to dig into this further. 9/N.

Finding 3 bonus: examining activations in the shared attention heads, we see (a) generally increased similarity with increasing model depth, and (b) no difference in similarity between base and post-trained models (circles and squares). 8/N

Finding 3: Different attention heads are identified by the FV procedure between demonstrations and instructions => different mechanisms are involved in creating task representations from different prompt forms. We also see consistent base/post-trained model differences. 7/N

Finding 2: Demonstration and instruction FVs help when applied to a model together (again, with the caveat of the 3.1-8B base model) => they carry (at least some) different information => these different forms elicit non-identical task representations (at least, as FVs). 6/N

Finding 1: Instruction FVs increase zero-shot task accuracy (even if not as much as demonstration FVs increase accuracy in a shuffled 10-shot evaluation). The 3.1-8B base model trails the rest; we think it has to do with sensitivity to the chosen FV intervention depth. 5/N

TL;DR: We successfully extend FVs to ICL instruction prompts and extract instruction function vectors that raise zero-shot task accuracy. We offer evidence that they carry different information from demonstration FVs and are represented by mostly different attention heads. 4/N

We were inspired by @davidbau.bsky.social 's talk at NYU last fall, in which he discussed the function vector work led by @ericwtodd.bsky.social ‪‪‬ . They show how to extract task representations (= FVs) from ICL demonstrations. Could we extend FVs to instructions? What would we learn? 3/N