Millicent Li

@millicent_li

cs phd @ northeastern | interpretability, evaluation, and training dynamics | formerly: @uwcse + @uwnlp, @meta fair, and @MSFTResearch

Cambridge, MA millicentli.github.io Joined November 2021

134 Following 291 Followers 99 Posts

Latest posts 5

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Millicent Li @millicent_li Jul 3

NLAs look like they "work"---but compared to what? How can we know that they actually tell us anything about model internals?

In my new blogpost (and paper, to be presented at ICML!), we investigate whether activation verbalizers (like NLAs) produce faithful explanations.

Anthropic Verified account @AnthropicAI May 7

New Anthropic research: Natural Language Autoencoders.

Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read.

Here, we train Claude to translate its activations into human-readable text.

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Millicent Li @millicent_li Oct 6

well-deserved award for @_emliu who really orchestrated all of this!! go to her talk, it'll be worth it! :)

Emmy Liu @_emliu Oct 6

Honoured to receive an outstanding paper award for our work! Couldn’t have done it without all my coauthors @KaiserWhoLearns @millicent_li @wordscompute @lltjuatja @JentseHuang and my advisor @gneubig 🫡
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Millicent Li @millicent_li Sep 9

highly recommend working with richard :) incredibly fun mentorship + I had a lot of fun as an undergrad at UW working on ideas that mattered to me! ASU is very lucky to have him

Richard Li @lichard49 Sep 8

I am excited to share that after 7 incredible years @uwcse , starting January 2027, I will join @ASU as an assistant professor and founding member of the School of Technology for Public Health with a joint appointment in @SCAI_ASU. 1/n

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Millicent Li @millicent_li May 20

How do we figure out whether a LM has learned the right skills, and in what order? We look at model internals :) Great work led by @_emliu!!

Emmy Liu @_emliu May 20

Copying → morphology/translation → basic arithmetic → complex reasoning & math. Across every model family we tested, LLMs acquire skills in roughly the same order during pretraining.

Can we use this to predict what a model will learn next, just from its internals? 🧵

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Millicent Li @millicent_li Sep 17, 2025

Wouldn’t it be great to have questions about LM internals answered in plain English? That’s the promise of verbalization interpretability. Unfortunately, our new paper shows that evaluating these methods is nuanced—and verbalizers might not tell us what we hope they do. 🧵👇1/9