Jennifer Hu

@jennhu.bsky.social

Asst Prof at Johns Hopkins Cognitive Science • Director of the Group for Language and Intelligence (glint) ✨• Interested in all things language, cognition, and AI jennhu.github.io

🤖🧠NEW PAPER🧠🤖 (The result of an 8-year project!) LLMs seem very different from symbolic systems. Yet LLMs excel in symbolic domains (e.g., language/code/math). How do they do it? Our finding: LLM representations have implicit symbolic structure! Link in thread ⬇️ 1/n

Overview of the paper. 
Title: The Emergent Symbolic Structure of Artificial Neural Networks
Authors: Tom McCoy, Paul Soulos, Tal Linzen, Paul Smolensky
Left: Neural networks encode information in vectors (there is then an image of a vector), yet they excel at tasks long thought to require symbolic structure (there is then an image of a symbolic representation, specifically a syntax tree). How do LLMs do it?
Right: We find that LLM representations can be closely approximated with symbolic structures. This approximation lets us edit the structure of an LLM’s output by editing the structure of its internal representations, as shown. There is then an image of two edits to LLMs. In the first one, the original input is 3 + 6 * 8, with an answer of 51. But if we swap the positions of the 3 and the 6, the output becomes 30. In the second one, the original input is a Python command repeating the list [Z, U] three times, producing [Z, U, Z, U, Z, U]. But if we edit the input in a way that adds a Q at the end of the input, the output becomes [Z, U, Q, Z, U, Q, Z, U, Q].

The full BBS treatment from me and @futrell.bsky.social on "How linguistics learned to stop worrying and love the LMs" is now out, with all the commentaries and our response. If you "Save PDF", it will give you the whole target article + commentary + response pdf: www.cambridge.org/core/journal...

How linguistics learned to stop worrying and love the language models | Behavioral and Brain Sciences | Cambridge Core

How linguistics learned to stop worrying and love the language models - Volume 49

cambridge.org

What's more nonsensical: smashing a pumpkin using a number, or growing flowers inside a sneeze? Our paper on graded inconceivability is out now in Cognition! Come for the cognitive science 🧠🔍, stay for the whimsy 🌼🧚! 🔗Journal link: bit.ly/gradedInconCog

Tomer Ullman@tomerullman.bsky.social · 3mo ago

Now out (for realz) in Cognition: "People Make Graded Judgments About The Inconceivable" (by Hu, Sosa, & me) Free preprint: www.tomerullman.org/papers/grade... Journal link: bit.ly/gradedInconCog @jennhu.bsky.social @cognitionjournal.bsky.social

Sadly won't be at ACL in person, but check out our presentations below! 🌟 I'm giving a (remote) keynote at SCiL on 7/4! 🌟We also have a poster on probability x grammaticality, and a talk on pragmatics x Theory of Mind! Our lab is actively recruiting, so please reach out! Details at glintlab.org ✨

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New work to appear @ TACL! Language models (LMs) are remarkably good at generating novel well-formed sentences, leading to claims that they have mastered grammar. Yet they often assign higher probability to ungrammatical strings than to grammatical strings. How can both things be true? 🧵👇

Screenshot of a figure with two panels, labeled (a) and (b). The caption reads: "Figure 1: (a) Illustration of messages (left) and strings (right) in toy domain. Blue = grammatical strings. Red = ungrammatical strings. (b) Surprisal (negative log probability) assigned to toy strings by GPT-2."

It’s grad school application season, and I wanted to give some public advice. Caveats: -*-*-*-* 
> These are my opinions, based on my experiences, they are not secret tricks or guarantees 
> They are general guidelines, not meant to cover a host of idiosyncrasies and special cases

Interested in doing a PhD at the intersection of human and machine cognition? ✨ I'm recruiting students for Fall 2026! ✨ Topics of interest include pragmatics, metacognition, reasoning, & interpretability (in humans and AI). Check out JHU's mentoring program (due 11/15) for help with your SoP 👇

JHU Cognitive Science@jhucogsci.bsky.social · 11mo ago

The department of Cognitive Science @jhu.edu is seeking motivated students interested in joining our interdisciplinary PhD program! Applications due 1 Dec Our PhD students also run an application mentoring program for prospective students. Mentoring requests due November 15. tinyurl.com/2nrn4jf9

Call for applications to cognitive science PhD program with QR code to the link above

Heading to CogSci this week! ✈️ Find me giving talks on: 💬 Prod-comp asymmetry in children and LMs (Thu 7/31) 💬 How people make sense of nonsense (Sat 8/2) 📣 Also, I’m recruiting grad students + postdocs for my new lab at Hopkins! 📣 If you’re interested in language / cognition / AI, let’s chat! 😄

Excited to share a new preprint w/ @michael-lepori.bsky.social & Michael Franke! A dominant approach in AI/cogsci uses *outputs* from AI models (eg logprobs) to predict human behavior. But how does model *processing* (across layers in a forward pass) relate to human real-time processing? 👇 (1/12)

Screenshot of Figure 1, which has two panels labeled (a) and (b). The caption states the following. Figure 1: Overview of our study. (a) Experiment 1: We explore whether forward passes show mechanistic signatures of competitor interference, first preferring a salient competing intuitive answer before preferring the correct answer. (b) Experiment 2: We systematically investigate the ability of dynamic measures derived from forward passes to predict indicators of processing load in humans.

Check out our new work on introspection in LLMs! 🔍 TL;DR we find no evidence that LLMs have privileged access to their own knowledge. Beyond the study of LLM introspection, our findings inform an ongoing debate in linguistics research: prompting (eg grammaticality judgments) =/= prob measurement!

Siyuan Song@siyuansong.bsky.social · 2y ago

New preprint w/ @jennhu.bsky.social @kmahowald.bsky.social : Can LLMs introspect about their knowledge of language? Across models and domains, we did not find evidence that LLMs have privileged access to their own predictions. 🧵(1/8)

Some things are more impossible than others. But some things might be even *more impossible* than impossible. (How) do people differentiate between the inconceivable and the merely impossible? Do language models also make similar distinctions? Check out our new preprint below!

Tomer Ullman@tomerullman.bsky.social · 2y ago

The Red Queen believed "6 impossible things before breakfast." But what about *inconceivable* things? For your breakfast read, check out the new preprint: "Shades of Zero: Distinguishing Impossibility from Inconceivability" (by @jennhu.bsky.social , Sosa, & me) arxiv: arxiv.org/pdf/2502.20469