Daniel Wurgaft
@danielwurgaft.bsky.social
PhD @Stanford working w @noahdgoodman and research fellow @GoodfireAI
Studying in-context learning and reasoning in humans and machines
Prev. @UofT CS & Psych
Studying in-context learning and reasoning in humans and machines
Prev. @UofT CS & Psych
Intuitively, what does this predictive account imply? A rational tradeoff between a strategy's loss and complexity!
🔵Early: A simplicity bias (prior) favors a less complex strategy (G)
🔴Late: reducing loss (likelihood) favors a better-fitting, but more complex strategy (M)
8/
🔵Early: A simplicity bias (prior) favors a less complex strategy (G)
🔴Late: reducing loss (likelihood) favors a better-fitting, but more complex strategy (M)
8/
June 28, 2025 at 2:35 AM
Intuitively, what does this predictive account imply? A rational tradeoff between a strategy's loss and complexity!
🔵Early: A simplicity bias (prior) favors a less complex strategy (G)
🔴Late: reducing loss (likelihood) favors a better-fitting, but more complex strategy (M)
8/
🔵Early: A simplicity bias (prior) favors a less complex strategy (G)
🔴Late: reducing loss (likelihood) favors a better-fitting, but more complex strategy (M)
8/
Fitting the three free parameters of our expression, we see that across checkpoints from 11 different runs, we almost perfectly predict *next-token predictions* and the relative distance maps!
We now have a predictive model of task diversity effects and transience!
7/
We now have a predictive model of task diversity effects and transience!
7/
June 28, 2025 at 2:35 AM
Fitting the three free parameters of our expression, we see that across checkpoints from 11 different runs, we almost perfectly predict *next-token predictions* and the relative distance maps!
We now have a predictive model of task diversity effects and transience!
7/
We now have a predictive model of task diversity effects and transience!
7/
We assume two well-known facts about neural nets as computational constraints (scaling laws and simplicity bias). This allows writing a closed-form expression for the posterior odds!
6/
6/
June 28, 2025 at 2:35 AM
We assume two well-known facts about neural nets as computational constraints (scaling laws and simplicity bias). This allows writing a closed-form expression for the posterior odds!
6/
6/
We first define Bayesian predictors for ICL settings that involve learning a finite mixture of tasks:
🔴 Memorizing (M): discrete prior on seen tasks.
🔵 Generalizing (G): continuous prior matching the true task distribution.
These match known strategies from prior work!
2/
🔴 Memorizing (M): discrete prior on seen tasks.
🔵 Generalizing (G): continuous prior matching the true task distribution.
These match known strategies from prior work!
2/
June 28, 2025 at 2:35 AM
We first define Bayesian predictors for ICL settings that involve learning a finite mixture of tasks:
🔴 Memorizing (M): discrete prior on seen tasks.
🔵 Generalizing (G): continuous prior matching the true task distribution.
These match known strategies from prior work!
2/
🔴 Memorizing (M): discrete prior on seen tasks.
🔵 Generalizing (G): continuous prior matching the true task distribution.
These match known strategies from prior work!
2/
🚨New paper! We know models learn distinct in-context learning strategies, but *why*? Why generalize instead of memorize to lower loss? And why is generalization transient?
Our work explains this & *predicts Transformer behavior throughout training* without its weights! 🧵
1/
Our work explains this & *predicts Transformer behavior throughout training* without its weights! 🧵
1/
June 28, 2025 at 2:35 AM
🚨New paper! We know models learn distinct in-context learning strategies, but *why*? Why generalize instead of memorize to lower loss? And why is generalization transient?
Our work explains this & *predicts Transformer behavior throughout training* without its weights! 🧵
1/
Our work explains this & *predicts Transformer behavior throughout training* without its weights! 🧵
1/
Excited to share a new CogSci paper co-led with @benpry.bsky.social!
Once a cornerstone for studying human reasoning, the think-aloud method declined in popularity as manual coding limited its scale. We introduce a method to automate analysis of verbal reports and scale think-aloud studies. (1/8)🧵
Once a cornerstone for studying human reasoning, the think-aloud method declined in popularity as manual coding limited its scale. We introduce a method to automate analysis of verbal reports and scale think-aloud studies. (1/8)🧵
June 25, 2025 at 5:00 AM
Excited to share a new CogSci paper co-led with @benpry.bsky.social!
Once a cornerstone for studying human reasoning, the think-aloud method declined in popularity as manual coding limited its scale. We introduce a method to automate analysis of verbal reports and scale think-aloud studies. (1/8)🧵
Once a cornerstone for studying human reasoning, the think-aloud method declined in popularity as manual coding limited its scale. We introduce a method to automate analysis of verbal reports and scale think-aloud studies. (1/8)🧵