Koyena Pal

@koyena.bsky.social

CS Ph.D. Candidate @ Northeastern | Interpretability + Data Science | BS/MS @ Brown koyenapal.github.io

Can you solve this algebra puzzle? 🧩 cb=c, ac=b, ab=? A small transformer can learn to solve problems like this! And since the letters don't have inherent meaning, this lets us study how context alone imparts meaning. Here's what we found:🧵⬇️

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How can a language model find the veggies in a menu? New pre-print where we investigate the internal mechanisms of LLMs when filtering on a list of options. Spoiler: turns out LLMs use strategies surprisingly similar to functional programming (think "filter" from python)! 🧵

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What's the right unit of analysis for understanding LLM internals? We explore in our mech interp survey (a major update from our 2024 ms). We’ve added more recent work and more immediately actionable directions for future work. Now published in Computational Linguistics!

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[📄] Are LLMs mindless token-shifters, or do they build meaningful representations of language? We study how LLMs copy text in-context, and physically separate out two types of induction heads: token heads, which copy literal tokens, and concept heads, which copy word meanings.

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🚀 How would you know what model to use? 🤗 With millions of models emerging rapidly, how do we verify, track, and find the right one? We survey and formalize Model Lakes 🌊🤖 — a framework to structure, navigate, and make sense of this landscape. Website: lakes.baulab.info #AI #Database 🧵1/5

Model Lakes Design. A model lake stores models and processes them using techniques, like inference, interpretability, weight-space modeling and indexing to support various user interactions. It generates outputs like version graphs, model cards and ranked models, refining them into human-readable results, as shown on the figure's right side.

More big news! Applications are open for the NDIF Summer Engineering Fellowship—an opportunity to work on cutting-edge AI research infrastructure this summer in Boston! 🚀

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