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  3. Cog Lunch: Cheng Tang
Cog Lunch: Cheng Tang
Department of Brain and Cognitive Sciences (BCS)

Cog Lunch: Cheng Tang

Add to CalendarAmerica/New_YorkCog Lunch: Cheng Tang03/04/2025 12:00 pm03/04/2025 1:00 pmBuilding 46,3310
March 4, 2025
12:00 pm - 1:00 pm
Location
Building 46,3310
    Description

    Zoom Link:  https://mit.zoom.us/j/99672193351

    Speaker: Cheng Tang

    Affiliation: Jazayeri lab, 4th year PhD candidate (system neuroscience)

    Title: An explainable transformer circuit for compositional generalization

    Abstract: Compositional generalization—the systematic combination of known elements into novel ensembles— is a hallmark of human cognition, enabling flexible problem-solving beyond rote memorization. While transformer models exhibit surprising proficiency in such tasks (Lake et al., 2023), the underlying mechanisms remain poorly understood. In this case study, we reverse-engineer how a transformer achieves compositional generalization at the circuit level, focusing on a function-primitive composition task. In this task, the model infers functions from teaching examples (e.g., interpreting “apple kiki → apple apple” to deduce that “kiki” means double) and generalizes them to new primitives (e.g., applying “kiki” to “tree” to produce “tree tree”). Our trained transformer achieves high test accuracy (~98%), demonstrating robust generalization.In the first half of the presentation, I will introduce the basics of transformer and provide an intuitive account on how attention operations perform information-routing between tokens with a slot-like data structure. Then I will present the human-interpretable algorithm implemented by the model, walk through the circuit discovery procedure, and highlight the correspondence between attention heads and the algorithm’s steps. Lastly, I will show causal perturbation experiments that validates the reverse-engineered circuit. This presentation aims to demystify the black-box impression of transformers to audience in neuroscience and invite discussion between model understanding and model control.

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