Papers by Paul Swoboda

2 papers
A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task (2024.findings-acl)

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Challenge: Existing studies do not provide insights into the internal mechanisms driving the observed abilities.
Approach: They propose to implement a depth-bounded recurrent mechanism that operates in parallel and stores intermediate results in selected token positions.
Outcome: The proposed model implements a depth-bounded recurrent mechanism that operates in parallel and stores intermediate results in selected token positions.
PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data (2026.acl-long)

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Challenge: Existing prompt generation methods are impractical in time and data constrained settings.
Approach: They propose a fast automatic prompt construction algorithm that augments human instructions by generating a small set of few shot examples.
Outcome: The proposed method outperforms existing prompting methods on classification, simplification, and MedQA.

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