Papers by Michael Lan
Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models (2024.emnlp-main)
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| Challenge: | Recent work aims to reverse engineer transformer models into human-readable representations . transformers exhibit strong capabilities on linguistic tasks, but their complex architectures make them difficult to interpret. |
| Approach: | They extend transformer models into human-readable representations that implement algorithmic functions by analyzing sequence continuation tasks. |
| Outcome: | The proposed model can be reverse-engineered into human-readable representations that implement algorithmic functions. |
Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)
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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel
| Challenge: | Currently, the Transformer is the de facto architecture of choice for processing sequential data. |
| Approach: | They evaluate the Transformer architecture and its modifications in a shared experimental setting . they conjecture that performance improvements may strongly depend on implementation details . |
| Outcome: | The proposed improvements do not significantly improve performance, the authors find . the proposed improvements are either developed in the same codebase or are minor changes . |
Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing (2026.acl-long)
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Michael Lan, Narmeen Fatimah Oozeer, Chaithanya Bandi, Philip Quirke, Austin Meek, Fazl Barez, Amir Abdullah
| Challenge: | a recent paper found conflicting conclusions for the same behavior in a neural network . authors propose auditing MI itself is essential for its application in AI safety, industry, and governance . |
| Approach: | They propose to develop a system that can audit experiments to ensure validity . authors propose to generalize good practices found on platform into expert-verified guidelines . |
| Outcome: | a new review system could be developed that can be standardized and audited . authors argue that auditing MI is essential for its application in AI safety, industry, and governance . |