Hi, I'm Jin, a 3rd year PhD student at UCL, supervised by Andrew Saxe.
Broadly, my research focuses on understanding of learning in neural networks and their intelligent behaviors. How does the structure of data and the inductive biases of models shape learning dynamics? Particularly, how compositional structure of the tasks (such as skill composition) or data (such as language) might emerge from this interplay. I take multiple approaches including theory based on a simple tractable model and tools from statistical physics, and empirical studies of large language models.
Using those approaches, my current projects are revolving around understanding the reasoning capabilities of large language models through the lens of compositionality.
Recently, I've been more committed to be involved in the pressing problems revolving AI capability and alignment. I'm seeking mentorship and community in AI safety research, driven by thoughts that it is a critical challenge that requires the action now and it is where my interest and skills can make a meaningful contribution.
You can reach me via: jin dot lee dot 22 at ucl dot ac dot uk
Broadly, my research focuses on understanding of learning in neural networks and their intelligent behaviors. How does the structure of data and the inductive biases of models shape learning dynamics? Particularly, how compositional structure of the tasks (such as skill composition) or data (such as language) might emerge from this interplay. I take multiple approaches including theory based on a simple tractable model and tools from statistical physics, and empirical studies of large language models.
Using those approaches, my current projects are revolving around understanding the reasoning capabilities of large language models through the lens of compositionality.
Recently, I've been more committed to be involved in the pressing problems revolving AI capability and alignment. I'm seeking mentorship and community in AI safety research, driven by thoughts that it is a critical challenge that requires the action now and it is where my interest and skills can make a meaningful contribution.
You can reach me via: jin dot lee dot 22 at ucl dot ac dot uk
News
• I'm invited to give a talk at COSYNE 2025 workshop on compositionality. See you in
Mont-Tremblant! (2025 Apr)
• I'm invited to visit UPF computational linguistics group led by Marco Baroni, digging up more
linguistic compositionality in LLMs! (2025 Feb)
• I'm giving a tutorial on Theoretical "Advances in Continual Learning" at COLLAS 2024, in
Pisa. (2024 July)
Research
Distinct Computations Emerge From Compositional
Curricula in In-Context Learning
JH. Lee, A. Lampinen, A. Singh* and A. Saxe*
Workshop on Spurious Correlation and Shortcut
Learning, ICLR 2025
- Training transformer architecture to learn compositional algorithmic task from scratch.
- Curricula-like data structure given in-context learning impacts models' strategy choice.
Geometric Signatures of Compositionality Across a
Language Model's Lifetime
JH. Lee*, T. Jiralerspong*, L. Yu, Y. Bengio and E.
Cheng
Under review
- Analyzing geometric properties of hidden representations in LLMs throughout pretraining and how does compositional structure of language is reflected and correlated to the linguistic capability.
Range, not Independence, Drives Modularity in Biologically
Inspired Representations
W. Dorrell*, Hsu. K*, L. Hollingsworth, JH. Lee, J. Wu, C.
Finn, PE. Latham, T. Behrens and TEJ. Whittington
ICLR 2025
- Deriving necessary and sufficient conditions on sample data statics to gain modular representation with biological neural constraints.
