Papers by Clement Neo
TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing text-to-SQL datasets are too complex and noisy for rigorous interpretability analysis. |
| Approach: | They propose text-to-SQL generation as an ideal task to study mechanistic interpretability . they use edge attribution patching and sparse autoencoders to identify minimal circuits . |
| Outcome: | The proposed task combines the formal structure of toy tasks with real-world complexity. |
Understanding Refusal in Language Models with Sparse Autoencoders (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a study of refusal in instruction-tuned language models identifies latent features that causally mediate refusal behaviors. |
| Approach: | They conduct a mechanistic study of refusal in instruction-tuned LLMs using sparse autoencoders . they identify latent features that causally mediate refusal behaviors using sparsed autoencoding . |
| Outcome: | The proposed method validates refusal-related features across multiple datasets. |
Spectra: A Mechanistic Interpretability Library for Vision-Language Models (2026.acl-demo)
Copied to clipboard
| Challenge: | Existing interpretability tools for visionlanguage models are limited to activation probing and saving. |
| Approach: | They propose a library specifically designed for mechanistic interpretability of visionlanguage models that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures. |
| Outcome: | The proposed library handles architecture-specific complexities while maintaining a simple, high-level interface. |
Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions (2024.emnlp-main)
Copied to clipboard
| Challenge: | Using a method to identify next-token neurons, we find that some attention heads recognize contexts relevant to predicting a token and activate a downstream token-predicting neuron accordingly. |
| Approach: | They propose a method to identify next-token neurons and determine the upstream attention heads responsible for their activity in LLMs. |
| Outcome: | The proposed method identifies next-token neurons, finds prompts that highly activate them, and determines the upstream attention heads responsible. |