Xiaoqing Sun — accepted AI-conference papers

2026

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

ICML 2026poster

Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors. Current methods often rely on costly LLM-based techniques (e.g. annotating dataset differences) or dense embedding models (e.g. for clustering), which lack cont…

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2025

Dense SAE Latents Are Features, Not Bugs

Xiaoqing Sun, Alessandro Stolfo, Joshua Engels, Ben Peng Wu, Senthooran Rajamanoharan, Mrinmaya Sachan +1

NeurIPS 2025poster

Sparse autoencoders (SAEs) are designed to extract interpretable features from language models by enforcing a sparsity constraint. Ideally, training an SAE would yield latents that are both sparse and semantically meaningful. However, many SAE latents activate frequently (i.e., are *dense*), raising…

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