Papers by Victor Shao
Interpretable Company Similarity with Sparse Autoencoders (2025.acl-industry)
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Marco Molinari, Victor Shao, Luca Imeneo, Mateusz Mikolajczak, Abhimanyu Pandey, Vladimir Tregubiak, Sebastião Kuznetsov Ryder Torres Pereira
| Challenge: | Traditionally, company comparisons rely on relative returns and discrete classifications, or a combination of both. |
| Approach: | They propose to use clusters of embeddings to enhance the interpretability of Large Language Models by decomposing Large Language models activations into interpretable features. |
| Outcome: | The proposed clusters of embeddings capture the internal representation of a company description, rather than just semantic similarity alone. |