Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
arXiv:2602.02726v2 Announce Type: replace-cross Abstract: Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information these internal representations capture. Latent concepts extracted from hidden states offer a promising direction for interpreting LLMs, but existing clustering-based methods face a trade-off: hierarchical clustering produces coherent concepts but is limited to small datasets due to its quadratic memory cost, while K-Means scales efficiently but may yield less semantically coherent concepts. We pro
This research addresses fundamental limitations in current AI interpretability methods, specifically the trade-off between concept coherence and scalability in LLM analysis.
Improving the interpretability of large language models is crucial for their responsible and effective deployment across critical applications, enhancing trust and enabling better debugging and control.
The proposed Vector Quantized Latent Concepts (VQLC) method offers a more scalable and coherent approach to understanding the internal workings of LLMs, potentially accelerating progress in AI safety and alignment.
- · AI researchers
- · Developers of interpretability tools
- · Industries deploying LLMs
- · N/A
More efficient and interpretable LLMs will lead to faster development cycles and broader adoption.
Enhanced LLM interpretability could reduce regulatory hurdles and foster greater public trust in AI systems.
A deeper understanding of LLM internal representations may unlock novel architectures or training paradigms.
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