Vector Quantized Latent Concepts: New Method for Interpreting Large Language Models
Researchers have developed Vector Quantized Latent Concept (VQLC), a new framework for extracting and understanding semantic concepts from the hidden states of large language models. The method addresses computational limitations of existing clustering approaches by offering better scalability than hierarchical clustering while maintaining competitive interpretability with K-Means. This advance could improve transparency and understanding of how LLMs process and encode information internally.
A new machine learning framework called Vector Quantized Latent Concept (VQLC) has been proposed to extract interpretable concepts from the hidden states of large language models. The approach learns a codebook of discrete latent concepts on frozen hidden states, addressing a key limitation of existing methods: hierarchical clustering produces coherent concepts but has prohibitive memory costs for large datasets, while K-Means scales efficiently but may sacrifice semantic coherence. Testing across 12 different dataset-model configurations shows that VQLC maintains computational efficiency comparable to K-Means while scaling better than hierarchical clustering and remaining competitive in faithfulness metrics. The researchers validated their approach through LLM-based evaluation, qualitative analysis, and comparison with Sparse Autoencoders, demonstrating that the learned concepts are both interpretable and relevant to downstream tasks, with particularly strong performance on decoder-only model architectures.
What's missing
The paper does not discuss potential limitations of the VQLC approach, such as sensitivity to hyperparameter choices (codebook size, quantization parameters), generalization to other model architectures beyond those tested, or how the method performs on out-of-distribution data. Additionally, the practical applicability of the discovered concepts for improving model performance or safety is not addressed.
What different sources said
- arXiv cs.LGCenter
Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
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