A framework for analyzing concept representations in neural models

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Speech Recognition · Depth: Expert, quick

Summary

A new unified framework is introduced for analyzing concept representations within neural models, addressing the challenge of understanding how these models encode human-interpretable concepts. This framework evaluates concept subspaces along two axes: "containment", which assesses if a concept is fully represented within a subspace and not outside it, and "disentanglement", which tests for isolation from other concepts. Experiments on both text and speech models reveal that concept subspaces may not be uniquely determined, and the choice of estimator significantly impacts containment and disentanglement properties. The concept erasure method LEACE performs well on both axes but struggles to generalize to unseen data. In HuBERT speech representations, phone information is both contained and disentangled from speaker information, while speaker information is hard to contain compactly despite being disentangled from phones.

Key takeaway

For machine learning engineers analyzing concept representations in neural models, you should adopt a dual-axis evaluation approach using "containment" and "disentanglement" to thoroughly assess subspace quality. Be aware that concept subspaces may not be uniquely determined, and your choice of estimator significantly impacts results. While LEACE is effective for concept erasure, plan for its generalization limitations on novel data. This framework provides a robust method for understanding and improving model interpretability.

Key insights

A unified framework analyzes neural model concept representations via "containment" and "disentanglement" axes.

Principles

• Concept subspaces are not always unique.
• Estimator choice affects subspace properties.
• LEACE struggles with generalization to unseen data.

Method

The framework studies concept subspaces by testing "containment" (concept fully in subspace, not outside) and "disentanglement" (isolation from other concepts).

In practice

• Evaluate concept subspaces using containment and disentanglement.
• Consider LEACE for concept erasure, noting generalization limits.
• Analyze phone/speaker information separation in HuBERT.

Topics

• Neural Models
• Concept Representations
• Subspace Analysis
• Disentanglement
• LEACE
• HuBERT

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.