Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models (2402.09236v2)

Abstract: To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate these two approaches and study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we formally define a notion of concepts and show that they can be provably recovered from diverse data. Experiments on synthetic data and LLMs show the utility of our unified approach.

• The paper presents a framework that unifies causal representation learning with foundation models by introducing human-interpretable affine subspace concepts.
• The methodology reduces the need for numerous environments, proving that n atomic concepts can be recovered using as few as n + 2 datasets.
• Experimental results demonstrate improved model alignment and interpretability, effectively steering large language models through inference-time interventions.

Unifying Causal Representation Learning with Foundation Models through Interpretable Concepts

Introduction to the New Framework

In recent developments, a novel framework has been proposed that aims to bridge the gap between Causal Representation Learning (CRL) and the empirical success of Foundation Models. This framework introduces a paradigm shift by focusing on learning identifiable, human-interpretable concepts from complex high-dimensional data without necessarily revealing the full underlying causal generative models. By adopting a more relaxed and practical viewpoint compared to traditional CRL, this framework aims to address some of the inherent limitations and challenges in interpreting foundation models.

Conceptual Formulation and Identifiability

Central to this framework is the formal definition of concepts as affine subspaces within a representation space, which is both a novel and insightful approach. By defining concepts in such a way, the framework allows for a significant reduction in the complexity of identifying salient features within data, thereby simplifying the learning process. Through rigorous mathematical proofs, the authors demonstrate that it is possible to provably recover these concepts under certain conditions. Notably, the framework requires a substantially fewer number of environments (or datasets) to identify concepts, countering the traditional belief in CRL that a large number of environments proportional to the dimensionality of the data is necessary. This represents a significant theoretical advancement in the field of representation learning, showing that with as few as $n + 2$ environments, $n$ atomic concepts can be systematically recovered.

Practical Implications and Future Directions

The implications of this research are far-reaching, both in theoretical and practical domains. Theoretically, it opens up new avenues in the paper of causal inference and representation learning, suggesting that foundational models can indeed learn causal representations under a relaxed set of assumptions. Practically, the framework posits a viable pathway towards enhancing the interpretability and utility of large foundation models, particularly in the alignment of LLMs towards desired objectives such as truthfulness. The concept of using steering matrices instead of vectors, inspired by observations made through implementing inference-time interventions (ITI) in LLMs, is particularly intriguing. This not only sheds light on the inner workings of such models but also introduces a more nuanced control mechanism that could lead to better alignment techniques.

Experiments and Verification

The empirical validation of this framework on both synthetic data and actual foundation models (e.g., LLMs) provides concrete evidence of its utility. Through a series of experiments designed to test the framework’s capability to identify and manipulate concepts within data, the researchers successfully demonstrate improved performance in aligning LLMs with desired truthfulness criteria. The application of steering matrices, in place of vectors, to direct LLM responses showcases an innovative method of model alignment that merits further exploration.

Concluding Remarks

This research represents an impressive leap towards unifying two seemingly divergent approaches in machine learning: the rigorous, theory-driven causal representation learning, and the empirically successful, yet often opaque, foundation models. By pivoting towards the idea of learning human-interpretable, identifiable concepts, the authors provide a promising framework that not only advances our understanding of these complex models but also enhances their applicability and alignment with human-centric values. As the field continues to evolve, this work lays down an important marker, highlighting the potential for more targeted, efficient, and interpretable machine learning systems.