The Platonic Representation Hypothesis

Authors: Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola

What

This paper proposes the Platonic Representation Hypothesis, which posits that neural networks, trained across various architectures and modalities, are converging towards a shared statistical representation of reality.

Why

This hypothesis is significant because it suggests that scaling data and model size could be sufficient for achieving highly generalizable AI systems capable of performing well across a wide range of tasks. It also offers insights into the potential for cross-modal synergy and a deeper understanding of how AI models represent the world.

How

The authors provide evidence for their hypothesis by analyzing existing literature on representational similarity and conducting experiments measuring the alignment of vision and language models. They use techniques like model stitching, nearest-neighbor analysis, and compare representations across models trained on different datasets and with different objectives.

Result

Key findings include: (1) Models with higher performance on a variety of tasks exhibit greater representational alignment, suggesting convergence towards a common solution as competence increases. (2) Alignment is observed even across modalities, with larger language models exhibiting greater alignment with vision models. (3) Alignment with vision representations is correlated with better performance on language-based reasoning tasks, indicating the practical benefits of such convergence.

LF

The authors acknowledge limitations such as difficulty in measuring alignment and the possibility of modality-specific information hindering complete convergence. They suggest further research is needed to understand the precise representation being converged to, the role of non-bijective modalities, and the implications for special-purpose AI.

Abstract

We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned. Next, we demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. We hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato’s concept of an ideal reality. We term such a representation the platonic representation and discuss several possible selective pressures toward it. Finally, we discuss the implications of these trends, their limitations, and counterexamples to our analysis.