CLDec 7, 2024

A polar coordinate system represents syntax in large language models

arXiv:2412.05571v118 citationsh-index: 14NIPS
Originality Highly original
AI Analysis

This work addresses the challenge of interpreting syntactic structures in LLMs for researchers in computational linguistics and AI, providing a more complete representation of syntax.

The authors tackled the problem of representing syntactic relations in large language models (LLMs) by hypothesizing that these relations are coded by the relative direction between word embeddings, not just distance. They introduced a 'Polar Probe' that outperformed the Structural Probe by nearly two folds in recovering the type and direction of syntactic relations.

Originally formalized with symbolic representations, syntactic trees may also be effectively represented in the activations of large language models (LLMs). Indeed, a 'Structural Probe' can find a subspace of neural activations, where syntactically related words are relatively close to one-another. However, this syntactic code remains incomplete: the distance between the Structural Probe word embeddings can represent the existence but not the type and direction of syntactic relations. Here, we hypothesize that syntactic relations are, in fact, coded by the relative direction between nearby embeddings. To test this hypothesis, we introduce a 'Polar Probe' trained to read syntactic relations from both the distance and the direction between word embeddings. Our approach reveals three main findings. First, our Polar Probe successfully recovers the type and direction of syntactic relations, and substantially outperforms the Structural Probe by nearly two folds. Second, we confirm that this polar coordinate system exists in a low-dimensional subspace of the intermediate layers of many LLMs and becomes increasingly precise in the latest frontier models. Third, we demonstrate with a new benchmark that similar syntactic relations are coded similarly across the nested levels of syntactic trees. Overall, this work shows that LLMs spontaneously learn a geometry of neural activations that explicitly represents the main symbolic structures of linguistic theory.

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