Polar probe linearly decodes semantic structures from LLMs

2026-05-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

A study by Diego-Simón et al. introduces the "Polar Probe" to investigate how Large Language Models (LLMs) represent complex semantic structures. This probe linearly decodes semantic relations from LLM activations, where the distance between entity embeddings signifies relation existence and their relative direction indicates relation type. The researchers tested this hypothesis across five domains: arithmetic, visual scenes, family trees, metro maps, and social interactions, using models like Llama3.1-8B and OLMo-7B. Results show that this polar code emerges primarily in the middle layers of pretrained LLMs, with relation existence scores peaking at approximately 0.80 and relation type scores at 0.50-0.70 in layers 12-15 of Llama3-8B. Performance improves with LLM size and pretraining steps but degrades with increasing semantic structure complexity and out-of-distribution entities. Causal interventions using the Polar Probe successfully steer LLM predictions, demonstrating a functional role for these geometric representations.

Key takeaway

For research scientists investigating LLM interpretability, you should explore the middle layers of models like Llama3-8B to find robust semantic representations. Understanding these geometric principles can inform the design of more transparent and controllable LLMs, allowing for targeted interventions to steer model behavior for specific tasks. This approach offers a path to bridge symbolic and connectionist AI paradigms.

Key insights

LLMs represent semantic structures using a polar coordinate system in their activation subspaces.

Principles

• Relation existence maps to embedding distance.
• Relation type maps to relative embedding direction.
• Semantic encoding peaks in middle LLM layers.

Method

A Polar Probe, a linear transformation, is trained to recover relational graphs from LLM entity token activations by minimizing structural and angular losses.

In practice

• Probe middle layers (12-15) for optimal semantic decoding.
• Use domain-specific prompts to enhance performance.
• Consider graph complexity when evaluating LLM semantic understanding.

Topics

• Polar Probe
• Semantic Structures
• LLM Interpretability
• Neural Representations
• Relational Graphs

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 cs.CL updates on arXiv.org.