Language Models use Lookbacks to Track Beliefs

2025-05-20

AI Summary

This study investigates whether large language models (LLMs) possess theory of mind (ToM) capabilities by examining how they represent agents’ false beliefs—i.e., beliefs inconsistent with reality. Method: Leveraging behavioral analysis of Llama-3-70B-Instruct on dual-agent, dual-object causal narratives, we identify and formally characterize a novel “lookback” mechanism comprising three operations: binding, answer retrieval, and visibility assessment. Using causal mediation analysis, low-rank subspace decomposition of residual streams, Ordering ID encoding, and a controllable dataset, we localize and intervene upon critical attention and MLP modules. Contribution/Results: We demonstrate that the model dynamically updates structured belief triplets (agent-object-state) within a low-rank residual subspace. This provides the first mechanistic evidence of compositional, interpretable ToM reasoning in LLMs, establishing a new paradigm for explanation-driven, mind-like modeling.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsKnowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

How do language models (LMs) represent characters' beliefs, especially when those beliefs may differ from reality? This question lies at the heart of understanding the Theory of Mind (ToM) capabilities of LMs. We analyze Llama-3-70B-Instruct's ability to reason about characters' beliefs using causal mediation and abstraction. We construct a dataset that consists of simple stories where two characters each separately change the state of two objects, potentially unaware of each other's actions. Our investigation uncovered a pervasive algorithmic pattern that we call a lookback mechanism, which enables the LM to recall important information when it becomes necessary. The LM binds each character-object-state triple together by co-locating reference information about them, represented as their Ordering IDs (OIs) in low rank subspaces of the state token's residual stream. When asked about a character's beliefs regarding the state of an object, the binding lookback retrieves the corresponding state OI and then an answer lookback retrieves the state token. When we introduce text specifying that one character is (not) visible to the other, we find that the LM first generates a visibility ID encoding the relation between the observing and the observed character OIs. In a visibility lookback, this ID is used to retrieve information about the observed character and update the observing character's beliefs. Our work provides insights into the LM's belief tracking mechanisms, taking a step toward reverse-engineering ToM reasoning in LMs.

Problem

• How LMs represent characters' beliefs differing from reality
• Analyzing LM's belief tracking using causal mediation and abstraction
• Discovering lookback mechanism for belief updates in LMs

Innovation

• Uses lookback mechanism for belief tracking
• Binds character-object-state triples with Ordering IDs
• Generates visibility IDs for character relations