Multimodal Theory-of-Mind Reasoning With Weak-to-Strong Control
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Solution Overview
Problem
Existing machine reasoning systems for human-like social cognition, such as Theory of Mind (ToM), face scalability issues when transitioning from smaller to larger language models, leading to high costs and limited world knowledge or reasoning depth.
Innovation Solution
A weak-to-strong control mechanism is employed, combining a large pre-trained language model with a smaller post-trained language model to guide Bayesian inverse-planning for multimodal ToM reasoning, using symbolic representations and Bayesian inference to infer human mental states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large pre-trained language model is used for ToM reasoning, then world knowledge and reasoning depth are improved, but computational cost and fine-tuning requirements increase significantly
Solution Approach 1:
The system segments the ToM reasoning task into distinct components: visual processing (video dataset), textual processing (textual dataset), symbolic representation generation, and Bayesian inference. Each component is handled by specialized modules rather than a monolithic large model, reducing computational overhead while maintaining reasoning capability.
Solution Approach 2:
Symbolic representations serve as an intermediary between raw multimodal inputs and the Bayesian inference engine. Instead of directly processing complex video and text data through a large language model, the system converts inputs into symbolic forms (states, actions, hypotheses) that can be efficiently processed by the Bayesian framework, reducing the burden on computational resources.
2Device complexity
If a smaller post-trained language model is used for ToM tasks, then computational cost is reduced, but world knowledge and reasoning depth are limited
Solution Approach 1:
The system copies and integrates knowledge from multiple sources: the large pre-trained language model provides general world knowledge and reasoning capabilities, while the smaller post-trained model contributes task-specific ToM reasoning skills. This knowledge copying approach allows the system to leverage the strengths of both model sizes without requiring the full computational resources of a large model for every operation.
Solution Approach 2:
The system creates a composite reasoning architecture that combines different model types and processing approaches. The hybrid framework integrates neural network processing (for pattern recognition) with symbolic representation and Bayesian inference (for logical reasoning), creating a composite system that achieves reasoning depth comparable to large models while maintaining lower computational costs.
3Reliability
If extensive fine-tuning is applied to large language models for ToM tasks, then task performance is improved, but training time and resource requirements increase
Solution Approach 1:
The system performs preliminary processing of visual and textual data into symbolic representations before the main Bayesian inference step. By pre-converting video and text inputs into structured symbolic forms (states, actions, hypotheses), the system eliminates the need for extensive fine-tuning during deployment, as the symbolic framework provides a ready-to-use representation that works directly with the Bayesian engine.
Solution Approach 2:
Instead of fully fine-tuning a large language model on ToM datasets, the system applies partial training to a smaller model while relying on the pre-trained knowledge of the large model through symbolic representations. This partial action approach achieves sufficient task performance without the excessive time and resource investment required for complete fine-tuning of large models.
Data Source
AI summary
A system for inferring human mental states through a multimodal theory-of-mind (ToM) framework includes a processor and a memory. The memory stores instructions that when executed by the processor cause the processor to perform the following. The processor receives a video dataset and a textual dataset associated with an environmental scene. The processor converts at least a portion of the video dataset and the textual dataset into symbolic representations. Based on the symbolic representations, the processor generates action-likelihood distributions via a language model based policy obtained by combining a large pre-trained language model with a smaller post-trained language model through a weak-to-strong control mechanism. The processor applies a Bayesian inverse-planning procedure that generates posterior probabilities over multiple goal-belief hypotheses based on the action-likelihood distributions and the symbolic representations. The processor outputs at least one inferred mental state of the agent by selecting from among the multiple goal-belief hypotheses.