Learning to Disentangle Latent Reasoning Rules with Language VAEs: A Systematic Study

• URL: http://arxiv.org/abs/2506.19418v1
• Date: Tue, 24 Jun 2025 08:38:03 GMT
• Title: Learning to Disentangle Latent Reasoning Rules with Language VAEs: A Systematic Study
• Authors: Yingji Zhang, Marco Valentino, Danilo S. Carvalho, André Freitas,
• Abstract summary: This work investigates how reasoning rules can be explicitly embedded and memorised within language models.<n>We propose a complete pipeline for learning reasoning rules within Transformer-based language VAEs.
• Score: 13.59688284637146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incorporating explicit reasoning rules within the latent space of language models (LMs) offers a promising pathway to enhance generalisation, interpretability, and controllability. While current Transformer-based language models have shown strong performance on Natural Language Inference (NLI) tasks, they often rely on memorisation rather than rule-based inference. This work investigates how reasoning rules can be explicitly embedded and memorised within the LMs through Language Variational Autoencoders (VAEs). We propose a complete pipeline for learning reasoning rules within Transformer-based language VAEs. This pipeline encompasses three rule-based reasoning tasks, a supporting theoretical framework, and a practical end-to-end architecture. The experiment illustrates the following findings: Disentangled reasoning: Under explicit signal supervision, reasoning rules - viewed as functional mappings - can be disentangled within the encoder's parametric space. This separation results in distinct clustering of rules in the output feature space. Prior knowledge injection: injecting reasoning information into the Query enables the model to more effectively retrieve the stored value Value from memory based on Key. This approach offers a simple method for integrating prior knowledge into decoder-only language models. Performance bottleneck: In mathematical reasoning tasks using Qwen2.5(0.5B), increasing sample count doesn't improve performance beyond a point. Moreover, ffn layers are better than attention layers at preserving the separation of reasoning rules in the model's parameters.

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