Teaching Transformers Causal Reasoning through Axiomatic Training

TL;DR

This paper introduces an axiomatic training approach for teaching transformers causal reasoning from symbolic demonstrations, enabling generalization to complex scenarios and improving performance on causal benchmarks.

Contribution

It presents a novel axiomatic training method for transformers, demonstrating effective generalization and state-of-the-art results on causal reasoning benchmarks.

Findings

Models trained on causal axioms generalize to complex graphs

Axiomatic training improves performance on causal benchmarks

Finetuned language models surpass GPT-4 on some causal tasks

Abstract

For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since active interventions are costly, we study to what extent a system can learn causal reasoning from symbolic demonstrations of causal axioms. Specifically, we present an axiomatic training method where the system learns from multiple demonstrations of a causal axiom (or rule), rather than incorporating the axiom as an inductive bias or inferring it from data values. A key question is whether the system would learn to generalize from the axiom demonstrations to more complex scenarios. Our results, based on applying axiomatic training to learn the transitivity axiom and d-separation rule, indicate that such generalization is possible. To avoid data contamination issues, we start with a 67 million parameter transformer model and train it from scratch. On both tasks, we find that a model trained on linear causal chains (along with some noisy variations) can generalize well to complex graphs, including longer causal chains, causal chains with reversed order, and graphs with branching.To handle diverse text inputs, the same method is extended to finetune language models. Finetuning Llama-3-8B-Instruct model on our axiomatic data leads to significant gains on causal benchmarks such as Corr2Cause and CLEAR, in some cases providing state-of-the-art performance surpassing GPT-4.