Causal reasoning

summary

This theme examines how large language models (LLMs) can be enhanced to incorporate advanced mathematical and probabilistic methods, specifically ordinary differential equations (ODEs), probabilistic machine learning (ProbML) and code synthesis, to improve causal reasoning.

 Key areas include:

1. Integration of ODEs and ProbML: This module explores innovative approaches to embedding ODEs and ProbML frameworks within LLMs, enabling these models to handle complex dynamical systems and probabilistic reasoning more effectively.
2. Causality as code synthesis: This module focuses on leveraging code synthesis to better understand causality, aiming to generate code that can not only predict correlations but also infer causal relationships critical for applications in scientific modeling.
3. Case studies and applications: The module includes case studies demonstrating the practical utility of this approach in various fields such as biomedicine and drug development.

This module posits that this novel integration of mathematical methods with LLMs could significantly advance the model's capability in understanding and applying causal reasoning, with wide-ranging implications for computational and applied biomedical sciences.

References

• Jin et al. CLadder: Assessing Causal Reasoning in Language Models. NeurIPS 2023
• Kıcıman, Emre, et al. "Causal reasoning and large language models: Opening a new frontier for causality." arXiv preprint arXiv:2305.00050 (2023).