A Theoretical Approach for Using Dependency Graphs to Facilitate Causal Reasoning in LLMs for Important Decisions
The natural language processing skills of large language models (LLMs) are astounding. They have trouble with causal thinking, which is a crucial component of high-stakes decision support.
Here I describe how modeling causal linkages in dependency networks can give LLMs the structured knowledge they need to make better judgments.
LLM-based decision support systems frequently only use statistical patterns that are taken from text corpora.
Understanding causal mechanisms, or how changing one variable affects others, is necessary for making real-world decisions.
LLMs cannot evaluate the effectiveness of interventions or reason about outcomes without encoding specific causal linkages.
Dependency graphs can help with this. Variables are represented as nodes in these graphs with directed edges standing in for causal dependencies. For illustration, in a medical
0 Comments