New Causal Prompt Engineering Framework Uses Expert Mental Models to Curb LLM Hallucination
A newly revised paper on arXiv (2509.10818) introduces a causal prompt engineering framework designed to minimize hallucinations in large language models (LLMs) when making critical decisions based on implicit knowledge. The authors contend that methods such as retrieval-augmented generation (RAG) and knowledge graphs fail to supply unwritten information, framing this issue as one of model discovery. Their framework incorporates expert decision-making logic into an Expert Mental Model (EMM) for LLM reasoning, utilizing monotone Boolean and k-valued functions. It outlines three processes: factor formulation, factor monotonization, and monotonicity-preserving hierarchical structuring, culminating in a four-step algorithm for EMM construction. This research seeks to improve LLM dependability in high-stakes environments, particularly in sectors like medicine and finance.
Key facts
- arXiv paper 2509.10818 is a revised version (announcement type: replace).
- The paper proposes a causal prompt engineering framework to reduce LLM hallucination.
- RAG and knowledge-graph methods cannot supply knowledge that has never been recorded.
- The framework reconceptualizes hallucination as a model discovery problem.
- It uses an Expert Mental Model (EMM) to encode a domain expert's decision logic.
- The framework is grounded in monotone Boolean and k-valued functions.
- Three prerequisite processes are formalized: factor formulation, factor monotonization, and monotonicity-preserving hierarchical structuring.
- These processes enable a four-step EMM construction algorithm that reduces hallucination.
Entities
Institutions
- arXiv