Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
arXiv:2505.24622v3 Announce Type: replace Abstract: Many high-stakes screening tasks require predicting rare outcomes from unstructured text, where errors are costly and decisions must be auditable. We introduce Random Rule Forest (RRF), an interpretable ensemble that uses a large language model (LLM) not as an end-to-end predictor but as a generator of simple YES/NO questions. Each question acts as a weak learner, and their responses are combined by a plain unit-weight vote into an auditable ``green-flags'' scorecard: enough independent positive signals indicate a higher chance of success. We
The increasing demand for explicable AI decisions in high-stakes environments, coupled with the rapid evolution of large language models, makes interpretable AI solutions crucial at this moment.
This development offers a novel approach to using LLMs for auditable decision-making, potentially mitigating issues of explainability and trust in critical applications.
The paradigm shifts from using LLMs as black-box end-to-end predictors to leveraging them as generators of simple, auditable rules for ensemble learning.
- · Sectors requiring compliant AI (e.g., healthcare, finance)
- · AI developers focused on explainable AI
- · Auditors and regulators
- · Organizations handling sensitive unstructured data
- · Black-box LLM only solution providers
- · Approaches with high error costs and low interpretability
- · Users hesitant to adopt AI due to lack of trust
This enables faster adoption of AI in highly regulated and risk-averse industries.
It could lead to new regulatory frameworks for AI that prioritize auditable and transparent model outputs.
Increased trust in AI might accelerate automation in complex decision-making processes, shifting professional roles.
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Read at arXiv cs.AI