From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
#LLM #verifiable reasoning #interpretable AI #code generation #decision-making
📌 Key Takeaways
- LLMs can generate executable code to enhance decision-making transparency.
- Code generation shifts LLM outputs from stochastic to verifiable reasoning.
- This approach improves interpretability in automated decision systems.
- The method allows for validation and debugging of AI-driven conclusions.
📖 Full Retelling
arXiv:2603.13287v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility. Black-box models obscure their reasoning, while recent LLM-based rule systems rely on per-sample evaluation, causing costs to scale with dataset size and introducing stochastic, hallucination-prone outputs. We propose reframing LLMs as code generators rather than per
🏷️ Themes
AI Transparency, Code Generation
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Original Source
arXiv:2603.13287v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility. Black-box models obscure their reasoning, while recent LLM-based rule systems rely on per-sample evaluation, causing costs to scale with dataset size and introducing stochastic, hallucination-prone outputs. We propose reframing LLMs as code generators rather than per
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