Authors: Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger

Published on: February 04, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.02392

Summary

• What is new: DeLLMa, a new framework, significantly improves decision-making accuracy of large language models in uncertain environments.
• Why this is important: Existing large language models perform poorly on decision-making tasks under uncertainty.
• What the research proposes: DeLLMa enhances decision-making by using a multi-step scaffolding process based on decision and utility theory.
• Results: Up to a 40% increase in decision-making accuracy in tests involving real agriculture and finance data.

Technical Details

Technological frameworks used: DeLLMa (Decision-making Large Language Model assistant)

Models used: Large Language Models

Data used: Agriculture and finance

Potential Impact

Agriculture, finance, and any sector reliant on decision-making under uncertainty.

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