Summary of Dellma: Decision Making Under Uncertainty with Large Language Models, by Ollie Liu et al.
DeLLMa: Decision Making Under Uncertainty with Large Language Models
by Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to enhancing large language model (LLM) decision support capabilities is proposed, focusing on uncertain environments where traditional prompting methods fall short. The DeLLMa framework integrates principles from decision theory and utility theory with recent advancements in scaling inference-time reasoning. This multi-step procedure improves decision-making accuracy by up to 40% compared to leading LLMs, while also showcasing the benefits of compute scaling at test time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at helping us make decisions! But sometimes they need a little extra help when faced with tricky choices under uncertainty. A new idea called DeLLMa tries to fix this by using special techniques from decision-making theory and utility theory, combined with ways to reason on the fly. It looks like it really works, making decisions up to 40% more accurate than usual! And if you have a lot of computer power available, it gets even better! |
Keywords
* Artificial intelligence * Inference * Large language model * Prompting
