Summary of Strux: An Llm For Decision-making with Structured Explanations, by Yiming Lu et al.
STRUX: An LLM for Decision-Making with Structured Explanations
by Yiming Lu, Yebowen Hu, Hassan Foroosh, Wei Jin, Fei Liu
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed STRUX framework improves large language model (LLM) decision-making capabilities by providing structured explanations for decisions made. This framework begins by condensing complex information into a concise table of key facts, then employs self-reflection steps to identify crucial points that support or contradict a specific decision. An LLM is fine-tuned to prioritize these key facts and optimize decision-making. The STRUX approach demonstrates superior performance in forecasting stock investment decisions based on earnings call transcripts, outperforming strong baselines. This framework enhances decision transparency by allowing users to understand the impact of different factors, marking a significant step towards practical decision-making with LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new decision-making system called STRUX that helps large language models make better decisions. It works by taking complex information and breaking it down into simple facts, then figuring out which ones are most important for making a good choice. The system even lets you see how different factors affect the decision, making it easier to understand why the model made its choice. This new approach is tested on a challenging task like predicting stock investment decisions based on earnings call transcripts and performs well. |
Keywords
» Artificial intelligence » Large language model
