Summary of Define: Enhancing Llm Decision-making with Factor Profiles and Analogical Reasoning, by Yebowen Hu et al.
DeFine: Enhancing LLM Decision-Making with Factor Profiles and Analogical Reasoning
by Yebowen Hu, Xiaoyang Wang, Wenlin Yao, Yiming Lu, Daoan Zhang, Hassan Foroosh, Dong Yu, Fei Liu
First submitted to arxiv on: 2 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 This paper proposes a new framework called DeFine for decision-making in complex scenarios using large language models (LLMs). The challenge lies in processing transcripts of spoken speech that often contain ungrammatical or incomplete sentences, repetitions, hedging, and vagueness. To address this, DeFine constructs probabilistic factor profiles from these complex scenarios and integrates them with analogical reasoning to guide LLMs in making critical decisions under uncertainty. This approach is particularly useful in fields such as medical consultations, negotiations, and political debates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps big computers make better choices when there’s a lot of uncertainty. Right now, these computers can understand lots of words but struggle with tricky conversations that might not make sense. For example, when an important person talks about how much money their company will make, they might sound confident even if they’re not really sure. The computer should be able to figure out that it’s not really sure either! To help them do this, the paper creates a new way of looking at complex situations and using what happened in similar situations before to guide decisions. |
