Summary of Language Models Benefit From Preparation with Elicited Knowledge, by Jiacan Yu et al.
Language Models Benefit from Preparation with Elicited Knowledge
by Jiacan Yu, Hannah An, Lenhart K. Schubert
First submitted to arxiv on: 2 Sep 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 PREP (Prompting RElated Entities Properly) technique is a simple yet effective way to improve question answering tasks by leveraging language models’ instruction-following capabilities. In this approach, two instances of language models are used: the first generates relevant information and the second receives user input and answers the question. This design allows for better utilization of the language model’s abilities without requiring domain-specific prompt engineering. The PREP technique is tested on various question answering tasks, including those that require accessing relevant knowledge rather than chaining reasoning steps. The results show that the average accuracy of the proposed method is consistently higher than that of other tested methods across all datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to ask questions using language models. Instead of asking one model to answer a question, two models are used: one to get information and another to use that information to answer the question. This helps the models work better together and make more accurate answers. The researchers tested this method on different types of questions and found that it worked well. |
Keywords
» Artificial intelligence » Language model » Prompt » Prompting » Question answering
