Summary of Direct-inverse Prompting: Analyzing Llms’ Discriminative Capacity in Self-improving Generation, by Jihyun Janice Ahn et al.
Direct-Inverse Prompting: Analyzing LLMs’ Discriminative Capacity in Self-Improving Generation
by Jihyun Janice Ahn, Ryo Kamoi, Lu Cheng, Rui Zhang, Wenpeng Yin
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper explores ways to reduce uncertainty in language generation models (LLMs) by leveraging their discriminative capabilities. Current LLMs are excellent at generating text but often produce varying results despite minor changes in input or slight variations in the same prompt. The authors propose three types of discriminative prompts – direct, inverse, and hybrid – to identify the most accurate responses from LLMs. By analyzing these prompts on two benchmark datasets, the study reveals which one is most effective and when to use it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are super smart at creating text! But sometimes they’re not so sure about what they’re saying. They might give you different answers if you ask them the same question again or if you make a tiny change in the question. This can be confusing! The researchers wanted to figure out how to fix this problem. They came up with three special questions that help LLMs decide which answer is best. They tested these questions on two big datasets and found out which one works the most. |
Keywords
* Artificial intelligence * Prompt
