Summary of Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect, by Sakshi Mahendru et al.
Venn Diagram Prompting : Accelerating Comprehension with Scaffolding Effect
by Sakshi Mahendru, Tejul Pandit
First submitted to arxiv on: 8 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 paper proposes a novel prompting technique called Venn Diagram (VD) Prompting for Large Language Models (LLMs). The approach enables LLMs to combine information from diverse documents in knowledge-intensive question-answering tasks, replacing the need for multiple LLM calls or pretrained models. VD prompting eliminates position bias and enhances consistency in answers by removing sensitivity to input sequence order. This technique consistently matches or surpasses the performance of a carefully crafted instruction prompt on four public benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Venn Diagram Prompting is a new way to help language models answer questions from multiple sources. Instead of asking the model to do lots of work, this method combines information from different places into one response. It makes sure that the answer doesn’t depend on the order of the information and gives consistent results. |
Keywords
» Artificial intelligence » Prompt » Prompting » Question answering
