Summary of Superposition Prompting: Improving and Accelerating Retrieval-augmented Generation, by Thomas Merth et al.
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
by Thomas Merth, Qichen Fu, Mohammad Rastegari, Mahyar Najibi
First submitted to arxiv on: 10 Apr 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 A novel prompting methodology is proposed to address drawbacks of large language models (LLMs) when processing long contexts. The “distraction phenomenon” occurs when irrelevant context in prompts degrades output quality, while inference cost scales quadratically with sequence length. To mitigate these issues, superposition prompting processes input documents in parallel prompt paths, discarding irrelevant paths. This technique improves time efficiency and accuracy on question-answering benchmarks using pre-trained LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Superposition prompting is a new way to help big language models work better when dealing with long texts. Right now, these models are good at answering questions, but they get slower and less accurate as the text gets longer. This method makes it faster and more accurate by breaking down the text into smaller parts that can be processed in parallel. It also helps when the model is asked to find relevant information from a large amount of text. |
Keywords
» Artificial intelligence » Inference » Prompt » Prompting » Question answering
