Summary of Auto-demo Prompting: Leveraging Generated Outputs As Demonstrations For Enhanced Batch Prompting, by Longyu Feng et al.
Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting
by Longyu Feng, Mengze Hong, Chen Jason Zhang
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 introduces “Auto-Demo Prompting,” a novel approach to improve the performance of large language models (LLMs) in processing multiple inputs simultaneously. Existing methods focus on data arrangement and majority voting, but Auto-Demo Prompting leverages question-output pairs from earlier questions as demonstrations for subsequent answer inference. The method provides a theoretical analysis of its functioning within LLMs’ autoregressive generation process, illustrating how it optimizes internal representations. Experimental results across five NLP tasks demonstrate its effectiveness in mitigating performance degradation and occasionally outperforming single prompts. Auto-Demo Prompting also opens new avenues for applying few-shot learning techniques, such as demonstration selection, within batch prompting, making it a robust solution for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to help big language models process lots of information at once. Right now, these models can get confused when they have to handle too much information. The new approach uses earlier answers as clues to help the model make better guesses later on. This makes the model more accurate and efficient. The researchers tested this method on five different tasks and found it worked well. It’s a promising solution for real-world applications. |
Keywords
» Artificial intelligence » Autoregressive » Few shot » Inference » Nlp » Prompting
