Summary of Prompt Recursive Search: a Living Framework with Adaptive Growth in Llm Auto-prompting, by Xiangyu Zhao et al.
Prompt Recursive Search: A Living Framework with Adaptive Growth in LLM Auto-Prompting
by Xiangyu Zhao, Chengqian Ma
First submitted to arxiv on: 2 Aug 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 Large Language Models (LLMs) have shown remarkable proficiency in addressing various Natural Language Processing (NLP) tasks. However, existing prompt design strategies, such as Chain of Thought (CoT), have inherent limitations. CoT involves manually crafting prompts specific to individual datasets, while LLM-Derived Prompts (LDPs) provide tailored solutions but may decline in performance when tackling complex problems. To address these challenges, we introduce the Prompt Recursive Search (PRS) framework that leverages the LLM to generate problem-specific solutions, conserving tokens. PRS incorporates an assessment of problem complexity and an adjustable structure to reduce errors. Our experiments with different LLM models across various datasets demonstrate the effectiveness of PRS, achieving a 22% improvement on the BBH dataset using the Llama3-7B model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do many tasks well, like understanding language. But they need special instructions called prompts to help them. These prompts are made by people or the models themselves. The problem is that these prompts have limitations. When we use prompts made by humans, it’s like using a template for simple and hard problems together. This makes the model waste time on easy tasks. On the other hand, when the model creates its own prompts, it might not do well with hard problems because of mistakes it makes while trying to solve them. To fix this, we created a new way called Prompt Recursive Search that lets the model make its own prompts for each problem, saving time and reducing mistakes. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Prompt
