RE: LeoThread 2024-09-25 05:16

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Perplexity AI

👨🏾‍💻: What prompt frameworks/formats have been proven to provide the best responses from LLMs based on user feedback and based on your perception?

🤖: Several prompt frameworks and formats have emerged as effective approaches for eliciting high-quality responses from Large Language Models (LLMs) based on recent research and best practices. Here are some of the most promising methods:

Multi-Agent Interactive Multi-hop Generation (MIMG)

The MIMG framework has shown significant improvements in generating high-quality, multi-hop instruction data for long-context tasks[1]. This approach incorporates:

• A Quality Verification Agent
• A Single-hop Question Generation Agent
• A Multiple Question Sampling Strategy
• A Multi-hop Question Merger Agent
  
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This framework has been shown to improve data quality, with over 85% of generated samples being high-quality, multi-hop, and diverse[1].

Structured Prompting

Structured prompting has emerged as a best practice for using LLMs in text annotation tasks[5]. This approach involves:

• Clearly defining the task and evaluation criteria
• Breaking down complex tasks into smaller, manageable steps
• Using consistent formatting and language across prompts

Structured prompting helps ensure consistency and reduces the likelihood of misunderstandings or biased results from the LLM.

Evolutionary Prompt Engineering for Code (EPiC)

For code generation tasks, the EPiC framework has shown promise in improving the quality of generated code while minimizing interactions with the LLM[2]. This approach uses a lightweight evolutionary algorithm to evolve prompts towards better ones that produce high-quality code.

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Logical Feedback and Constraint Refinement

The LMLF (Language Models with Logical Feedback) approach has shown effectiveness in guiding LLMs for specific tasks, such as drug discovery[4]. This method:

• Separates domain constraints (written in logical form) from the text-based query
• Iteratively refines constraints based on logical feedback
• Keeps the query unchanged while improving the logical component

This approach has demonstrated the ability to guide different LLMs to generate potential leads in pharmacological research.

AI-Enhanced Prompting and Fine-tuning

Research on computational social science tasks has revealed that[7]:

1. AI-enhanced prompting outperforms simple zero-shot approaches
2. Fine-tuning on task-specific data yields better results than prompt engineering alone
3. Instruction-tuning on multiple datasets can be beneficial when training data is abundant

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Multi-Agent Debate Framework

The ChatEval system demonstrates the potential of using a multi-agent debate framework for text evaluation tasks[8]. This approach:

• Mimics human evaluation processes by involving multiple "agents"
• Allows for discussion and collaboration between different LLM instances
• Provides more nuanced and reliable assessments than single-agent approaches

Best Practices for Prompt Engineering

Across various studies, some common best practices for prompt engineering have emerged:

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1. Clear task descriptions: Provide detailed and unambiguous instructions for the desired output[5].
2. Consistent formatting: Maintain a consistent structure and language across prompts[5].
3. Iterative refinement: Use feedback and results to continuously improve prompts[6].
4. Model selection: Choose LLMs with larger vocabularies and diverse pre-training corpora[7].
5. Prompt stability analysis: Assess the consistency of LLM outputs across multiple runs with the same prompt[5].
6. Ethical considerations: Be mindful of potential biases and ethical implications when designing prompts[5].

By incorporating these frameworks and best practices, researchers and practitioners can improve the quality and reliability of LLM-generated responses across a wide range of tasks.