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Prompt

I'm writing up the results of prompting experiments on Github. these involve assessing the capabilities of LLms for various use-cases. Please suggest a format for the actual reports specifying which headings to use and what content to include in each section

Output

To effectively document your prompting experiments on GitHub, especially when assessing the capabilities of large language models (LLMs) for various use cases, you can follow a structured format that clearly communicates the goals, methods, and results of your experiments. Below is a suggested format with appropriate headings and content for each section.

1. Introduction

  • Purpose: Briefly describe the main objectives of your experiments. What are you trying to assess about the LLMs? Why are these use cases important?
  • Context: Provide background information on the models being tested and their relevance to your project.
  • Scope: Outline the specific use cases or tasks you are focusing on (e.g., text summarization, code generation, question answering).
  • Summary of Results: Offer a high-level summary of the key findings or insights from your experiments.

2. Experimental Setup

2.1 Models and Tools

  • List the LLMs being tested (e.g., GPT-4, GPT-3.5) and any relevant configurations (e.g., model versions, API parameters).
  • Mention any tools or libraries used for evaluation (e.g., promptfoo, write-the, MLflow).

2.2 Prompts and Use Cases

  • Define the prompts or instructions given to the models.
  • Describe the specific use cases or tasks you are testing (e.g., generating creative text, answering factual questions).

2.3 Test Cases

  • Provide details on how you designed test cases. Include both successful and failure scenarios to ensure comprehensive evaluation.

2.4 Evaluation Criteria

  • Explain how you will measure success or failure for each use case (e.g., accuracy, fluency, creativity).
  • Mention any metrics or benchmarks used in your evaluation (e.g., BLEU score, human evaluation).

3. Results

3.1 Quantitative Results

  • Present quantitative data from your experiments using tables, charts, or graphs to compare model performance across tasks.

3.2 Qualitative Analysis

  • Provide examples of model outputs for different prompts and discuss their quality.
  • Highlight interesting or unexpected findings in the model's behavior.

3.3 Error Analysis

  • Discuss any common errors or limitations observed in the LLM outputs.
  • Provide explanations for why certain prompts may have failed or produced suboptimal results.

4. Discussion

4.1 Performance Comparison

  • Compare how different models performed across various tasks.
  • Discuss any trade-offs between different models or configurations (e.g., speed vs accuracy).

4.2 Insights and Learnings

  • Summarize key insights gained from the experiments regarding model capabilities and limitations.

4.3 Implications for Future Work

  • Discuss how these findings might impact future development or research on LLMs for similar use cases.
  • Briefly mention other research or projects that have explored similar use cases with LLMs.
  • Compare your approach and findings with related work to highlight what is novel about your experiments.

6. Conclusion

  • Summarize the most important takeaways from your experiments.
  • Reflect on how these results could be applied in real-world applications or future research.

7. Future Work

  • Suggest potential improvements to your experimental setup (e.g., adding more diverse test cases).
  • Propose additional use cases or models that could be explored in future experiments.

8. Appendix (Optional)

8.1 Detailed Prompts

  • Include a list of all prompts used in your experiments for reproducibility.

8.2 Raw Data and Outputs

  • Provide links to raw data files, logs, or model outputs stored in the GitHub repository.