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Project Evolution: The Impact of Varying Prompts

Overview

In our continuous pursuit of refining the accuracy and reliability of our language model-based fact-checking system, we've conducted extensive experiments with various prompts. These explorations have been instrumental in understanding the nuances of prompt engineering and its significant impact on model performance. This document outlines our journey, highlighting the pivotal role of prompt selection and optimization in achieving improved results.

Initial Approach

Challenges with Early Prompts

Initially, our approach involved using simple, straightforward prompts. However, we quickly encountered limitations:

  • Vague Responses: The model often generated ambiguous or overly general responses.
  • Inconsistency: The results varied significantly, lacking a consistent standard of accuracy.
  • Lack of Context: Without sufficient context, the model struggled to understand the nuances of certain queries.

Examples of Early Prompts

Early prompts often led to unclear or repetitive answers. For instance:

  1. Complex Prompt with Embedded Question:

    Prompt: "Please answer this question with 'Yes,' or 'No,' followed with an explanation. The question is: 'Is the triple '<predicate_text>' supported by the sentence: '<sentence>'?"

    This prompt structure resulted in responses that repeated the question without providing a clear 'Yes' or 'No' answer.

  2. Simplified Yes/No Prompt:

    Prompt: "'Is the triple '<predicate_text>' supported by the sentence: '<sentence>'? Please start by answering yes or no to this question and then explain why."

    While aiming for clarity, this prompt still failed to elicit a straightforward answer, often leading to evasion of the yes or no requirement.

  3. Alternative Structured Prompt:

    Prompt: "'Does this sentence <> support the statement <>? 
Or alternatively: is the triple (<subject>, <predicate>, <object>) supported by the sentence <>?"

    This approach, while attempting to provide structure, often led to convoluted responses and a lack of direct affirmation or negation.

These examples illustrate the complexities we faced in prompt design, where the desired binary (Yes/No) response was often clouded by the structure of the prompt itself.

Conclusion

The evolution of our prompt strategy underscores the critical role of prompt engineering in leveraging language models effectively. By continually adapting and fine-tuning our prompts, we've been able to significantly enhance the performance of our fact-checking system, making it a more reliable and efficient tool.