Why does the API using text-babbage-001 return an extra revenue value compared to the Playground?

Below is a breakdown of possible reasons and suggestions:

Potential Causes

• Model Capability Differences:
  The text-babbage-001 model is designed for general text generation and may not be as precise for extraction tasks compared to models like Curie. Its output might include “hallucinated” data (such as the extra 811,000) when the information isn’t explicitly present.

• Sampling Behavior & Temperature:
  Differences in settings (like temperature, max tokens, or other sampling parameters) between your Playground test and your API call can impact output. A higher temperature setting will increase randomness, which might explain inconsistent behavior in extracting revenue figures.

• Prompt Interpretation:
  If the email does not mention a revenue number, the model might be “guessing” or defaulting to an arbitrary amount (811,000 in your case). The model could have picked up on patterns in its training data or previous examples that led it to fill in a number when none was provided.

Suggestions for Improvement

1. Compare Parameters:
   Double-check that all parameters (temperature, top_p, max tokens, etc.) used in your Playground are exactly replicated in your API request. This ensures consistency across environments.

2. Adjust the Prompt:
   If the task is extraction rather than open-ended generation, consider rephrasing your prompt to be more explicit. For example, explicitly instruct the model to output “N/A” or “Not Mentioned” if the revenue isn’t stated.

3. Try More Suitable Models:
   Since Curie seems to be handling the Q&A task more reliably, consider switching to it or another model known for extraction and understanding tasks if consistency is critical.

4. Post-Processing Safeguards:
   Implement checks in your application to validate the extracted revenue against the expected format or range. This way, you can catch any spurious outputs like the extra 811,000.

Example API Request Settings (in JSON)

JSONCopied
{
  "model": "text-babbage-001",
  "prompt": "Given the following email, extract the revenue of the company mentioned. If the revenue isn’t stated, output 'Not mentioned':\n\n[Email Text Here]",
  "temperature": 0.7,
  "max_tokens": 150,
  "top_p": 1.0,
  "frequency_penalty": 0,
  "presence_penalty": 0
}

By ensuring your parameters and prompt are consistent and perhaps by switching to a more precise model, you can likely avoid the unexpected addition of 811,000 in your API responses.