Harnessing 'Logprobs' in GPT for Confidence Score and Mitigating Hallucination

Have you wondered about the confidence level in a GPT response or sought ways to tackle hallucinations? The underutilized logprobs feature in OpenAI's API is a key tool for these challenges.

Understanding Log Probabilities

Log probabilities indicate the likelihood of each token in a GPT response, essential for gauging confidence and identifying hallucinations. High log probabilities (closer to zero) signify greater confidence. When logprobs is enabled, the API returns log probabilities of each output token, indicating their likelihood given the context.

To use logprobs for assessing the confidence score of a response from an OpenAI APIs, follow these steps:

1. Enable logprobs in your API request to receive log probabilities of each token generated by the model.
2. Analyze the log probabilities: Higher log probabilities (closer to 0) indicate higher confidence in the token choice.
3. To get a confidence score for the entire response, compute the average of these log probabilities, or convert them to linear probabilities for easier interpretation.
4. Use this score as an indicator of the model's confidence in its response.

Remember, log probabilities are provided for each token, so aggregating them meaningfully to understand the overall confidence for a sentence or paragraph is crucial.

Methods to Calculate Confidence Score

1. Average of Log Probabilities:You can calculate the average of the log probabilities of each token in the response. Since log probabilities are negative, a higher average (closer to zero) indicates greater confidence. However, averaging log probabilities may not always provide an intuitive sense of confidence due to the nature of logarithmic values.Example: Suppose a response consists of three tokens with log probabilities: -0.2, -0.5, -0.3. The average log probability is (-0.2 - 0.5 - 0.3)/3 = -0.333. Since it's closer to 0, it indicates higher confidence.Pros:- Simplicity: Straightforward to calculate.- Direct Reflection: Represents the model's raw output without modification.Cons:- Less Intuitive: Logarithmic values can be less intuitive to interpret.- Skewed by Outliers: Extreme values can disproportionately affect the average.
2. Converting to Linear Probabilities:To make the values more interpretable, you can convert log probabilities to linear probabilities. This is done by applying the exponential function (e^x) to each log probability. After conversion, you can average these linear probabilities. In this form, probabilities closer to 1 indicate higher confidence.Example: Applying the exponential function to each log probability: e^-0.2 ≈ 0.82, e^-0.5 ≈ 0.61, e^-0.3 ≈ 0.74. The average linear probability is (0.82 + 0.61 + 0.74)/3 ≈ 0.723. Here, values closer to 1 suggest higher confidence.Pros:- Intuitive: Linear probabilities are easier to understand, resembling percentages.- Balanced Interpretation: Reduces the impact of extreme log probability values.Cons:- Additional Computation: Requires conversion, adding computational steps.- Potential Misinterpretation: Higher linear probabilities might overestimate confidence for low-probability tokens.

It's important to note that these methods provide a general sense of confidence and may need adjustments based on specific use cases and the nature of the model's responses.

Addressing Hallucinations

Apart from gauging confidence, logprobs can also help in identifying and mitigating hallucinations - instances where the model generates factually incorrect or nonsensical information.

Tackling hallucination in GPT responses using log probabilities involves identifying parts of the response where the model shows low confidence, which could indicate potential inaccuracies. For instance, in a historical fact statement, if a specific date or event name has a significantly lower log probability compared to the rest of the sentence, it might signal a hallucination.

To calculate a hallucination score, one could:

1. Identify key tokens or phrases critical for factual accuracy.
2. Analyze their log probabilities. Lower values (farther from zero) suggest lower confidence.
3. Aggregate these values to create a hallucination score. A lower score indicates higher likelihood of hallucination.

This method helps in discerning and addressing inaccuracies in GPT responses, particularly in scenarios where factual correctness is paramount.

Conclusion

Logprobs offers an untapped mechanism to assess model confidence and safeguard against misinformation, enhancing the reliability of GPT-generated content. For a comprehensive guide, refer to the OpenAI Cookbook here.