Diving into Prompt Validation for Large Language Models With Bias Evaluation Testing — Closed Beta Featured Image

Diving into Prompt Validation for Large Language Models With Bias Evaluation Testing — Closed Beta

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Oct 13, 2023

In this beta preview, we dive deeper into what goes into evaluating how prompts may introduce potential unintended bias in a large language model (LLM) based application. Specifically, we take a closer look at how to use the Bias Evaluation test in our Developer Framework as part of our closed beta testing functionality. 

If you haven’t read it, our previous post discussed running test suites on LLMs for prompt validation. It covered what the ValidMind Developer Framework can do for you once your dataset is ready. In this instalment and following posts, we dive deeper into the prompt validation tests included in our Developer Framework. 

Why bias matters in LLM-based applications

Our focus here is on use cases where LLMs are used for few-shot learning and classification tasks, such as  sentiment classification of financial news. In this context, bias refers to the presence of partiality or prejudice in the model’s understanding, interpretation, or responses when provided with a limited amount of training examples or prompts. Bias can thus lead the model to favor certain perspectives, stereotypes, or assumptions, potentially resulting in unbalanced or inaccurate outputs.

In order to mitigate such bias in few-shot learning type applications, it is essential for model developers to carefully design and evaluate prompts used for generating the output. 

The Bias Evaluation test

The Bias Evaluation test calculates if and how the order and distribution of exemplars (exemples) in a few-shot learning prompt affect the output of a large language model (LLM). The result of this evaluation can be used to detect and manage any unintended biases in its results, and may also be used as part of fine-tuning the model’s performance. 

This test uses two checks:

  • Distribution of exemplars: The number of positive vs. negative examples in a prompt is varied. The test then examines the LLM’s classification of a neutral or ambiguous statement under these circumstances.
  • Order of exemplars: The sequence in which positive and negative examples are presented to the model is modified. Their resultant effect on the LLM’s response is studied.

For each test case, the LLM grades the input prompt on a scale of 1 to 10. It evaluates whether the examples in the prompt could produce biased responses. The test only passes if the score meets or exceeds a predetermined minimum threshold. This threshold is set at 7 by default, but it can be modified as per the requirements via the test parameters.

How to interpret Bias Evaluation test results

A skewed result favoring either positive or negative responses may suggest potential bias in the model. This skew could be caused by an unbalanced distribution of positive and negative examples.

If the score given by the model is less than the set minimum threshold, it might indicate a risk of high bias and subsequent poor performance.

What are potential strengths and limitations of this test?

In practice, understanding both the strengths and limitations of bias evaluation tests is crucial for developers and researchers to make informed decisions about the potential bias in their LLMs and take appropriate steps to mitigate bias when necessary.

Strengths of the Bias Evaluation test:

  • Quantitative measurement: Provides a quantifiable measure of potential bias, offering developers clear and objective guidelines for assessing the bias in their LLM.
  • Impartiality assessment: Helps evaluate the impartiality of the model by analyzing the distribution and sequence of examples, allowing for a systematic examination of potential bias.
  • Flexibility: Allows for flexibility in setting the minimum required bias threshold, making it adaptable to different standards of bias evaluation, whether stricter or more lenient.

Limitations of the Bias Evaluation test:

  • Limited detection of subtle bias: May not be sensitive enough to detect more subtle forms of bias or biases that are not directly related to the distribution or order of examples. It may miss nuanced or hidden biases in the model.
  • Quality and balance of examples: Effectiveness depends on the quality and balance of positive and negative examples used. If these examples do not accurately represent the problem space, the test’s results may be misleading.
  • Grading mechanism accuracy: May not always yield entirely accurate results. In cases where the difference between the threshold and the score is narrow, it might not provide a reliable measure of bias.

It’s important to recognize that addressing bias in LLMs is an ongoing and challenging process. Developers and users of these models should be aware of the potential for bias and actively work to minimize its impact on model outputs.

See it in action

Here is an example of how we include Bias Evaluation test output in your model documentation: 

You can take a look at the sample notebook yourself right here:

Prompt Validation for Large Language Models (LLMs).

You won’t be able to run this notebook without joining our closed beta — invite yourself at the bottom of this post — but it should give you a pretty good idea of what to expect. We try to make it easy for you to get started, as we should.

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