Evaluating Nuanced Bias in Large Language Model Free Response Answers
This addresses the need for businesses to monitor bias in customized LLMs, though it is incremental as it builds on existing bias evaluation methods.
The paper tackles the problem of detecting nuanced bias in large language model free response answers, identifying four specific types (confidence, implied, inclusion, and erasure bias) that existing multiple-choice benchmarks miss, and presents a semi-automated pipeline combining automatic filtering with crowd worker evaluation to detect them.
Pre-trained large language models (LLMs) can now be easily adapted for specific business purposes using custom prompts or fine tuning. These customizations are often iteratively re-engineered to improve some aspect of performance, but after each change businesses want to ensure that there has been no negative impact on the system's behavior around such critical issues as bias. Prior methods of benchmarking bias use techniques such as word masking and multiple choice questions to assess bias at scale, but these do not capture all of the nuanced types of bias that can occur in free response answers, the types of answers typically generated by LLM systems. In this paper, we identify several kinds of nuanced bias in free text that cannot be similarly identified by multiple choice tests. We describe these as: confidence bias, implied bias, inclusion bias and erasure bias. We present a semi-automated pipeline for detecting these types of bias by first eliminating answers that can be automatically classified as unbiased and then co-evaluating name reversed pairs using crowd workers. We believe that the nuanced classifications our method generates can be used to give better feedback to LLMs, especially as LLM reasoning capabilities become more advanced.