Compensatory Biases Under Cognitive Load: Reducing Selection Bias in Large Language Models

Large Language Models (LLMs) like gpt-3.5-turbo and claude-instant-1.2 have become instrumental in interpreting and executing semantic-based tasks. Unfortunately, these models' inherent biases, akin to human cognitive biases, adversely affect their performance. Particularly affected is object selection from lists; a fundamental operation in digital navigation and decision-making. This research critically examines these biases and quantifies the effects on a representative list selection task. To explore these biases, we conducted a series of controlled experiments, manipulating temperature, list length, object identity, object …

become bias biases claude cognitive cognitive biases cs.ai cs.cl decision digital gpt gpt-3 gpt-3.5 gpt-3.5-turbo human language language models large language large language models lists llms making navigation performance research semantic tasks turbo