Reducing Selection Bias in Large Language Models

Understand

Large Language Models (LLMs) like gpt-3.5-turbo-0613 and claude-instant-1.2 are vital in interpreting and executing semantic tasks.

• Unfortunately, these models' inherent 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 experiment manipulating temperature, list length, object identity, object type, prompt complexity, and model.