Selection Collider Bias in Large Language Models
This addresses bias and uncertainty issues in LLMs for NLP applications, though it is incremental in focusing on a specific causal mechanism.
The paper tackles the problem of selection collider bias in Large Language Models, which causes them to learn spurious correlations between unconditionally independent entities, and demonstrates an uncertainty metric that matches human uncertainty in gender pronoun tasks with an extended Winogender Schemas dataset.
In this paper we motivate the causal mechanisms behind sample selection induced collider bias (selection collider bias) that can cause Large Language Models (LLMs) to learn unconditional dependence between entities that are unconditionally independent in the real world. We show that selection collider bias can become amplified in underspecified learning tasks, and although difficult to overcome, we describe a method to exploit the resulting spurious correlations for determination of when a model may be uncertain about its prediction. We demonstrate an uncertainty metric that matches human uncertainty in tasks with gender pronoun underspecification on an extended version of the Winogender Schemas evaluation set, and we provide an online demo where users can apply our uncertainty metric to their own texts and models.