Can Large Language Models (or Humans) Disentangle Text?
This work addresses the problem of text disentanglement for fairness and causal inference, showing incremental limitations in existing methods.
The study examined whether large language models (LLMs) can disentangle text variables, specifically removing sentiment while preserving other content, and found that both LLMs and human annotators failed to fully eliminate detectable sentiment associations, indicating limited separability in text contexts.
We investigate the potential of large language models (LLMs) to disentangle text variables--to remove the textual traces of an undesired forbidden variable in a task sometimes known as text distillation and closely related to the fairness in AI and causal inference literature. We employ a range of various LLM approaches in an attempt to disentangle text by identifying and removing information about a target variable while preserving other relevant signals. We show that in the strong test of removing sentiment, the statistical association between the processed text and sentiment is still detectable to machine learning classifiers post-LLM-disentanglement. Furthermore, we find that human annotators also struggle to disentangle sentiment while preserving other semantic content. This suggests there may be limited separability between concept variables in some text contexts, highlighting limitations of methods relying on text-level transformations and also raising questions about the robustness of disentanglement methods that achieve statistical independence in representation space.