Computer Science > Computation and Language
[Submitted on 25 Mar 2024 (v1), last revised 3 May 2024 (this version, v2)]
Title:Can Large Language Models (or Humans) Disentangle Text?
View PDFAbstract: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.
Submission history
From: Connor Jerzak [view email][v1] Mon, 25 Mar 2024 09:51:54 UTC (55 KB)
[v2] Fri, 3 May 2024 14:04:19 UTC (80 KB)
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