Can Large Language Models (or Humans) Distill Text?
Nicolas Audinet de Pieuchon Adel Daoud Connor Thomas Jerzak Moa Johansson Richard Johansson
We investigate the potential of large language models (LLMs) to distill text:
to remove the textual traces of an undesired forbidden variable. We employ a
range of LLMs with varying architectures and training approaches to distill
text by identifying and removing information about the target variable while
preserving other relevant signals. Our findings shed light on the strengths and
limitations of LLMs in addressing the distillation and provide insights into
the strategies for leveraging these models in computational social science
investigations involving text data. In particular, we show that in the strong
test of removing sentiment, the statistical association between the processed
text and sentiment is still clearly detectable to machine learning classifiers
post-LLM-distillation. Furthermore, we find that human annotators also struggle
to distill 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 distillation
methods that achieve statistical independence in representation space if this
is difficult for human coders operating on raw text to attain.