Goal Driven Discovery of Distributional Differences via Language Descriptions

Exploring large corpora can generate useful discoveries but is time-consuming for humans. We formulate a new task, D5, that automatically discovers differences between two large corpora in a goal-driven way. The task input is a problem comprising a user-specified exploration goal ("comparing the side effects of drug A and drug B") and a corpus pair (collections of patients' self-reported reactions after taking each drug). The output is a goal-relevant description (discovery) of how these corpora differ (patients taking drug A "mention feelings of paranoia" more often). We build a D5 system, and to quantitatively evaluate its performance, we 1) build a diagnostic benchmark, SYND5, to test whether it can recover known differences between two synthetic corpora, and 2) contribute a meta-dataset, OPEND5, aggregating 675 open-ended problems ranging across business, social sciences, humanities, machine learning, and health. With both synthetic and real datasets, we confirm that language models can leverage user-specified goals to propose more relevant candidate discoveries, and they sometimes produce discoveries previously unknown to the authors, including demographic differences in discussion topics, political stances in speech, insights in commercial reviews, and error patterns in NLP models. Finally, we discuss the limitations of our D5 system, which discovers correlation rather than causation and potentially reinforces biases when misused; therefore, practitioners should treat the outputs of our system with caution.