Compared to What? Baselines and Metrics for Counterfactual Prompting
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CoBe is a benchmark that evaluates a model’s ability to perform conversational counterfactual text editing, requiring the model to revise a given scenario based on a hypothetical change while preserving unaffected facts. The dataset includes 2,000 questions across domains such as healthcare, science, engineering, business, and everyday life. Experiments show that current models often default to associational generation rather than maintaining causal structure, and performance does not reliably improve with larger model size or longer outputs.
The study evaluates counterfactual bias in ten open‑source large language models (LLMs) for pediatric Emergency Severity Index (ESI) prediction. By creating paired clinical vignettes that differ only in demographic or socioeconomic variables, the authors measure shifts in acuity assignment, finding that counterfactual sensitivity varies widely across model families and sizes. A fine‑tuned Qwen2.5‑7B model exhibited the lowest sensitivity, while larger or medical‑domain models sometimes showed greater shifts, highlighting the need for fairness assessment before clinical deployment.
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