Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains
The minimal pairs paradigm of comparing model probabilities for contrasting completions has proven useful for evaluating linguistic knowledge in language models, yet its application has largely been confined to binary grammaticality judgments over syntactic phenomena. Additionally, standard prompting-based evaluation requires expensive text generation, may elicit post-hoc rationalizations rather than model judgments, and discards information about model uncertainty. We address both limitations by extending surprisal-based evaluation from binary grammaticality contrasts to ordinal-scaled classification and scoring tasks across multiple domains. Rather than asking models to generate answers...
| Authors | Andrew Katz |
|---|---|
| Primary category | cs.CL |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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