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Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness

6 upvotes · 21 JUL 2026 · Xilun Chen, Zhaleh Feizollahi, Ross Goodwin et al.

This paper introduces a new framework to evaluate the factuality and completeness of long-form generation models, which is essential for ensuring that generated text is accurate and informative. Practitioners in natural language processing and artificial intelligence can benefit from this framework to assess the quality of their models.

open-ended generationfactual completenessmultimodal evaluationrubric-based assessmentlong-form text generation