Saku Sugawara

Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification.However, most existing methods require costly rubric annotations, limiting scalability.Moreover, we find that rubric generation is vulnerable to a...

Language models (LMs) behave more like humans when their cognitive resources are restricted, particularly in predicting sentence processing costs such as reading times. However, it remains unclear whether such constraints similarly affect sentence comprehension strategies, and existing methods do...

Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge). This work challenges its reliability by showing that trust judgments by LLMs are biased by disclosed source labels. Using a counterfactual design, we find that both humans and LLM judges assign higher tru...

Understanding language acquisition in language models remains an open question, yet many benchmarks focus on grammatical acceptability, with far less attention to interpreting meanings conveyed by grammatical forms.We introduce the Linguistic Minimal-Pair Benchmark for Evaluating Constructional U...