3 papers
cs.CL2026
C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
Akira Kawabata, Saku Sugawara
Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. However, most existing method…
cs.CL2024
Rationale-Aware Answer Verification by Pairwise Self-Evaluation
Akira Kawabata, Saku Sugawara
Answer verification identifies correct solutions among candidates generated by large language models (LLMs). Current approaches typically train verifier models by labeling solution…
cs.CL2023
Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension
Akira Kawabata, Saku Sugawara
To precisely evaluate a language model's capability for logical reading comprehension, we present a dataset for testing the understanding of the rationale behind critical reasoning…