EvoRubric is proposed, a co-evolutionary RL framework that combines criterion-validity feedback, response discrimination, and peer agreement to learn rubrics for open-ended generation and improves over matched static and external evolving-rubric baselines across five benchmarks in Medical, Writing, and Science.
While large language models (LLMs) have achieved impressive gains in commonsense reasoning, they often fall into “associative shortcuts,” failing to distinguish correct answers from plausible but constraint-violating hard negatives. This reliance on semantic priors rather than specific situational constraints limits th...
Xin Guan, Jiu-Xin Cao, Biwei Cao et al.· IEEE Transactions on Audio,...· 0 citations
SARK is proposed, a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations in the reranker model and improves generation performance across multiple LLMs under mixed-style conditions.
Ruwen Zhang, Bo Liu, Zhang-Sheng Xiang et al.· Annual Meeting of the Associ...· 0 citations
The LLM-Enhanced Component Dependency Evolution Graph (CDEG) framework is proposed, a hybrid representation that fuses structural features extracted by Tree-sitter with semantic embeddings derived from a fine-tuned LLM, effectively distinguishing backported patches from code refactoring.
Yuan-Jun Gao, Hong-Zhou Wu, Yu-Jia Luo et al.· Mathematics· 0 citations
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