Novel Evaluation
Novel evaluation methods are being developed to address limitations in assessing the capabilities of various AI models, particularly large language models (LLMs). Current research focuses on creating more comprehensive and robust evaluation frameworks that go beyond simple accuracy metrics, incorporating aspects like curiosity, reasoning ability, and alignment with human values, often leveraging LLMs themselves as evaluators. These advancements are crucial for improving the reliability and trustworthiness of AI systems across diverse applications, from natural language processing and image generation to medical diagnosis and financial modeling.
Papers
Are Your LLMs Capable of Stable Reasoning?
Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen
DSGram: Dynamic Weighting Sub-Metrics for Grammatical Error Correction in the Era of Large Language Models
Jinxiang Xie, Yilin Li, Xunjian Yin, Xiaojun Wan