As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a complex challenge that requires careful consideration of various linguistic tasks, model architectures, and benchmarking methodologies. In recent years, various frameworks have emerged as noteworthy contributions to the field, offering comprehensive evaluation tests and benchmarks for assessing the capabilities of LLMs across diverse domains. This paper provides an exploration and critical analysis of some of these evaluation methodologies, shedding light on their strengths, limitations, and impact on advancing the state-of-the-art in natural language processing.
Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English
HiTZ Center, University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU）HiTZ中心）；University of Alicante (UA)（阿利坎特大学（UA））；GOI Institute, Basque Summer University (UEU)（巴斯克夏季大学（UEU）GOI研究所）
Measures of Variability for Risk-averse Policy Gradient
School of Computer Science, University of Waterloo（滑铁卢大学计算机科学学院）；Vector Institute（矢量研究所）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；School of Computing Science, Simon Fraser University（西蒙菲莎大学计算科学学院）
Beyond the First Read: AI-Assisted Perceptual Error Detection in Chest Radiography Accounting for Interobserver Variability
High School Student（高中学生）；PhD Candidate（博士生）；Department of Electrical and Computer Engineering University of Houston（电气与计算机工程系，休斯顿大学）；Department of Computer Science Emory University（计算机科学系，埃默里大学）；Department of Thoracic Imaging, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center, Houston, TX（胸腔成像部，诊断影像学分会，德克萨斯大学MD安德森癌症中心，休斯顿，TX）
