Large language models (LLMs) have garnered significant attention, but the
definition of "large" lacks clarity. This paper focuses on medium-sized
lan-guage models (MLMs), defined as having at least six billion parameters but
less than 100 billion. The study evaluates MLMs regarding zero-shot genera-tive
question answering, which requires models to provide elaborate answers without
external document retrieval. The paper introduces an own test da-taset and
presents results from human evaluation. Results show that combin-ing the best
answers from different MLMs yielded an overall correct answer rate of 82.7%
which is better than the 60.9% of ChatGPT. The best MLM achieved 46.4% and has
7B parameters, which highlights the importance of using appropriate training
data for fine-tuning rather than solely relying on the number of parameters.
More fine-grained feedback should be used to further improve the quality of
answers.

本文研究中等规模的语言模型在零 - shot 生成问答方面的性能，评估结果表明最佳模型的回答率可达 46.4％，使用适当的训练数据进行微调比仅仅依靠参数数量更为重要。