Evaluation of medium-large Language Models at zero-shot closed book generative question answering
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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 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.
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