A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks

• URL: http://arxiv.org/abs/2405.10251v1
• Date: Thu, 16 May 2024 16:56:54 GMT
• Title: A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks
• Authors: Xuanfan Ni, Piji Li,
• Abstract summary: This paper conducts a comprehensive evaluation of well-known and high-performing large language models (LLMs) We select English and Chinese datasets encompassing Dialogue Generation and Text Summarization. Our study reports both automatic results, accompanied by a detailed analysis.
• Score: 30.54635848057259
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent efforts have evaluated large language models (LLMs) in areas such as commonsense reasoning, mathematical reasoning, and code generation. However, to the best of our knowledge, no work has specifically investigated the performance of LLMs in natural language generation (NLG) tasks, a pivotal criterion for determining model excellence. Thus, this paper conducts a comprehensive evaluation of well-known and high-performing LLMs, namely ChatGPT, ChatGLM, T5-based models, LLaMA-based models, and Pythia-based models, in the context of NLG tasks. We select English and Chinese datasets encompassing Dialogue Generation and Text Summarization. Moreover, we propose a common evaluation setting that incorporates input templates and post-processing strategies. Our study reports both automatic results, accompanied by a detailed analysis.

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