A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks
It addresses the need for systematic evaluation of LLMs in NLG tasks, which is incremental as it builds on existing work in other areas like reasoning.
This paper tackles the lack of evaluation of large language models (LLMs) in natural language generation (NLG) tasks by conducting a comprehensive assessment of models like ChatGPT and LLaMA on English and Chinese datasets for dialogue generation and text summarization, reporting automatic results with analysis.
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.