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        <title>A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks</title>
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        <namePart type="given">Ni</namePart>
        <namePart type="family">Xuanfan</namePart>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 2: Frontier Forum)</title>
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        <name type="personal">
            <namePart type="given">Jiajun</namePart>
            <namePart type="family">Zhang</namePart>
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    <abstract>“Recent efforts have evaluated large language models (LLMs) in areas such as com-monsense reasoning, mathematical reasoning, and code generation. However, to thebest of our knowledge, no work has specifically investigated the performance of LLMsin natural language generation (NLG) tasks, a pivotal criterion for determining modelexcellence. Thus, this paper conducts a comprehensive evaluation of well-known andhigh-performing LLMs, namely ChatGPT, ChatGLM, T5-based models, LLaMA-basedmodels, and Pythia-based models, in the context of NLG tasks. We select English andChinese datasets encompassing Dialogue Generation and Text Summarization. More-over, we propose a common evaluation setting that incorporates input templates andpost-processing strategies. Our study reports both automatic results, accompanied by adetailed analysis.”</abstract>
    <identifier type="citekey">xuanfan-piji-2023-systematic</identifier>
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        <url>https://aclanthology.org/2023.ccl-2.4</url>
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        <date>2023-08</date>
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            <start>40</start>
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%0 Conference Proceedings
%T A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks
%A Xuanfan, Ni
%A Piji, Li
%Y Zhang, Jiajun
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 2: Frontier Forum)
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F xuanfan-piji-2023-systematic
%X “Recent efforts have evaluated large language models (LLMs) in areas such as com-monsense reasoning, mathematical reasoning, and code generation. However, to thebest of our knowledge, no work has specifically investigated the performance of LLMsin natural language generation (NLG) tasks, a pivotal criterion for determining modelexcellence. Thus, this paper conducts a comprehensive evaluation of well-known andhigh-performing LLMs, namely ChatGPT, ChatGLM, T5-based models, LLaMA-basedmodels, and Pythia-based models, in the context of NLG tasks. We select English andChinese datasets encompassing Dialogue Generation and Text Summarization. More-over, we propose a common evaluation setting that incorporates input templates andpost-processing strategies. Our study reports both automatic results, accompanied by adetailed analysis.”
%U https://aclanthology.org/2023.ccl-2.4
%P 40-56

Markdown (Informal)

[A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks](https://aclanthology.org/2023.ccl-2.4) (Xuanfan & Piji, CCL 2023)

• A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks (Xuanfan & Piji, CCL 2023)

ACL

• Ni Xuanfan and Li Piji. 2023. A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 2: Frontier Forum), pages 40–56, Harbin, China. Chinese Information Processing Society of China.