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    <titleInfo>
        <title>Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning</title>
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    <name type="personal">
        <namePart type="given">Tianhui</namePart>
        <namePart type="family">Zhang</namePart>
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    <name type="personal">
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        <namePart type="family">Peng</namePart>
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    <name type="personal">
        <namePart type="given">Danushka</namePart>
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            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
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    <abstract>Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the generated sentences is crucial, the diversity of the generation is equally important because it reflects the model’s ability to use a range of commonsense knowledge facts. Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks through in-context learning (ICL) using given examples without the need for any fine-tuning. However, the diversity aspect in LLM outputs has not been systematically studied before. To address this, we propose a simple method that diversifies the LLM generations, while preserving their quality. Experimental results on three benchmark GCR datasets show that our method achieves an ideal balance between the quality and diversity. Moreover, the sentences generated by our proposed method can be used as training data to improve diversity in existing commonsense generators.</abstract>
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        <url>https://aclanthology.org/2024.findings-emnlp.540</url>
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        <date>2024-11</date>
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            <start>9226</start>
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%0 Conference Proceedings
%T Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
%A Zhang, Tianhui
%A Peng, Bei
%A Bollegala, Danushka
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-improving-diversity
%X Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the generated sentences is crucial, the diversity of the generation is equally important because it reflects the model’s ability to use a range of commonsense knowledge facts. Large Language Models (LLMs) have shown proficiency in enhancing the generation quality across various tasks through in-context learning (ICL) using given examples without the need for any fine-tuning. However, the diversity aspect in LLM outputs has not been systematically studied before. To address this, we propose a simple method that diversifies the LLM generations, while preserving their quality. Experimental results on three benchmark GCR datasets show that our method achieves an ideal balance between the quality and diversity. Moreover, the sentences generated by our proposed method can be used as training data to improve diversity in existing commonsense generators.
%U https://aclanthology.org/2024.findings-emnlp.540
%P 9226-9242

Markdown (Informal)

[Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning](https://aclanthology.org/2024.findings-emnlp.540) (Zhang et al., Findings 2024)

• Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning (Zhang et al., Findings 2024)

ACL

• Tianhui Zhang, Bei Peng, and Danushka Bollegala. 2024. Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9226–9242, Miami, Florida, USA. Association for Computational Linguistics.