Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
arxiv(2024)
摘要
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.
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