@inproceedings{wang-etal-2023-elaboration,
    title = "Elaboration-Generating Commonsense Question Answering at Scale",
    author = "Wang, Wenya  and
      Srikumar, Vivek  and
      Hajishirzi, Hannaneh  and
      Smith, Noah A.",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.90",
    doi = "10.18653/v1/2023.acl-long.90",
    pages = "1619--1635",
    abstract = "In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models{---}an elaboration generator and an answer predictor{---}allowing each to influence the other. Using less than 0.5{\%} of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.",
}

In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance. Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations. Our framework alternates between updating two language models{---}an elaboration generator and an answer predictor{---}allowing each to influence the other. Using less than 0.5{\%} of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.
Wang, Wenya Srikumar, Vivek Hajishirzi, Hannaneh Smith, Noah A.
Elaboration-Generating Commonsense Question Answering at Scale