Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023.

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.

Links

Bib Entry 

@inproceedings{wang2023elaboration-generating,
  author = {Wang, Wenya and Srikumar, Vivek and Hajishirzi, Hannaneh and Smith, Noah A.},
  title = {Elaboration-Generating Commonsense Question Answering at Scale},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023}
}