Elaboration-Generating Commonsense Question Answering at Scale

• URL: http://arxiv.org/abs/2209.01232v2
• Date: Fri, 14 Jul 2023 21:43:36 GMT
• Title: Elaboration-Generating Commonsense Question Answering at Scale
• Authors: Wenya Wang, Vivek Srikumar, Hanna Hajishirzi, Noah A. Smith
• Abstract summary: In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge. 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.
• Score: 77.96137534751445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 on GPT-3 on four commonsense question answering benchmarks. Human evaluations show that the quality of the generated elaborations is high.

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