Teaching Smaller Language Models To Generalise To Unseen Compositional Questions

• URL: http://arxiv.org/abs/2308.00946v2
• Date: Mon, 21 Aug 2023 00:28:44 GMT
• Title: Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
• Authors: Tim Hartill, Neset Tan, Michael Witbrock, Patricia J. Riddle
• Abstract summary: We propose a combination of multitask pretraining on up to 93 tasks designed to instill diverse reasoning abilities. We show that performance can be significantly improved by adding retrieval-augmented training datasets.
• Score: 6.9076450524134145
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We equip a smaller Language Model to generalise to answering challenging compositional questions that have not been seen in training. To do so we propose a combination of multitask supervised pretraining on up to 93 tasks designed to instill diverse reasoning abilities, and a dense retrieval system that aims to retrieve a set of evidential paragraph fragments. Recent progress in question-answering has been achieved either through prompting methods against very large pretrained Language Models in zero or few-shot fashion, or by fine-tuning smaller models, sometimes in conjunction with information retrieval. We focus on the less explored question of the extent to which zero-shot generalisation can be enabled in smaller models with retrieval against a corpus within which sufficient information to answer a particular question may not exist. We establish strong baselines in this setting for diverse evaluation datasets (StrategyQA, CommonsenseQA, IIRC, DROP, Musique and ARC-DA), and show that performance can be significantly improved by adding retrieval-augmented training datasets which are designed to expose our models to a variety of heuristic reasoning strategies such as weighing partial evidence or ignoring an irrelevant context.

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