Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts

Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal


Abstract
Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in six multi-step reasoning datasets. These contexts are carefully designed to avoid common reasoning shortcuts prevalent in real contexts that prevent models from learning the right skills. This results in a pretraining dataset, named TeaBReaC, containing 525K multi-step questions (with associated formal programs) covering about 900 reasoning patterns. We show that pretraining standard language models (LMs) on TeaBReaC before fine-tuning them on target datasets improves their performance by up to 13 F1 points across 4 multi-step QA datasets, with up to 21 point gain on more complex questions. The resulting models also demonstrate higher robustness, with a 5-8 F1 point improvement on two contrast sets. Furthermore, TeaBReaC pretraining substantially improves model performance and robustness even when starting with numerate LMs pretrained using recent methods (e.g., PReasM, POET). Our work thus shows how to effectively use decomposition-guided contexts to robustly teach multi-step reasoning.
Anthology ID:
2022.emnlp-main.439
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
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Pages:
6541–6566
Language:
URL:
https://aclanthology.org/2022.emnlp-main.439
DOI:
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Cite (ACL):
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6541–6566, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts (Trivedi et al., EMNLP 2022)
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https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.439.pdf