The CoT Collection: Improving Zero-shot and Few-shot Learning of
Language Models via Chain-of-Thought Fine-Tuning
- URL: http://arxiv.org/abs/2305.14045v2
- Date: Sat, 14 Oct 2023 10:46:55 GMT
- Title: The CoT Collection: Improving Zero-shot and Few-shot Learning of
Language Models via Chain-of-Thought Fine-Tuning
- Authors: Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang, Seonghyeon Ye,
Jamin Shin, Minjoon Seo
- Abstract summary: Language models (LMs) with less than 100B parameters are known to perform poorly on chain-of-thought (CoT) reasoning.
In this work, we aim to equip smaller LMs with the step-by-step reasoning capability by instruction tuning with CoT rationales.
- Score: 50.75534397373867
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Language models (LMs) with less than 100B parameters are known to perform
poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when
solving unseen tasks. In this work, we aim to equip smaller LMs with the
step-by-step reasoning capability by instruction tuning with CoT rationales. In
order to achieve this goal, we first introduce a new instruction-tuning dataset
called the CoT Collection, which augments the existing Flan Collection
(including only 9 CoT tasks) with additional 1.84 million rationales across
1,060 tasks. We show that CoT fine-tuning Flan-T5 (3B & 11B) with CoT
Collection enables smaller LMs to have better CoT capabilities on unseen tasks.
On the BIG-Bench-Hard (BBH) benchmark, we report an average improvement of
+4.34% (Flan-T5 3B) and +2.60% (Flan-T5 11B), in terms of zero-shot task
accuracy. Furthermore, we show that instruction tuning with CoT Collection
allows LMs to possess stronger few-shot learning capabilities on 4
domain-specific tasks, resulting in an improvement of +2.24% (Flan-T5 3B) and
+2.37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until
the max length by a +13.98% margin. Our code, the CoT Collection data, and
model checkpoints are publicly available.
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