The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

2 code implementations • 23 May 2023 • Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang, Seonghyeon Ye, Jamin Shin, Minjoon Seo

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.

Common Sense Reasoning (Zero-Shot) Few-Shot Learning +2

CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification

1 code implementation • 7 Mar 2023 • Seungone Kim, Se June Joo, Yul Jang, Hyungjoo Chae, Jinyoung Yeo

To improve the correctness of the explanations, fine-tuning language models with explanation data is needed.

Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization

1 code implementation • COLING 2022 • Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, Jinyoung Yeo

In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.

Abstractive Dialogue Summarization Multi-Task Learning +1