Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models

• URL: http://arxiv.org/abs/2308.01684v2
• Date: Mon, 23 Oct 2023 12:05:52 GMT
• Title: Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
• Authors: Zheyu Zhang, Han Yang, Bolei Ma, David R\"ugamer, Ercong Nie
• Abstract summary: We propose a "CoThought" pipeline, which efficiently trains smaller "baby" language models (BabyLMs) Our pipeline restructures a dataset of less than 100M in size using GPT-3.5-turbo, transforming it into task-oriented, human-readable texts. Our BabyLM outperforms the vanilla RoBERTa in 10 linguistic, NLU, and question-answering tasks by more than 3 points.
• Score: 3.1244568065126863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability. This ability could be applied to building babylike models, i.e. models at small scales, improving training efficiency. In this paper, we propose a "CoThought" pipeline, which efficiently trains smaller "baby" language models (BabyLMs) by leveraging the Chain of Thought prompting of LLMs. Our pipeline restructures a dataset of less than 100M in size using GPT-3.5-turbo, transforming it into task-oriented, human-readable texts that are comparable to the school texts for language learners. The BabyLM is then pretrained on this restructured dataset in a RoBERTa fashion. In evaluations across 4 benchmarks, our BabyLM outperforms the vanilla RoBERTa in 10 linguistic, NLU, and question-answering tasks by more than 3 points, showing a superior ability to extract contextual information. These results suggest that compact LMs pretrained on small, LLM-restructured data can better understand tasks and achieve improved performance.

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