Concept-aware Training Improves In-context Learning Ability of Language Models

• URL: http://arxiv.org/abs/2305.13775v1
• Date: Tue, 23 May 2023 07:44:52 GMT
• Title: Concept-aware Training Improves In-context Learning Ability of Language Models
• Authors: Michal \v{S}tef\'anik and Marek Kadl\v{c}\'ik
• Abstract summary: Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability. We propose a method to create LMs able to better utilize the in-context information. We measure that data sampling of Concept-aware Training consistently improves models' reasoning ability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability, manifested in the LMs' ability to modulate their function by a task described in a natural language input. Previous work curating these models assumes that ICL emerges from vast over-parametrization or the scale of multi-task training. However, a complementary branch of recent theoretical work attributes ICL emergence to specific properties of training data and creates functional in-context learners in small-scale, synthetic settings. Inspired by recent findings on data properties driving the emergence of ICL, we propose a method to create LMs able to better utilize the in-context information, by constructing training scenarios where it is beneficial for the LM to capture the analogical reasoning concepts. We measure that data sampling of Concept-aware Training (CoAT) consistently improves models' reasoning ability. As a result, the in-context learners trained with CoAT on only two datasets of a single (QA) task perform comparably to larger models trained on 1600+ tasks.

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