Can In-context Learners Learn a Reasoning Concept from Demonstrations?

TL;DR

This paper investigates whether in-context language models can learn reasoning concepts from demonstrations by focusing on concept-sharing examples, revealing that most models struggle but T0 models show some sensitivity.

Contribution

The paper introduces a concept-sharing evaluation method to better assess in-context learning of reasoning concepts, highlighting limitations of current models and the relative sensitivity of T0 models.

Findings

Most models do not benefit from concept-sharing demonstrations.

T0 models show increased sensitivity to demonstrated concepts.

Evaluation method isolates models' ability to learn new reasoning concepts.

Abstract

Language models exhibit an emergent ability to learn a new task from a small number of input-output demonstrations. However, recent work shows that in-context learners largely rely on their pre-trained knowledge, such as the sentiment of the labels, instead of learning new associations from the input. We argue that the commonly-used few-shot evaluation using a random selection of in-context demonstrations can not disentangle models' reliance on such biases, as most of the randomly-selected demonstrations do not present relations informative for prediction beyond exposing the task's input-output distribution. Therefore, to evaluate models' in-context learning ability independent of models' memory, we introduce a Concept-sharing few-shot learning method choosing the demonstrations that share an underlying concept with the predicted sample. We extract a set of such concepts from available human explanations and measure how much models can benefit from presenting these concepts in few-shot demonstrations. We find that most of the recent in-context learners can not consistently benefit from the demonstrated concepts, irrespective of the model size. However, we note that T0 models are more sensitive to exhibited concepts, benefiting from concept-sharing demonstrations in 7 out of 8 evaluation scenarios.