Shengnan An


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How Do In-Context Examples Affect Compositional Generalization?
Shengnan An | Zeqi Lin | Qiang Fu | Bei Chen | Nanning Zheng | Jian-Guang Lou | Dongmei Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Compositional generalization–understanding unseen combinations of seen primitives–is an essential reasoning capability in human intelligence. The AI community mainly studies this capability by fine-tuning neural networks on lots of training samples, while it is still unclear whether and how in-context learning–the prevailing few-shot paradigm based on large language models–exhibits compositional generalization. In this paper, we present CoFe, a test suite to investigate in-context compositional generalization. We find that the compositional generalization performance can be easily affected by the selection of in-context examples, thus raising the research question what the key factors are to make good in-context examples for compositional generalization. We study three potential factors: similarity, diversity and complexity. Our systematic experiments indicate that in-context examples should be structurally similar to the test case, diverse from each other, and individually simple. Furthermore, two strong limitations are observed: in-context compositional generalization on fictional words is much weaker than that on commonly used ones; it is still critical that the in-context examples should cover required linguistic structures, even though the backbone model has been pre-trained on large corpus. We hope our analysis would facilitate the understanding and utilization of in-context learning paradigm.


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Learning Algebraic Recombination for Compositional Generalization
Chenyao Liu | Shengnan An | Zeqi Lin | Qian Liu | Bei Chen | Jian-Guang Lou | Lijie Wen | Nanning Zheng | Dongmei Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021