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    <abstract>Large language models (LLMs) have exhibited striking in-context learning (ICL) ability to adapt to target tasks with a few input-output demonstrations. For better ICL, different methods are proposed to select representative demonstrations from existing training corpora. However, such settings are not aligned with real-world practices, as end-users usually query LMs without access to demonstration pools. In this work, we introduce Self-ICL—a simple framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. Given a test input, Self-ICL first prompts the model to generate pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we perform ICL for the test input with the pseudo-input-label pairs as demonstrations. Evaluation on 23 BIG-Bench Hard tasks shows Self-ICL outperforms zero-shot baselines on both average accuracy and head-to-head comparison. Moreover, with zero-shot chain-of-thought, Self-ICL achieves results comparable to using real demonstrations. Additionally, we conduct a range of analyses to validate Self-ICL‘s effectiveness and provide insights for its behaviors under different settings.</abstract>
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%0 Conference Proceedings
%T Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations
%A Chen, Wei-Lin
%A Wu, Cheng-Kuang
%A Chen, Yun-Nung
%A Chen, Hsin-Hsi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-self
%X Large language models (LLMs) have exhibited striking in-context learning (ICL) ability to adapt to target tasks with a few input-output demonstrations. For better ICL, different methods are proposed to select representative demonstrations from existing training corpora. However, such settings are not aligned with real-world practices, as end-users usually query LMs without access to demonstration pools. In this work, we introduce Self-ICL—a simple framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. Given a test input, Self-ICL first prompts the model to generate pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we perform ICL for the test input with the pseudo-input-label pairs as demonstrations. Evaluation on 23 BIG-Bench Hard tasks shows Self-ICL outperforms zero-shot baselines on both average accuracy and head-to-head comparison. Moreover, with zero-shot chain-of-thought, Self-ICL achieves results comparable to using real demonstrations. Additionally, we conduct a range of analyses to validate Self-ICL‘s effectiveness and provide insights for its behaviors under different settings.
%R 10.18653/v1/2023.emnlp-main.968
%U https://aclanthology.org/2023.emnlp-main.968/
%U https://doi.org/10.18653/v1/2023.emnlp-main.968
%P 15651-15662

Markdown (Informal)

[Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations](https://aclanthology.org/2023.emnlp-main.968/) (Chen et al., EMNLP 2023)

• Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations (Chen et al., EMNLP 2023)

ACL

• Wei-Lin Chen, Cheng-Kuang Wu, Yun-Nung Chen, and Hsin-Hsi Chen. 2023. Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15651–15662, Singapore. Association for Computational Linguistics.