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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 representativedemonstrations from existing training corpora. However, such settings are notaligned with real-world practices, as end-users usually query LMs withoutaccess to demonstration pools. In this work, we introduce Self-ICL -- a simpleframework which bootstraps LMs' intrinsic capabilities to perform zero-shotICL. Given a test input, Self-ICL first prompts the model to generatepseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs viazero-shot prompting. Finally, we perform ICL for the test input with thepseudo-input-label pairs as demonstrations. Evaluation on 23 BIG-Bench Hardtasks shows Self-ICL outperforms zero-shot baselines on both average accuracyand 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'seffectiveness and provide insights for its behaviors under different settings.

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations