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Active Example Selection for In-Context Learning

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arxiv 2211.04486 v1 pith:MH3SDVIE submitted 2022-11-08 cs.CL cs.AI

Yiming Zhang , Shi Feng , Chenhao Tan This is my paper
classification cs.CLcs.AI
keywords exampleslearningin-contextmodelsimprovementlanguagepoliciesdemonstrate
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5.8\%$ improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Test-Time Scaling via Error Localization

    cs.LG 2026-07 conditional novelty 6.0 of 10

    TTEL uses feedback-induced token probability drops to localize the first error in a failed reasoning trace and branch a new generation from that prefix, improving pass@k per token on coding and math benchmarks.

  2. Towards Compute-Optimal Many-Shot In-Context Learning

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Hybrid demonstration selection that adds 20 similar examples to a large cached random or k-means set matches or beats similarity-only selection at up to 10x lower estimated inference cost in many-shot ICL.

  3. Learning to Select Visual In-Context Demonstrations

    cs.LG 2026-03 reject novelty 5.0 of 10

    A Dueling-DQN agent selects visual in-context demonstrations and outperforms kNN retrieval on objective regression benchmarks but not on subjective preference tasks, per the paper's main table.

  4. InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity

    cs.LG 2025-08 conditional novelty 5.0 of 10

    A submodular mutual information framework for selecting and training in-context learning exemplars improves average accuracy on nine benchmarks by about five points over the IDEAL baseline.

  5. Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis

    cs.CL 2025-07 conditional novelty 5.0 of 10

    For Flamingo-style models, increasing the number of in-context examples improves language coherence but degrades visual-text alignment, and similarity-based image retrieval inflates CIDEr scores by encouraging caption...

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