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Finding Support Examples for In-Context Learning

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arxiv 2302.13539 v3 pith:HN2UFLAF submitted 2023-02-27 cs.CL

Xiaonan Li , Xipeng Qiu This is my paper · ORCID
classification cs.CL
keywords examplesin-contextproposeexamplefilterlenspermutationsadditionally
verification ladder T0 review T1 audit T2 compute T3 formal
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Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages: First we filter the dataset to obtain informative in-context examples individually. Specifically, we propose a novel metric, InfoScore, to evaluate the example's in-context informativeness based on the language model's feedback, and further propose a progressive filtering process to filter out uninformative examples. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations, to find examples that fully depict the task. The experimental results show that LENS significantly outperforms a wide range of baselines.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 7 citations worldwide. Full citation record

  1. Selecting Demonstrations for Many-Shot In-Context Learning via Gradient Matching

    cs.CL 2025-06 conditional novelty 6.0 of 10

    CLG selects many-shot demonstrations by matching fine-tuning gradients of a small language model to the full training set, improving accuracy over random selection by 2-4%.

  2. The Role of Diversity in In-Context Learning for Large Language Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Diversity-aware selection of in-context examples improves performance on complex and out-of-distribution tasks, though effect sizes are often modest.

  3. Explanation based In-Context Demonstrations Retrieval for Multilingual Grammatical Error Correction

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Retrieving in-context demonstrations by matching natural-language grammatical error explanations beats input-text similarity for few-shot multilingual GEC.

  4. Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation

    cs.SE 2025-01 conditional novelty 5.0 of 10

    An LLM code optimizer using control-flow-graph differences and retrieved examples reports 7.3% average runtime reduction on 116 C++ programs versus zero-shot GPT-4o.

  5. StaICC: Standardized Evaluation for Classification Task in In-context Learning

    cs.CL 2025-01 conditional novelty 5.0 of 10

    StaICC standardizes in-context classification evaluation with fixed prompts and splits, then measures 29 LMs and 10 inference methods under those fixed settings.

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