Challenge: In-context learning (ICL) performs tasks by prompting a large language model using an instruction and a small set of annotated examples.
Approach: They develop an ICL evaluation suite to evaluate the performance of popular instruction selection methods.
Outcome: The proposed evaluation suite compares instruction selection methods over five metrics relevant to ICL.

Similar Papers

Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance (2024.findings-emnlp)

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Challenge: In-context learning (ICL) is a dominant paradigm in natural language processing.
Approach: They propose a prompting method for classification tasks using exemplar answers in a *comparative format' they also propose introducing a test instance before the exemplars to improve performance .
Outcome: The proposed method achieves up to 13.76% increase in accuracy on classification tasks across decoder-only and encoder-decoder LLMs.
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning (2025.acl-long)

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Challenge: Recent learning-based demonstration selection methods have proven beneficial to in-context learning (ICL) by choosing more useful exemplars.
Approach: They propose two methods to capture task-agnostic similarities between input and output of LLMs.
Outcome: The proposed methods integrate task-agnostic similarities of different levels between input and output of exemplars and test cases to eliminate costly data collection.
Coverage-based Example Selection for In-Context Learning (2023.findings-emnlp)

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Challenge: In-context learning (ICL) is a training-free paradigm of fewshot inference that can generalize to novel tasks by conditioning on a few task examples.
Approach: They show that BERTScore-Recall (BSR) selects better examples that demonstrate more of the salient aspects of the test input.
Outcome: The proposed model outperforms methods that leverage task or LLM-specific training on compositional tasks.
ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)

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Challenge: Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability.
Approach: They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark.
Outcome: The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy.
Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)

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Challenge: In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context.
Approach: They revisited previous studies using in-context learning techniques . they found that using a data augmentation approach, they significantly improved ICL performance .
Outcome: The proposed approach significantly improves ICL performance on 18 datasets spanning 4 tasks . the proposed approach does not improve performance over a simple random sample selection method .
STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment (2025.emnlp-main)

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Challenge: Existing methods for incontext learning often overlook structural alignment, leading to poor generalization and suboptimal performance.
Approach: They propose a two-stage exemplar selection strategy that achieves a strong balance between efficiency, generalizability and performance.
Outcome: The proposed method outperforms baselines on semantic parsing tasks on four benchmarks.
OpenICL: An Open-Source Framework for In-context Learning (2023.acl-demo)

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Challenge: In-context Learning (ICL) is a new paradigm for large language model evaluation.
Approach: They propose an open-source toolkit for ICL and LLM evaluation.
Outcome: The proposed framework is highly flexible and flexible and can be easily combined with other tools to suit users' needs.
InstructEval: Instruction-Tuned Text Evaluator from Human Preference (2024.findings-acl)

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Challenge: InstructEval is a general text evaluator based on open-source Large Language Models (LLMs).
Approach: They propose to build a general multi-aspect text evaluator based on open-source Large Language Models (LLMs) they use extensive open Human Preference Modeling datasets and a small set of multi-spect annotated data to overcome the shortage of annotation resources for multi-task evaluations.
Outcome: The proposed model performs comparable or superior to commercial LLMs like ChatGPT or GPT-4 in terms of both overall and multi-aspect evaluation tasks.
Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)

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Challenge: Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL).
Approach: They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods.
Outcome: The proposed method improves language understanding and generation tasks with different model scales.
Rethinking the Evaluation of In-Context Learning for LLMs (2024.emnlp-main)

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Challenge: Existing studies evaluate In-context learning methods based on task performance . however, this evaluation protocol overlooks the significant cost associated with the demonstration configuration process .
Approach: They propose a two-dimensional evaluation paradigm that considers both configuration costs and task performance.
Outcome: The proposed evaluation paradigm can be applied to any ICL method as a plugin.

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