| Challenge: | Large language models can do in-context learning by conditioning on a few training examples with no parameter updates or task-specific templates. |
| Approach: | They propose a meta-training framework where a pretrained language model is tuned to do in-context learning on a large set of training tasks. |
| Outcome: | The proposed framework outperforms baseline models on 142 NLP datasets and a range of target tasks with domain shifts. |
Similar Papers
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models (2023.acl-short)
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| Challenge: | Large-scale pre-trained vision-language models do not possess the ability to conduct in-context learning. |
| Approach: | They propose to meta-train a language model to perform in-context learning on NLP tasks and then transfer this model to VL tasks by attaching a visual encoder. |
| Outcome: | The proposed model outperforms the baseline model on VQA, OK-VQA, and GQA while having 20 times fewer parameters. |
Rapid Word Learning Through Meta In-Context Learning (2025.emnlp-main)
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| Challenge: | Existing methods for few-shot word learning are underexplored. |
| Approach: | They propose a method that trains language models to generate new examples of a word’s usage given a few in-context examples, using a special placeholder token to represent the new word. |
| Outcome: | The proposed method trains language models to generate new examples of a word’s usage given a few in-context examples, using a special placeholder token to represent the new word. |
Natural Language to Structured Query Generation via Meta-Learning (N18-2)
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| Challenge: | Conventional supervised training is a pervasive paradigm for NLP problems . however, examples of the same problem may vary widely . a few-shot meta-learning scenario is used to learn multiple models . |
| Approach: | They propose a learning protocol that treats each example as a unique pseudo-task . they use a few-shot meta-learning scenario to reduce the original learning problem to a single example . |
| Outcome: | The proposed learning protocol achieves 1.1%–5.4% accuracy gains over non-meta-learning counterparts on a WikiSQL dataset. |
ParaICL: Towards Parallel In-Context Learning (2025.naacl-long)
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| Challenge: | Existing methods to improve ICL performance are limited by the length of the input context. |
| Approach: | They propose a method that utilizes all demonstration examples without exceeding the manageable context length. |
| Outcome: | The proposed method can be scaled up to integrate with existing methods. |
Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization. |
| Approach: | They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
| Outcome: | The proposed framework significantly boosts parser performance on English and Chinese spider datasets. |
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. |
Meta-learning via Language Model In-context Tuning (2022.acl-long)
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| Challenge: | Recent advances in large language models have reduced "task learning and prediction" to a simple sequence prediction problem. |
| Approach: | They propose a meta-learning method that recasts task adaptation and prediction as a sequence prediction problem. |
| Outcome: | The proposed method outperforms MAML on two classification tasks and improves on binaryClfs. |
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification (2021.findings-acl)
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| Challenge: | Existing approaches for few-shot text classification rely on exploitation of lexical features and distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. |
| Approach: | They propose a meta-learning framework integrated with an adversarial domain adaptation network to improve the model's adaptive ability and generate high-quality text embedding for new classes. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on four datasets and shows clear superiority over existing models. |
Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation (2025.findings-acl)
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| Challenge: | Existing Large Language Model (LLM)-based recommender systems face challenges to adapt to dynamic user interests without any model-level updates. |
| Approach: | They propose a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. |
| Outcome: | The proposed model adapts to dynamic user interests without model updates without any model updates and is available online at https://anonymous.4open.science/r/RecICL-8003. |
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks (2024.findings-emnlp)
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| Challenge: | Existing methods for few-shot text classification often encounter problems drawing accurate class prototypes from support set samples. |
| Approach: | They propose a meta-learning method that leverages the information within the task itself . they propose Query-Data-Augmenter and Label-Adapter to build a task-adaptive metric space . |
| Outcome: | The proposed method shows obvious advantages over state-of-the-art models on eight benchmark datasets. |