| Challenge: | Pre-trained language models are not explicitly trained to learn in context. |
| Approach: | They propose a framework to enhance in-context learning by pre-training language models on a large collection of "intrinsic tasks" they evaluate the in-constitution learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark . |
| Outcome: | The proposed framework outperforms larger language models with nearly 4x parameters on seven widely-used datasets and the Super-NaturalInstrctions benchmark. |
Similar Papers
Understanding In-Context Learning via Supportive Pretraining Data (2023.acl-long)
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| Challenge: | In-context learning (ICL) is a form of learning that provides a handful of examples at inference time, but it is not well understood why it emerges as the model has never been specifically trained on such demonstrations. |
| Approach: | They adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL and compare it with random subsets of pretrain data. |
| Outcome: | The proposed method improves the model's ICL ability by 18% if it is continued on a small subset of pretraining data. |
What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning (2023.findings-acl)
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| Challenge: | Large language models (LLMs) can perform in-context learning (ICL) with only a few demonstrations, but its mechanisms are not well-understood. |
| Approach: | They characterize two ways in which LLMs leverage demonstrations to solve tasks with a few demonstrations. |
| Outcome: | The proposed model achieves non-trivial performance with only TR, and TR does not improve with larger models or more demonstrations. |
In-Context Learning Creates Task Vectors (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) is a powerful new learning paradigm for Large Language Models (LLMs). |
| Approach: | They propose to use a model with a prompt and a query to learn a mapping based on two examples to produce the output. |
| Outcome: | The proposed model can learn functions from a simple structure based on a training set and a single task vector calculated from the training set. |
What Do Language Models Learn in Context? The Structured Task Hypothesis. (2024.acl-long)
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| Challenge: | Pre-trained large language models have exhibited an impressive ability to learn in context across various domains, e.g., code generation, education, medicine and even medicine. |
| Approach: | They taxonomize existing candidate theories into three competing hypotheses that explain LLMs’ ability to learn in context. |
| Outcome: | The proposed model can learn a task from in-context examples presented in a demonstration and generalize it to the prompt. |
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection (2025.naacl-long)
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| Challenge: | In-context learning is sensitive to the choice of demonstrations and can be used for tasks with few examples. |
| Approach: | They propose a framework for in-context learning with noisy, pseudo-annotated demonstrations . they annotate large quantities of demonstrations in a zero-shot first pass . |
| Outcome: | The proposed framework outperforms ICL on biomedical NED datasets with zero human-annotation. |
A Survey on In-context Learning (2024.emnlp-main)
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
| Challenge: | In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples . |
| Approach: | They propose to explore ICL to evaluate and extrapolate the ability of large language models. |
| Outcome: | The proposed methods can be used to evaluate and extrapolate the ability of large language models. |
Concept-aware Data Construction Improves In-context Learning of Language Models (2024.findings-acl)
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| Challenge: | Recent work curating in-context learners assumes that ICL emerges from vast over-parametrization or the scale of multitask training. |
| Approach: | They propose a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. |
| Outcome: | The proposed framework makes it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations and fares comparably to previous in-context learners trained in large-scale multitask learning requiring magnitudes of more training data. |
A Survey to Recent Progress Towards Understanding In-Context Learning (2025.findings-naacl)
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| Challenge: | Existing research on In-Context Learning (ICL) is unclear, despite empirical success . a data generation perspective is used to interpret ICL . |
| Approach: | They propose to use data generation to reinterpret recent efforts from a systematic angle to demonstrate the potential broader usage of ICL. |
| Outcome: | The proposed model can learn from examples provided in the prompt, enabling downstream generalization without the need for gradient updates. |
Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines (2025.findings-acl)
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Do Xuan Long, Duong Ngoc Yen, Do Xuan Trong, Anh Tuan Luu, Kenji Kawaguchi, Shafiq Joty, Min-Yen Kan, Nancy F. Chen
| Challenge: | In-context learning is an important but not fully understood ability of pre-trained large language models. |
| Approach: | They propose a tool that generates two streams of guidelines capturing task language and format distributions and prompts them to define them by prompting. |
| Outcome: | The proposed model improves both strong open- and closed-source LLMs by over 5% in both zero- and few-shot settings. |
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. |