| 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. |
Similar Papers
Label Words as Local Task Vectors in In-Context Learning (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable abilities, one of the most important being in-context learning (ICL). |
| Approach: | They hypothesized that the network creates a task vector in specific positions during ICL, which can be computed by averaging across the dataset. |
| Outcome: | The proposed model can achieve zero-shot performance with dummy inputs comparable to few-shot learning by patching the global task vector. |
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. |
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. |
Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures (2026.findings-acl)
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| Challenge: | In-context learning is an emergent ability from pretrained Large Language Models (LLMs). |
| Approach: | They perform in-depth evaluations of in-context learning on transformers and hybrid large language models using behavioral probing and intervention-based methods. |
| Outcome: | The proposed model performs well on state-of-the-art transformer, state-space, and hybrid large language models. |
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes (2024.naacl-short)
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| Challenge: | Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential. |
| Approach: | They propose to combine multi-task learning (MTL) with in-context learning (ICL) to build models that can generalize to multiple tasks while being robust to out-of-distribution examples. |
| Outcome: | The proposed training strategies enable models to learn difficult tasks while mixing in prior tasks, denoted as mixed curriculum. |
One Task Vector is not Enough: A Large-Scale Study for In-Context Learning (2026.acl-srw)
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| Challenge: | Existing studies limit comprehensive analysis of large language models based on task vectors . recent work points to "task vectors" as mechanism for encoding task rules . |
| Approach: | They propose a novel task vector with 30 input-output pairs for in-context learning . they use a few prompt-based examples to adapt to new tasks without weight updates . |
| Outcome: | Experiments with Llama-3-8B on QAF show task vector performance peaks at intermediate layer . complex tasks rely on multiple, subtask-specific vectors rather than a single vector . |
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. |
An Empirical Study of In-context Learning in LLMs for Machine Translation (2024.findings-acl)
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| Challenge: | Recent studies focus on optimizing translation quality, with limited attention to understanding specific aspects of ICL that influence the said quality. |
| Approach: | They conduct the first of its kind, exhaustive study of in-context learning for machine translation (MT) they establish that ICL is primarily example-driven and not instruction-driven . |
| Outcome: | The proposed model is based on examples and not instruction-driven learning. |
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)
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| Challenge: | In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples. |
| Approach: | They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept . |
| Outcome: | The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples. |
Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models (2025.naacl-long)
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| Challenge: | Existing studies on in-context learning mechanisms are not consistent . current research identifies two main approaches to explain the ICL mechanism . |
| Approach: | They propose a framework for evaluating in-context learning mechanisms by focusing on regression tasks. |
| Outcome: | The proposed framework can solve regression problems and then measure the extent to which the LLM retrieves its internal knowledge versus learning from in-context examples. |