| Challenge: | In-context learning (ICL) is a popular way to stimulate LLM capabilities for downstream tasks due to context length constraints. |
| Approach: | They propose a feature-adaptive and data-scalable in-context learning framework which leverages task-adaptives to promote inference on the downstream task. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on 10 datasets under different data settings and LLM scale. |
Similar Papers
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. |
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. |
In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax (2024.naacl-long)
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| Challenge: | In-context learning is a common method for teaching large language models new tasks . given labeled examples in the input context, the model learns to perform the task without weight updates. |
| Approach: | They examine whether models guided via ICL infer the underlying structure of the task defined by the context or rely on superficial heuristics that only generalize to identically distributed examples. |
| Outcome: | The proposed model generalizes syntactically or linearly on out-of-distribution examples . the proposed model is able to generalize better on pre-trained models . |
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. |
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning (2024.findings-naacl)
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| Challenge: | Existing studies have explored multiple aspects that affect the performance of large language models (LLMs) such as input-output mapping, extensive data resources, and the ability to train on labeled examples. |
| Approach: | They propose a framework that injects knowledge into LLMs during continual self-supervised pre-training and judiciously selects examples with high knowledge relevance. |
| Outcome: | The proposed framework outperforms baseline models and improves by more than 13% and 7% on text classification and question-answering tasks. |
In-Context Learning with Long-Context Models: An In-Depth Exploration (2025.naacl-long)
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| Challenge: | In-context learning is limited by context length, but it can be used for many tasks. |
| Approach: | They study the behavior of in-context learning at an extreme context length . example retrieval shows excellent performance at low context lengths but has diminished gains . |
| Outcome: | The proposed model can perform many tasks with reasonable accuracy when a few examples are provided in-context. |
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 . |
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. |
IAD: In-Context Learning Ability Decoupler of Large Language Models in Meta-Training (2024.lrec-main)
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| Challenge: | In-context Learning (ICL) is a paradigm in which LLMs acquire task-specific knowledge by processing input-output pairs provided as prompts. |
| Approach: | They propose an In-context learning Ability Decoupler to separate ICL ability from general ability of LLMs in meta-training phase . they first identify parameters suitable for ICL by transference-driven gradient importance and propose a new max-margin loss to emphasize the separation of the two abilities. |
| Outcome: | The proposed model separates the ICL ability from the general ability of LLMs in the meta-training phase, where the I-related parameters are tuned to adapt for ICL tasks. |
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 . |