On the Feasibility of In-Context Probing for Data Attribution (2025.findings-naacl)
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| Challenge: | In-context probing (ICP) can be used to identify training data that contributes to model outputs, but many data attribution methods, such as influence functions, use model gradients and are computationally expensive. |
| Approach: | They propose to use in-context probing (ICP) to proxy for gradient-based data attribution for data selection under conditions contingent on data similarity. |
| Outcome: | The proposed method can be used to identify training data that contribute to model outputs and fine tune models on training data. |
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Can Input Attributions Explain Inductive Reasoning in In-Context Learning? (2025.findings-acl)
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| Challenge: | interpreting the internal process of neural models has long been a challenge . despite rapid progress, there are still questions bridging the IA and MI eras . |
| Approach: | They propose to use input attribution methods to interpret in-context learning . they find that a certain simple IA method works best in large models . |
| Outcome: | The proposed method is the best for interpreting LLM-based ICL, but the larger the model, the harder it is to interpret it. |
In-context Learning and Gradient Descent Revisited (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism remains elusive. |
| Approach: | They propose a simple gradient descent-based optimization procedure that respects layer causality and improves similarity scores significantly. |
| Outcome: | The proposed procedure improves similarity scores on untrained models despite not showing ICL. |
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. |
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 . |
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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. |
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An Empirical Comparison of Instance Attribution Methods for NLP (2021.naacl-main)
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| Challenge: | Influence functions provide machinery for identifying training instances that may have led to a specific prediction, but are computationally expensive and prohibitive in many cases. |
| Approach: | They evaluate the degree to which different potential instance attribution agrees with respect to the importance of training samples. |
| Outcome: | The proposed methods exhibit desirable characteristics similar to more complex methods, but are computationally expensive. |
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. |
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Predicting Fine-Tuning Performance with Probing (2022.emnlp-main)
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| Challenge: | Large-scale neural models have recently demonstrated impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. |
| Approach: | They propose to use probing to extract a proxy signal widely used in model development to predict fine-tuning performance. |
| Outcome: | The proposed method predicts fine-tuning performance with errors 40% - 80% smaller than baselines. |
Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings (2025.acl-long)
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| Challenge: | Large language models excel at downstream NLP tasks through in-context learning . however, the internal mechanisms behind ICL remain under-explored . |
| Approach: | They propose a PC patching approach to identify modules where input-label mappings function . they observe and verify that key heads utilize input-labeled mappings to generate target labels for new queries. |
| Outcome: | The proposed approach detects modules where input-label mappings function . it also detects that key heads use the mappings to generate labels for new queries . |
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. |