Feb 10, 2025 · In-Context Learning (ICL) allows Large Language Models (LLMs) to perform tasks by leveraging examples embedded in the prompt rather than modifying their internal parameters. This means that instead of requiring model retraining, the model dynamically adapts to the task based on the provided context.

Feb 18, 2024 · In-context learning (ICL) is a method of prompt engineering where the model is shown task demonstrations as part of the prompt in natural language. Using ICL, you can utilize pre-trained large language models (LLMs) to solve new tasks without fine-tuning.

Mar 6, 2025 · Recently, retrieval-based in-context learning (ICL) methods for selecting demonstrations have been widely investigated. Existing methods train a dense retriever to retrieve the most appropriate demonstrations for a given test query, which improves ICL performance.

May 22, 2023 · Inspired by in-context learning (ICL), a new paradigm based on demonstration contexts without parameter updating, we explore whether ICL can edit factual knowledge. To answer this question, we give a comprehensive empirical study of ICL strategies.

5 days ago · Large pretrained language models have shown surprising in-context learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input without parameter updates.

Jun 21, 2024 · In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs. Evaluating the ICL ability of LLMs can enhance their utilization and deepen our understanding of how this ability is acquired at the training stage.

In-context learning (ICL) is a technique where task demonstrations are integrated into the prompt in a natural language format. This approach allows pre-trained LLMs to address new tasks without fine-tuning the model.

May 30, 2023 · In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language models? (b) What is a proper performance metric for ICL and what is the error rate? (c) How does the transformer architecture enable ICL?

Jan 28, 2025 · We examined four key factors influencing ICL: model size, templates, shot types, and context window size. Through 32 experiments, we evaluated the results using deep learning performance metrics like recall, precision, F1-score, and accuracy.

Jul 1, 2023 · Compared with supervised training, ICL is a training-free learning framework. This not only greatly reduces the computation costs for adapting the model to new tasks, but also makes...