A Survey on In-context Learning

A Survey on In-context Learning
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Original Paper
With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few examples. It has been a new trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, demonstration designing strategies, as well as related analysis. Finally, we discuss the challenges of ICL and provide potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL.

Summary Notes

Simplifying AI Training with In-Context Learning for Enterprise AI Engineers

In the rapidly changing field of AI and natural language processing (NLP), in-context learning (ICL) stands out as a groundbreaking technique.
It's changing how we train large language models (LLMs) by requiring fewer updates. This guide breaks down the core of ICL, its advantages, implementation tips, and the challenges it faces.
It's designed to help AI engineers in enterprises utilize this innovative method effectively.


Large language models have revolutionized our ability to interact with human language, showing impressive understanding and generation capabilities. In-context learning is at the forefront of this revolution, offering a way to train models more efficiently. By learning from a few examples, much like humans do, ICL reduces the need for extensive training, saving time and resources. This opens up new possibilities for NLP applications in business.

Training Strategies

For AI engineers eager to use ICL, it's essential to know the different training strategies:
  • Supervised In-Context Training
    • Techniques like MetaICL and OPT-IML aim to enhance how LLMs understand and use context for better learning.
  • Self-Supervised In-Context Training
    • This involves creating training data that matches ICL formats to improve model generalization. Generating aligned training data is key here.

Designing Demonstrations

How demonstrations are designed significantly impacts ICL's effectiveness. Effective strategies include:
  • kNN-Prompting: Uses nearest neighbor algorithms to select relevant examples for tasks.
  • Channel Prompt Tuning: Adjusts prompts based on feedback to improve model performance.
These approaches help organize demonstrations to maximize learning from minimal inputs.

Scoring Functions

Scoring functions are critical in ICL for evaluating answer possibilities. There are two main methods:
  • Direct Estimation Methods: Estimate outcome probabilities based on input and context.
  • Perplexity-Based Scoring: Uses perplexity to measure how well a model's output matches the expected response.
Choosing between these depends on the task's specific needs.

Challenges and Future Directions

ICL faces hurdles like sensitivity to demonstration setups and a limited understanding of its mechanisms. These challenges open doors for future research:
  • Developing pretraining strategies tailored for ICL.
  • Making ICL methods more robust and efficient.
  • Applying ICL in multimodal contexts, combining text and visual data.
The path to fully leveraging ICL's potential is still unfolding, with much to learn and improve.


In-context learning marks a significant advancement in NLP, offering an efficient approach to training LLMs.
Despite existing challenges, the potential for enterprise applications is vast, from enhancing customer service bots to streamlining data analysis. As research and development in ICL progress, it promises to push AI capabilities in the enterprise sector further.
AI engineers leading enterprise technology can find inspiration in the ongoing advancements in ICL. By adopting these innovative approaches, they can boost their AI models' performance and contribute to AI technology's evolution.
In-context learning exemplifies the creativity of researchers and engineers, pointing towards a future where AI can learn and adapt in ways that are just beginning to be understood.

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