Deconstructing In-Context Learning in Large Language Models

Ali Arsanjani
7 min readJan 5, 2025

--

(Part 4 of 6)

Introduction

Unlike traditional learning methods that require explicit updates to model parameters through fine-tuning, in-context learning (ICL) allows models to perform new tasks by conditioning on examples provided directly in the input prompt. These examples serve as implicit demonstrations, enabling the model to infer task requirements dynamically.

In this article we will discuss the best practices for how best to do in-context learning.

I’ve synthesized insights from the survey paper “Deciphering In-Context Learning in Large Language Models: A Comprehensive Survey” [1] and related literature outlined in the references section. It provides a deep dive into ICL’s mechanisms, challenges, and practical implications, framed around ten key insights and a structured problem-solving framework.

Analyzing In-Context Learning Through a Structured Framework

To better understand the phenomenon of ICL, let’s analyze it through a pattern framework.

  1. Problem
    Traditional methods for adapting LLMs to new tasks require fine-tuning, which is resource-intensive and impractical for many applications. ICL addresses the challenge of enabling LLMs to perform novel tasks without additional training or labeled data.
  2. Context
    ICL arises in the context of rapidly evolving LLMs that are increasingly deployed in diverse real-world scenarios. These models must adapt dynamically to tasks with varying requirements and limited labeled data availability.
  3. Forces
    ICL is driven by the need for flexibility, cost-effectiveness, and reduced dependency on labeled data. However, its development is constrained by factors like sensitivity to prompts, calibration challenges, and the limitations of pre-trained knowledge.
  4. Solution
    ICL leverages examples provided within the input prompt to condition the model for specific tasks. By drawing on pre-trained knowledge, ICL circumvents the need for explicit parameter updates, enabling quick and versatile adaptation.
  5. Solution Details
    The core of ICL lies in crafting effective prompts. This includes selecting high-quality demonstrations, providing explicit instructions, and aligning tasks with pre-trained patterns. Implicit learning mechanisms within LLMs enable task adaptation without direct training.
  6. Consequences
    ICL offers significant advantages, such as reduced training costs and enhanced adaptability. However, challenges like unpredictable performance, sensitivity to prompt design, and calibration issues remain unresolved.

Ten Key Best-practices for Successful In-Context Learning

In this section we look at ten best practices that you can use to create successful in-context learning implementations for a subset of use-cases versus having to do full fine tuning.

  1. Sensitivity to Demonstration Format and Structure
    In-context learning is heavily influenced by how demonstrations are presented. For example, the sequence of input-output pairs, delimiters between examples, and the presence of instructional text can drastically alter performance. Models do not merely extract semantic relationships; they also leverage structural patterns and statistical regularities within the prompt. This behavior highlights that ICL is as much about “how” information is presented as it is about “what” is presented. If examples are inconsistent in formatting, the model’s predictions may degrade, emphasizing the need for careful prompt design.
  2. Dependence on Label Space Definition
    The label space — the set of possible outputs defined by the examples — is crucial for effective ICL. Misalignment in labels (e.g., using “yes” and “no” in some examples and “true” and “false” in others) can confuse the model, even when the outputs are semantically equivalent. This sensitivity underscores the importance of maintaining consistency within the prompt. Moreover, the presence of extraneous or irrelevant labels in the context can lead to unintended associations, reducing performance. ICL thrives on well-defined and consistent label spaces.
  3. Explicit Instructions Enhance Performance
    Including explicit instructions in the prompt can significantly improve ICL outcomes. Instructions help clarify the task, reducing ambiguity about the expected behavior. For instance, specifying “Classify the following reviews as positive or negative” provides a clear directive that complements the input-output demonstrations. This is particularly valuable for complex tasks where examples alone might not provide sufficient guidance. Without such clarity, the model may misinterpret the task or prioritize irrelevant patterns.
  4. Influence of Pre-Training Data
    The success of ICL is deeply rooted in the statistical patterns and regularities learned during pre-training. Tasks that align closely with these patterns tend to yield better performance. For instance, if the task involves translating common phrases, and the model has seen similar translations during pre-training, it will likely excel. Conversely, tasks that are entirely out of distribution or require reasoning not encountered during pre-training may challenge the model. This reflects the transfer-learning nature of ICL — it leverages pre-existing knowledge rather than building entirely new understanding from scratch.
  5. Calibration Issues
    One of the notable challenges with ICL is the poor calibration of predicted probabilities. This means that the confidence levels assigned by the model to its predictions often do not align with their actual correctness. For example, a model might confidently predict an incorrect answer, which can be problematic in applications requiring reliable uncertainty estimates, such as decision support systems. Calibration issues suggest that while ICL can produce accurate outputs, it does so without reliably quantifying its uncertainty.
  6. Non-Trivial Demonstration Selection
    Selecting demonstrations is a critical determinant of ICL performance. While random selection can sometimes yield reasonable results, the choice of examples is rarely straightforward. The survey highlights that high-quality demonstrations — those representative of the task and free from ambiguity — are key to success. However, in practice, identifying these optimal examples can be challenging, particularly for tasks requiring diverse scenarios. This finding suggests the need for systematic methodologies or automated tools to curate demonstrations effectively.
  7. Task Complexity Constraints
    ICL performs well for simpler tasks where the relationship between inputs and outputs can be captured with a small number of examples. However, as task complexity increases, the model’s ability to generalize diminishes. For instance, a task requiring multi-step reasoning may exceed the model’s capacity to infer patterns from the given demonstrations. Furthermore, increasing the number of examples to cover more complex scenarios often leads to impractically long prompts, which can overwhelm the model or dilute its focus.
  8. Sequential Input Order Sensitivity
    The order in which examples are presented within a prompt influences the model’s predictions. For instance, presenting examples in a logical progression may lead to better performance than random sequencing. This behavior suggests that LLMs are not merely treating demonstrations as independent instances but are also attending to temporal relationships and transitions between them. Understanding this sensitivity can aid in designing prompts that better guide the model’s reasoning.
  9. Utility of Negative Examples
    Negative examples — those illustrating incorrect or undesirable outputs — can enhance ICL performance in specific contexts. For example, in a classification task, including examples of both correct and incorrect labels helps the model discern subtle differences. Negative examples act as counterfactuals, refining the model’s understanding of what constitutes an acceptable response. However, they must be used judiciously to avoid confusing the model.
  10. Prompt Engineering as a Determinant of Success
    The strategic design of prompts plays a pivotal role in unlocking the full potential of ICL. Effective prompt engineering involves balancing clarity, conciseness, and contextual relevance. For instance, a well-crafted prompt might include clear instructions, representative examples, and consistent formatting, all of which collectively guide the model toward the desired outcome.

Google’s Gemini’s 2M token large context window

Gemini offers several key advantages over full fine-tuning, especially when considering cost. Note that with context caching you don’t need to incur the input token cost every time you resubmit the prompt containing the examples for the domain e.g., in a system prompt .

Reduced Computational Cost: Fine-tuning a large language model (LLM) can be computationally expensive, requiring significant processing power and time. With a large context window, you can achieve comparable results for many tasks without the need for full fine-tuning, saving on computational resources.

Faster Iteration: Fine-tuning involves a training process that can take time. Gemini’s large context window allows you to quickly adapt the model’s behavior by simply providing relevant information within the context, enabling faster iteration and experimentation.

Simplified Adaptation: Fine-tuning requires expertise in machine learning and careful adjustment of hyperparameters. Utilizing a large context window simplifies the adaptation process, making it more accessible to users without deep technical expertise.

Preservation of General Knowledge: Full fine-tuning can sometimes lead to overfitting to the specific fine-tuning data, potentially degrading the model’s performance on other tasks. Gemini’s large context window allows the model to retain its general knowledge while adapting to new information.

Dynamic Adaptation: A large context window enables dynamic adaptation to new information and tasks without retraining. This is particularly valuable in scenarios where the model needs to quickly adjust to changing requirements or incorporate new knowledge.

Conclusion

In-context learning represents a paradigm shift in how LLMs are adapted to new tasks. While it offers remarkable flexibility and efficiency, its performance is highly dependent on prompt design, pre-trained knowledge, and the quality of demonstrations. Addressing its limitations through systematic research and prompt engineering techniques will be essential for unlocking its full potential.

Google’s Gemini’s 2M token large context window provides a cost-effective and efficient alternative to full fine-tuning for many applications, offering benefits in terms of computational cost, speed, ease of use, knowledge preservation, and dynamic adaptation.

References

  1. Deciphering In-Context Learning in Large Language Models: A Comprehensive Survey. arXiv:2412.15287v1.
  2. Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models.

— thank you Erik !

3. Bommasani et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258.

4. Brown et al. (2020). Language Models are Few-Shot Learners. NeurIPS.

5. Wei et al. (2022). Emergent Abilities of Large Language Models. TMLR.

--

--

Ali Arsanjani
Ali Arsanjani

Written by Ali Arsanjani

Director Google, AI | EX: WW Tech Leader, Chief Principal AI/ML Solution Architect, AWS | IBM Distinguished Engineer and CTO Analytics & ML

No responses yet