In-context learning empowers large language models (LLMs) to execute tasks by conditioning on input-output examples, eliminating the need for parameter optimization. At LEARNS.EDU.VN, we explore the mechanics of in-context learning and provide a Bayesian inference framework for understanding this phenomenon. This approach frames in-context learning as the “location” of latent concepts learned during pretraining. Dive in to learn more, and discover additional AI learning resources that enhance your understanding and expertise at LEARNS.EDU.VN.
1. Understanding the Mystery of In-Context Learning
Large Language Models (LLMs) like GPT-3 excel at predicting the next word in a sequence, a skill honed through extensive training on vast amounts of text. This seemingly simple objective, when combined with massive datasets and sophisticated models, leads to an intriguing emergent behavior known as in-context learning.
1.1. Defining In-Context Learning
Popularized by the original GPT-3 paper, in-context learning allows language models to learn tasks from just a few examples, without updating any parameters.[1] The model receives a prompt consisting of several input-output pairs demonstrating the task. Following these examples, a test input is provided, and the LLM generates a prediction based solely on the prompt. To accurately respond, the model must interpret the input distribution (e.g., financial versus general news), output distribution (e.g., Positive/Negative sentiment or topic), the input-output mapping (e.g., sentiment or topic classification), and the formatting.
Alt text: Examples of in-context learning: financial news sentiment analysis and general news topic classification
1.2. Capabilities of In-Context Learning
In-context learning rivals and even surpasses models trained with much larger labeled datasets on various NLP benchmarks. It achieves state-of-the-art performance on tasks like LAMBADA (commonsense sentence completion) and TriviaQA (question answering). Its versatility enables rapid prototyping of applications, including code generation from natural language descriptions, assistance in app design mockups, and generalization of spreadsheet functions.
1.3. The Surprising Nature of In-Context Learning
Unlike conventional machine learning, in-context learning doesn’t involve parameter optimization. While meta-learning methods also train models to learn from examples, the unique aspect of in-context learning is that the LLM is not explicitly trained for this purpose.[5] This apparent mismatch between pretraining (next token prediction) and the task at hand (in-context learning) is what makes it so intriguing.
2. A Framework for Understanding In-Context Learning
To demystify in-context learning, consider that LLMs like GPT-3 are trained on diverse text from Wikipedia pages to Reddit posts. We propose that this training allows the LLM to model a wide array of learned concepts.
2.1. In-Context Learning as Concept Location
In Xie et al., it’s suggested that the LLM uses the in-context learning prompt to “locate” a previously learned concept to perform the task. For instance, the LLM analyzes training examples to determine whether the task is sentiment analysis or topic classification, applying the same mapping to the test input.
Alt text: Illustration of a Language Model locating different concepts based on training examples.
2.2. Defining Concepts
A concept can be viewed as a latent variable containing document-level statistics, such as word distributions, formats, and relationships between words. In the context of “news topics,” this includes the distribution of words associated with news and their topics, the format of news articles, and the semantic relationships between words. Concepts may encompass multiple latent variables specifying various aspects of semantics and syntax.
2.3. How LLMs Learn Bayesian Inference During Pretraining
LLMs trained on synthetic data with a latent concept structure learn to perform in-context learning. Real pretraining data exhibits similar effects, as documents naturally have long-term coherence: sentences, paragraphs, and table rows in the same document share underlying semantic information and formatting. The document-level latent concept creates this coherence, and modeling it during pretraining requires the model to infer the latent concept.
Steps:
- Pretraining: To predict the next token, the LLM must infer the latent concept for the document using evidence from previous sentences.
- In-context learning: If the LLM infers the prompt concept (the latent concept shared by examples in the prompt) using in-context examples, in-context learning emerges.
2.4. Bayesian Inference View of In-Context Learning
Let’s define the in-context learning setting before discussing the Bayesian inference view:
- Pretraining distribution ((p)): Documents are generated by sampling a latent concept, and then the document is generated conditioned on the latent concept.
- Prompt distribution: In-context learning prompts consist of independent, identically distributed (IID) training examples concatenated with a test input. Each example is drawn as a sequence conditioned on the same prompt concept.
“Locating” learned capabilities can be seen as Bayesian inference of a prompt concept shared by every example in the prompt. By inferring this concept, the model can correctly predict the test example. Mathematically, the prompt provides evidence for the model ((p)) to refine the posterior distribution over concepts, (p(text{concept} mid text{prompt})). Concentrating (p(text{concept} mid text{prompt})) on the prompt concept means the model has effectively “learned” it from the prompt.
Alt text: Graphical representation of concept selection through marginalization.
Ideally, (p(text{concept} mid text{prompt})) focuses on the prompt concept as more examples are provided in the prompt.
2.5. Prompts as Noisy Evidence for Bayesian Inference
The leap of faith in this explanation is that the LLM will infer the prompt concept from in-context examples, even though prompts are sampled from a distribution that differs from the pretraining distribution. Prompts concatenate independent training examples, so transitions between examples can be low-probability under the LLM, introducing noise into the inference process. However, GPT-3 demonstrates that the LLM can still perform Bayesian inference despite these mismatches. In a simplified theoretical setting, in-context learning via Bayesian inference can emerge from latent concept structure in the pretraining data, generating a synthetic dataset where both Transformers and LSTMs can learn in-context.
Alt text: Representation of signal and noise in training examples from training examples
- Training examples provide signal: Transitions within training examples allow the LLM to infer the shared latent concept. The input distribution, output distribution, format, and input-output mapping within a prompt provide signal for Bayesian inference.
- Transitions between training examples can be low-probability (noise): Concatenating IID training examples often creates unnatural, low-probability transitions, creating noise in the inference process due to the mismatch between pretraining and prompt distributions.
2.6. Robustness to Noise
The LLM can successfully perform in-context learning if the signal is greater than the noise, predicting the correct test output as the number of training examples ((n)) increases. The signal is characterized as the KL divergence between other concepts and the prompt concept conditioned on the prompt, while the noise comes from transitions between examples. A strong signal allows the model to distinguish the prompt concept from others easily.[3] This implies that with a strong signal, other forms of noise, such as removing the input-output mapping, are tolerable if the prompt format remains consistent and the input-output mapping information is present in the pretraining data.
2.7. Small-Scale Testbed for In-Context Learning (GINC Dataset)
To validate the theory, GINC, a synthetic pretraining dataset and in-context learning testbed with latent concept structure, was created. Pretraining on GINC leads to in-context learning for both Transformers and LSTMs, indicating that the main effect stems from the structure in the pretraining data. Ablations show that the latent concept structure (which leads to long-term coherence) is crucial for the emergence of in-context learning in GINC.
3. Empirical Evidence for In-Context Learning
Empirical evidence supporting the framework is provided through a series of experiments.
3.1. Input-Output Pairing Matters Less Than Previously Thought
Forming the prompt with the ground truth output isn’t essential for achieving good in-context learning performance.[4]
In Min et al., three different methods are compared:
- No-examples: The LLM conditions only on the test input, akin to zero-shot inference.
- Examples with ground truth outputs: The LLM conditions on a concatenation of in-context examples and the test input, with all outputs in the prompt being ground truth.
- Examples with random outputs: The LLM conditions on in-context examples and the test input, but each output is randomly sampled from the output set.
Alt text: Illustration of a prompt with correct outputs versus a prompt with random outputs.
The approach of “Examples with random outputs” is innovative. Traditional supervised learning fails when the labeled data’s outputs are random.
The experiments involve 12 models ranging from 774M to 175B parameters, including the largest GPT-3 (Davinci), evaluated on 16 classification datasets and 10 multi-choice datasets.
Alt text: Graph comparing the no-examples approach, example with ground truth outputs, and example with random outputs.
In-context learning performance doesn’t significantly drop when ground truth outputs are replaced with random outputs. Using examples with ground truth outputs significantly outperforms no-examples. However, performance is only slightly affected when replacing ground truth outputs with random outputs. This indicates that, unlike typical supervised learning, ground truth outputs aren’t crucial for achieving good in-context learning performance.
3.2. Importance of Prompt Components
If the correct input-output mapping has a marginal effect, which aspects of the prompt are most important for in-context learning?
One important aspect is the input distribution: the underlying distribution from which inputs in the examples are drawn. To quantify its impact, a variant of demonstrations is designed where each in-context example consists of an input sentence randomly sampled from an external corpus. The performance is then compared with demonstrations using random labels. These two versions differ in whether or not the LLM conditions on the correct input distribution.
Alt text: Graph showing a comparison between using the correct input distribution and a random input distribution.
Results indicate that the model performs significantly worse (up to 16% absolute points) when using random sentences as inputs, showing that conditioning on the correct input distribution is important.
Another influencing factor is the output space: the set of outputs (classes or answer choices) in the task. To quantify its impact, a variant of demonstrations consisting of in-context examples with randomly paired English unigrams unrelated to the original labels of the task is designed.
Alt text: Graph showing performance with the correct output space versus random unigrams as outputs.
There’s a significant performance drop when using this demonstration (up to 16% absolute), indicating that conditioning on the correct output space is important.[5] This holds true even for multi-choice tasks, likely because they still have a specific distribution of choices that the model utilizes.
3.3. Connections to the Bayesian Inference Framework
The fact that LLMs don’t heavily rely on the input-output correspondence in the prompt suggests that they may have been exposed to some notions of the input-output correspondence for the task during pretraining. In-context learning then relies on these pre-existing notions. All components of the prompt (input distribution, output space, and format) provide “evidence” to enable the model to better infer (locate) concepts learned during pretraining. While the random input-output mapping increases “noise” due to concatenating random sequences in the prompt, the model still performs Bayesian inference as long as there is enough signal (such as the correct input distribution, output space, and format). The correct input-output mapping can help by providing more evidence and reducing noise, particularly when the input-output mapping doesn’t frequently appear in pretraining data.
3.4. Correlation with Term Frequencies During Pretraining
Razeghi et al. evaluate GPT-J on various numeric tasks, finding that in-context learning performance correlates highly with the frequency of terms (numbers and units) in each instance appearing in GPT-J’s pretraining data (The PILE).
Alt text: Graph showing the correlation between the term frequency in the pretraining data and in-context learning performance for the addition task
Alt text: Graph showing the correlation between the term frequency in the pretraining data and in-context learning performance for the multiplication task
This is consistent across different numeric tasks (addition, multiplication, and unit conversions) and different values of k (the number of labeled examples in the prompt). It remains true even when the input doesn’t explicitly state the task, as in “Q: What is 3 # 4? A: 12.”
3.5. Connections to the Bayesian Inference Framework
This work further supports the idea that in-context learning primarily involves locating latent concepts learned during pretraining. If terms in a particular instance are frequently present in the pretraining data, the model is likely to have a better understanding of the distribution of inputs. This provides stronger evidence to locate latent concepts for performing a downstream task, according to Bayesian inference. The frequencies of the input-output correlation, format, and text pattern also contribute to the model’s understanding.
4. Extensions and Future Directions
4.1. Understanding Model Performance on Unseen Tasks
Our framework suggests that models “localize” or “retrieve” concepts learned during pretraining. However, models can perform well on unusual synthetic tasks, such as mapping sports to animals and vegetables to sports. In these cases, the input-output mapping still matters as the model learns from examples. While in-context learning behaviors may differ in synthetic tasks compared to real NLP benchmarks, Bayesian inference could explain extrapolation if a concept is viewed as a composition of latent variables. For example, syntax and semantics could be represented by separate latent variables. Bayesian inference can generalize to new semantics-syntax pairs, even if the model hasn’t seen all the pairs during pretraining. General operations like permutation, swapping, and copying can help with extrapolation. More work is needed to model in-context learning on unseen tasks.
Alt text: An example synthetic task with unusual semantics.
4.2. Connection to Learning to Read Task Descriptions
Task descriptions in natural language can be used in the prompt to perform a downstream task. Specifying the task description improves Bayesian inference by providing explicit observations of the latent prompt concept. Extending the framework to incorporate task descriptions can inform more compact ways of specifying the task.
4.3. Understanding Pretraining Data for In-Context Learning
While in-context learning arises from long-term coherence structure in pretraining data, further investigation is needed to pinpoint which elements contribute the most. Recent studies offer insights into the type of pretraining data needed to elicit in-context learning behaviors. A better understanding of the ingredients for in-context learning can help construct more effective large-scale pretraining datasets.
4.4. Capturing Effects from Model Architecture and Training
The framework focuses on the effect of pretraining data on in-context learning, but other parts of the ML pipeline can also have effects. Model scale is one such factor, with many studies demonstrating the benefits of scale. Architecture and objective are also influential factors. Future work may investigate how model behavior in in-context learning depends on model scale and the choices of architecture and training objective.
5. Conclusion
In this exploration, we’ve presented a framework where LLMs perform in-context learning by using the prompt to “locate” relevant concepts learned during pretraining. We can theoretically view this as Bayesian inference of a latent concept conditioned on the prompt, a capability that stems from structure (long-term coherence) in the pretraining data. Empirical evidence from NLP benchmarks indicates that in-context learning functions effectively even when outputs in the prompt are replaced with random outputs.
While random outputs introduce noise and remove input-output mapping information, other components (input distribution, output distribution, format) still provide evidence for Bayesian inference. We encourage further research to understand and improve in-context learning.
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FAQ About In-Context Learning
Q1: What is in-context learning?
In-context learning is the ability of large language models (LLMs) to perform tasks based on a few examples provided in the prompt, without updating the model’s parameters.
Q2: How does in-context learning differ from traditional machine learning?
Unlike traditional machine learning, in-context learning doesn’t require any parameter optimization. The model learns solely from the examples given in the prompt.
Q3: What are the key components of an in-context learning prompt?
The key components include input-output pairs that demonstrate the task, along with a test input for the model to predict.
Q4: What is the role of pretraining in in-context learning?
Pretraining allows LLMs to model a diverse set of learned concepts, which they can then “locate” and apply during in-context learning.
Q5: How does the Bayesian inference framework explain in-context learning?
The Bayesian inference framework views in-context learning as the model inferring a prompt concept from the provided examples, allowing it to make accurate predictions.
Q6: What happens when random outputs are used in the prompt?
In-context learning still works well even when random outputs are used, as other components like input distribution, output space, and format provide evidence for Bayesian inference.
Q7: What is the GINC dataset?
GINC is a synthetic pretraining dataset and in-context learning testbed with latent concept structure, used to validate theories about in-context learning.
Q8: Why is the input distribution important in in-context learning?
Conditioning on the correct input distribution is important because it provides the model with crucial information about the task at hand.
Q9: How does term frequency in pretraining data affect in-context learning performance?
In-context learning performance is highly correlated with how many times the terms in each instance appear in the pretraining data.
Q10: What are some potential extensions and future directions for in-context learning research?
Potential extensions include understanding model performance on unseen tasks, connecting in-context learning to reading task descriptions, and understanding pretraining data for in-context learning.
