The Allure of Larger Context Windows

Bigger isn’t always better.

ChatGPT — Dall-E 3 Generated Image (Prompt: “with Dall-E generate an image of a bunch of tokens running through a field being chased by a tokenizer.”)

During Sam Altman’s keynote speech at OpenAI’s inaugural developer conference, he announced the release of a new model variant, GPT-4 Turbo. Among the new features revealed were a knowledge cut-off date of April 2023, and an increased context window size of 128k tokens or roughly 300 pages of a book. This is a 4x boost over its predecessor GPT-4–32k model that has a limit of 32,768 tokens and 32x improvement over the initial GPT-4 model that was limited to 4096 tokens.

But what does this increase in context window size mean for users and developers? What exactly is a context window and how does it work? And does this increased context window size present any challenges and could it negatively impact the performance of developer applications? Are there any ways to work around context window limitations? And what are AI researchers doing to ensure that increased context window sizes don’t come at the expense of decreased model performance?

Working with large language models, the ‘context window’ is a crucial element of a model’s performance and practical applications, as it dictates how much text the model can consider for generating responses and how much content it can generate. Although larger windows are often seen as advantageous, the benefits are not straightforward. This article will clarify what context windows are, their function in relation to input and output tokens, and their impact on the performance of transformer models.

In the Beginning There Were Tokens

When we converse, we use words, each carrying a distinct meaning. In the context of natural language processing (NLP), particularly with Large Language Models (LLMs) like GPT-4, the concept of a ‘word’ is encapsulated in what we call a ‘token’.

A token can be thought of as a slice of language — it might be a whole word, a part of a word such as a syllable or a prefix, or even just punctuation. For instance, the word “unbelievable” could be split into tokens like “un”, “believ”, and “able”. This granular breakdown allows LLMs to efficiently process language by handling complex words or phrases it may not have encountered before, using combinations of tokens it already knows.

When an LLM like GPT-4 reads or generates text, it’s actually processing a sequence of these tokens. Each one is mapped to a unique identifier — a number that the model can understand and use in its internal computations (called an encoding.) This numerical encoding transforms text into a format that the neural network can work with, enabling it to recognize patterns, make predictions, and construct sentences (called an embedding).

Sidebar on vocabulary, encodings and embeddings.

There are some related concepts that are important to understanding the importance of tokens and their transformations. Those being vocabulary, encodings and embeddings which each play a separate role in language understandings and generation.

Vocabulary: In the context of LLMs, ‘vocabulary’ refers to the predefined set of tokens that the model has been trained to recognize and understand. It is essentially the collection of all the unique tokens — such as words, subwords, characters, punctuation, and other language elements — that the model can process. Each element in the vocabulary is assigned a specific identifier, known as a token ID, which is used during the encoding process to convert raw text into a numerical format that the model can work with.

The vocabulary serves as the dictionary of sorts for both tokenization, where text is segmented into recognizable tokens, and the subsequent generation of embeddings, where these tokens are represented as vectors encapsulating their semantic and syntactic properties. When an LLM generates text, token IDs are then converted back to their original alphanumeric text (more of a lookup than a conversion) to form coherent and human readable text.

Encodings: An encoding is a broad term that refers to the process of converting data from one format to another. In the context of LLMs, encoding typically refers to the initial stage of data preprocessing, where raw text is converted into a numerical format that the model can understand. This involves tokenization, where the text is split into tokens, followed by converting these tokens into a sequence of integers, known as token IDs. These token IDs are a form of encoding that directly corresponds to the model’s vocabulary.

Embeddings: An embedding, on the other hand, is a specific representation of these tokens (or token IDs) as vectors of continuous numbers, placed in a high-dimensional space. Embeddings capture more than just the identity of a token; they are designed to encapsulate the semantic meaning, context, and relationships between different tokens. In transformers, the embedding layer transforms the sequence of token IDs into these dense vectors, which are then passed through the model’s subsequent layers.

How Tokens Are Created

The foundation of any language model’s understanding of text lies in its ability to break down complex structures into manageable units known as tokens. This process is facilitated by a component called a tokenizer.

A tokenizer’s primary function is to parse through text data and segment it into these tokens based on its vocabulary. It operates by recognizing patterns and structures within the text, often using pre-defined rules or learned behaviors. The tokens produced can range from individual characters to words, or even subwords, depending on the tokenizer’s design.

There are various types of tokenizers, each with its methodology and application. The simplest might split text on white space, treating each word as a token, while more advanced tokenizers might employ algorithms to segment words into subword units, which can be particularly advantageous for handling morphologically rich languages or vocabularies with many rare words.

The choice of tokenizer can affect a model’s performance significantly. Subword tokenizers, such as Byte-Pair Encoding (BPE) or WordPiece, tend to offer a balance between the granularity of character-level tokenization and the efficiency of word-level tokenization. They can generalize better to unseen text compared to their word-level counterparts by breaking down words into more frequently occurring subword units.

Once tokenized, these tokens are then transformed into encodings then into embeddings, which are dense vector representations that capture the semantic and syntactic nuances of each token. Embeddings act as the bridge between the discrete tokens (token IDs) and the continuous mathematical space that neural networks operate in. The quality of these embeddings, which directly relates to the tokenizer’s effectiveness, is crucial as they form the input for the subsequent layers of the transformer model where the actual language processing occurs.

In essence, the tokenizer’s output quality, its ability to produce a balanced and informative set of tokens, sets the stage for everything that follows in a transformer-based language model.

You can experiment with OpenAI’s Tokenizer to see how sentences are broken down into tokens here.

Context Windows Explained

In transformers, a context window is a defined span within which the model operates and generates predictions. It’s the amount of information — specifically, the number of tokens — the model can consider at any given time when producing output.

Each token is a discrete piece of data, and for language models like GPT-4, each is a segment of text. The context window, then, is the model’s working memory. It determines how much prior text the model can use to understand the current position and generate subsequent text. For example, in a conversation, the context window allows the model to ‘remember’ previous exchanges, which is crucial for providing relevant and coherent responses.

The size of this window is pivotal because it influences the transformer’s performance. A larger window can improve the model’s ability to maintain context over longer interactions, which is particularly beneficial for complex tasks such as summarizing large documents or composing extensive pieces of writing. However, it also imposes greater demands on the model’s architecture and the computational infrastructure, as more data requires more processing power and memory to manage effectively.

What is a Prompt Token Limit?

A prompt token limit refers to the maximum number of tokens a user can input into the model to generate a response. This limit is not just a random number but is deeply intertwined with the architecture of the model and its operational parameters. For instance, with a GPT-4 model, if the prompt token limit is set to 500 tokens, it means that users can input up to 500 tokens for the model to consider when generating its output.

What is a Response (Completion) Token Limit?

A response or output token, sometimes referred to as a completion token limit is the maximum number of tokens that the model will generate in response to a prompt.

The response token limit is a crucial parameter for controlling the length and, indirectly, the computational intensity of the generation task. If a user sets a response token limit of 150 tokens, the model will generate content up to that number, ensuring that the output remains concise and within the expected bounds.

This limit is intricately related to the model’s context window. The context window encompasses both the initial prompt and the generated response, meaning it must accommodate the sum of input and output tokens. For example, if the context window of a model is 2048 tokens and the prompt consumes 500 tokens, the potential response token limit could go up to 1548 tokens, adhering to the maximum capacity of the context window.

The response token limit not only provides a way to control the verbosity of model outputs but also ensures that the model remains efficient in its operation. It prevents the model from generating endlessly, which could lead to excessive resource usage and potential degradation in coherence as the response grows. Balancing this limit with the demands of the task at hand is a nuanced aspect of working with LLMs, ensuring that the output is both relevant and computationally sustainable.

context window = prompt tokens + output tokens

The Upside of Expansion

Larger context windows enable models to reference more information, grasp narrative flow, maintain coherence over longer passages, and generate more contextually rich responses. This can be particularly valuable in scenarios like document summarization, extended conversations, or complex problem-solving where historical context is crucial.

The Complications of Scale

However, larger windows come with trade-offs. They demand more computational power and memory during both training and inference times, escalating hardware requirements and costs. Moreover, the diminishing returns of adding context can become a bottleneck. Models might struggle with longer dependencies or become prone to repeating or contradicting themselves. Furthermore, the increased carbon footprint of larger models is a growing concern in sustainable AI development.

Performance and Accuracy

Contrary to what one might expect, a larger context window does not universally translate to better model performance or accuracy. The benefits plateau, and certain tasks may not gain any improvement from the added context, especially if the essential information can be encapsulated in a smaller window.

Hardware Implications

Training and running models with expansive context windows require significant memory bandwidth and storage. For instance, the latest GPUs with substantial VRAM or utilization of TPUs at scale can become a necessity, which can gatekeep smaller entities from leveraging state-of-the-art models.

The Bigger Picture

Beyond hardware and performance, larger context windows can affect the data processing pipeline, model fine-tuning, and even the design of applications that utilize these AI models. Developers must balance the context window’s size with the application’s needs, user experience considerations, and operational constraints.

Workarounds for Developers and Users

For Small Context Windows:

• Prompt Engineering: Carefully crafting prompts to include only the most relevant information can maximize the use of a smaller context window.
• Prompt Chaining: This involves breaking up a longer interaction into smaller parts and using the output from the model as a new prompt, effectively creating a chain of prompts and responses.
• Window Sliding: Implementing a sliding window technique where the context moves forward, keeping only the most recent tokens that are relevant for continued generation.
• Summarization: Summarizing the content that exceeds the context window allows the model to work with the gist of the text, rather than the full detail.
• P-Tuning: Leveraging prompt tuning, involves training a small set of learnable parameters that act as a continuous prompt. This method helps the model better utilize a small context window by embedding additional information into these tunable parameters, effectively expanding the context without needing more tokens.

For Large Context Windows:

• Selective Reading: Rather than using the entire available context, selectively focusing on key sections of the text that are most likely to influence the desired output.
• Hierarchical Approaches: Breaking down the input into a hierarchical structure can allow the model to first understand the broader context before diving into specifics, managing large context more effectively.
• Memory Augmentation: Adding external memory components that can be referenced by the model, which allows for context beyond the fixed window size.
• Dynamic Context Management: Using algorithms to dynamically adjust the context window size based on the complexity of the task at hand.
• P-Tuning: In addition to its use in small context windows, P-tuning can also optimize performance in large context scenarios by focusing the model’s attention on the most relevant parts of the context. This allows the model to ‘learn’ which parts of a larger context are most salient for a given task.

Model Architecture Workarounds:

There are optimizations and alternate architectural choices that can be implemented into the model itself to increase its performance (computationally, memory consumption, increased context sizes, so forth) that can be implemented in the language model’s architecture. These of course are not run-time workarounds like the those mentioned above.

• Checkpointing: Storing intermediate states of the model can help manage longer contexts by allowing the model to ‘remember’ past states without holding them in the immediate context window.
• Knowledge Distillation: Training a smaller model to mimic the behavior of a larger model can allow for more efficient handling of context while preserving performance.
• Hybrid Models: Combining transformer models with other types of neural networks that specialize in long-term dependencies can help to manage context more effectively.
• Sparse Attention Mechanisms: Utilizing models with more efficient attention mechanisms that can handle longer sequences without a proportional increase in computation, like the Longformer or Linformer.
• Memory-Augmented Transformers: These include architectures like the Transformer-XL, which incorporates a segment-level recurrence mechanism and a novel positional encoding scheme. It allows the model to maintain a longer context by using a memory of previous hidden states, thereby overcoming fixed-length context window limitations.
• Hierarchical Transformers: This approach involves processing text in a hierarchical fashion, where the model first processes smaller chunks of text and then aggregates these to understand longer passages. This can help in managing longer contexts without a proportional increase in computational demand.
• Adaptive Attention Span: Some transformer models can learn the optimal attention span for each head in the multi-head attention layers, allowing the model to focus on more relevant segments of the input sequence and ignore others, thus optimizing computational resources.
• Axial Attention: This technique factorizes the two-dimensional attention mechanism into two one-dimensional attentions, which can be particularly useful for tasks that involve structured inputs like images or for processing long documents by treating them as two-dimensional data.
• Compressive Transformers: These models compress old memories for long-term storage and retrieval, which can be especially useful for tasks that require maintaining information over long time scales, such as summarizing very long documents.
• Dynamic Convolution: A method that applies convolutional filters with dynamically generated weights for each position in the sequence, allowing the model to capture local context more effectively.

Closing Thoughts

As we continue to push the boundaries of what’s possible with language models, we’re finding that advancements are not solely about scaling up. The drive towards larger context windows in language models like GPT-4 is a testament to our pursuit of more human-like understanding and generation of text. However, this pursuit is tempered by the practical realities of computational demands and the quality of output.

Expanding the context window does indeed offer the potential for richer, more nuanced dialogue with machines. Yet, it’s crucial to navigate this expansion with a critical eye towards the trade-offs involved. The goal is not merely to process a larger swath of text but to enhance the relevance and coherence of what’s generated. More importantly, we aim to minimize errors — those unexpected ‘hallucinations’ that can emerge from the vastness of data these models contend with.

It’s clear that the future of language models will be shaped by a balanced approach to model design — one that weighs the benefits of larger context against the imperative to maintain quality and manage resources effectively. The real allure, then, lies not in the window’s size but in our ability to see through it clearly and discerningly, crafting language models that are as intelligent in their operation as they are expansive in their scope.