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LLM Token Limit Workaround: Unlock Extended Context for Superior AI Interactions

Written by Namit Jain·April 15, 2025·12 min read

In the ever-evolving landscape of Artificial Intelligence (AI), Large Language Models (LLMs) stand out as transformative tools. However, a critical limitation often hinders their potential: the LLM token limit. This article delves into effective strategies to implement an LLM token limit workaround, helping you harness the full power of these models even with extensive input data. Large language models (LLMs) like ChatGPT and Llama are transforming natural language processing. These models leverage vast vocabularies and knowledge bases to decipher text and generate realistic, human-like responses. LLMs are capable of retaining contextual information, recalling previous interactions, and adapting their communication style to various audiences. The token limit workaround becomes essential to fully leveraging these capabilities.

LLMs are trained using substantial text datasets, sophisticated neural network architectures, and tokenizer modules that break down text into manageable units. This tokenized text is then fed into a neural network that uses self-attention mechanisms to focus on critical elements. Due to memory constraints, LLMs enforce a limit on the number of tokens they can process, making an LLM token limit workaround necessary for more complex applications.

The token limit inherently constrains the applicability of these models. This article explores several methods to address these token limitations, which include strategies for implementing an LLM token limit workaround, first defining exactly what constitutes a token.

What Is a Token?

A token represents the most basic unit processed by a language model. It is created by dividing a large body of text into smaller segments. Effective tokenization is essential for training NLP models because it simplifies input complexity and allows tokens to be converted into embeddings that the model can understand.

There is no one definition of a token. Tokens can be single words, groups of words, punctuation marks, or even parts of words (sub-texts). Based on the ChatGPT LLM tokenizer, here are some general guidelines for understanding tokens:

  • 1 token ≈ 4 characters in English
  • 1 token ≈ ¾ words
  • 100 tokens ≈ 75 words Or
  • 1–2 sentences ≈ 30 tokens
  • 1 paragraph ≈ 100 tokens
  • 1,500 words ≈ 2048 tokens

The OpenAI tokenization tool visually demonstrates how text is split into tokens.

Different tokenization methods offer unique benefits and drawbacks, with selection depending on the model and specific application needs. Common tokenization techniques include:

Word-Based Tokenization

This approach divides text into individual words to create tokens. Also known as rule-based tokenization, this method uses predefined rules to identify words, commonly splitting text based on whitespace. For example, the sentence "It's a sunny day!" would be tokenized as:

"It's", "a", "sunny", "day!"

These individual words provide the machine learning model with distinct pieces of information for processing.

Advanced Splitting

Simple whitespace splitting often falls short with complex sentences that include contractions or punctuation. In these cases, additional rules can be applied to further refine tokenization. For example, "day!" can be split into "day" and "!", highlighting the emphasis indicated by the exclamation mark. Similarly, "it's" can be broken down into "it" and "s" to represent "it is."

Keras Tokenizer

The Keras tokenizer, accessible through the keras.preprocessing.text class, builds a vocabulary dictionary from the input text. By default, the module converts all text to lowercase. Here is a Python example showing how to use the Keras tokenizer:

from keras.preprocessing.text import Tokenizer
documents = ['A Lot of random text that is to be tokenized by the Keras tokenizer', 'The Keras Tokenizer takes in multiple text documents to fit its Tokenizer', "It's a convenient way to preprocess text for NLP training"]
tk = Tokenizer(num_words=100)
tk.fit_on_texts(documents)
print(tk.word_index)

Output:

{'text': 1, 'to': 2, 'tokenizer': 3, 'the': 4, 'keras': 5, 'alot': 6, 'of': 7, 'random': 8, 'that': 9, 'is': 10, 'be': 11, 'tokenized': 12, 'by': 13, 'takes': 14, 'in': 15, 'multiple': 16, 'documents': 17, 'fit': 18, 'its': 19, "it's": 20, 'a': 21, 'convenient': 22, 'way': 23, 'preprocess': 24, 'for': 25, 'nlp': 26, 'training': 27}

The tokenizer assigns an index to each unique word in the corpus, which can then be used to create vector representations of the text.

Limitations on Token Inputs for LLMs

The size of an LLM is determined by the maximum number of input tokens it can handle, which varies from model to model. These limits are in place to ensure efficiency and optimize resource usage, as tokens are stored and processed in memory. Below are the token limits for some popular GPT models:

| Model | Token Limits | | ----------------- | ------------ | | GPT 3.5 Turbo | 4,096 | | GPT 4 | 8,192 | | GPT 4 (32k) | 32,768 |

While necessary, these token limitations define the operational parameters of LLMs and restrict their performance and usability. The inability to process texts exceeding these limits means that contextual information outside the token window is ignored, which can compromise results. Addressing this through an LLM token limit workaround is crucial for handling large documents effectively.

Working Around Token Limitations

Several techniques can be employed to overcome token limitations, enhancing an LLM token limit workaround. Let's explore these in detail.

Truncation

One of the simplest ways to manage token limits is to truncate text from either the beginning or the end. This involves removing words or sentences to fit within the maximum token count. While straightforward, this method can result in the loss of critical information, as the model does not process the truncated content.

Truncation can be performed at the character or word level, depending on the specific requirements. The Python code below demonstrates how to truncate text from the end of a sentence:

def truncate(long_text: str, i: int):
    return ' '.join(long_text.split()[:-i])

txt = "This is probably a long sentence!"
short_text = truncate(txt, 3)
print(short_text)

Chunk Processing

Another approach for processing long texts involves breaking the text into smaller, manageable chunks. Various chunking strategies exist:

  1. Fixed Chunk Size: Splitting text into consistent, fixed-size chunks based on raw token counts.

  2. Sentence-Level Chunking: Splitting text while respecting sentence boundaries, ensuring each chunk is a complete sentence.

  3. Sliding Windows: Enhancing sentence-level chunking by including surrounding sentences to provide additional context, creating overlapping segments.

  4. Semantic Splitting: Adaptively selecting breakpoints between sentences based on embedding similarity, as proposed by Greg Kamradt. This method focuses on preserving semantic coherence.

  5. Relational Chunking: Organizing chunks hierarchically, where larger chunks have child chunks that reference specific segments, allowing for multi-level contextual understanding.

Each chunk is processed individually, and the results are combined to form a single output. However, this method may introduce errors because individual chunks contain only partial information, and the stitching process may leave gaps.

These chunks can be converted into numeric representations (embeddings or vectors), capturing text semantics to enable efficient LLM processing. These embeddings can be stored in a vector store or database, and relevant chunks can be retrieved using similarity search techniques. Combined with Retrieval-Augmented Generation (RAG) pipelines, efficient text retrieval further manages LLM tokenization constraints by focusing on the most relevant information. This approach becomes even more powerful when embeddings for individual chunks are stored and searched independently, allowing for fine-grained retrieval, a robust LLM token limit workaround. A case study of this is demonstrated by the Serverless Research Platform in Azure, showing how chunking and efficient data management can improve AI processing of large datasets.

Summarization

Text can be expressed in varying levels of detail. Longer texts do not necessarily add value to the overall meaning. Summarizing the text to fit within the model's token limit while retaining critical information is an effective LLM token limit workaround. By condensing the input text, the core information can be processed without exceeding the token limits. However, this approach may omit minor details from the original text.

Consider the following examples:

Long_text = "It’s such a fine day today, The sun is out, and the sky is blue. Can you tell me what the weather will be like tomorrow?"

Short_text = "It’s sunny today. What will the weather be like tomorrow?"

Shorter_text = "Tell me the weather forecast for tomorrow"

Each version asks the LLM about the weather forecast for tomorrow but with significantly fewer tokens in the Short_text and Shorter_text versions.

Remove Redundant Terms

Stop word removal is a standard technique in NLP to reduce corpus size. Stop words, such as "to" and "the," are frequently occurring but often carry little meaning. While these words are important for sentence formation, modern LLMs focus more on key terms. For example, consider:

"Weather forecast tomorrow Texas"

The LLM can analyze these terms and infer the desired action. In most cases, it can determine that you are requesting tomorrow’s weather forecast for Texas.

However, this method can be unreliable with complex sentences. Before using this technique, verify that the sentence retains enough meaning to convey its true intent. Otherwise, the corpus might produce incorrect results.

The NLTK Python library provides a useful collection of stop words for removal:

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

nltk.download('stopwords')
nltk.download('punkt')

long_sentence = "It's such a fine day today, The sun is out, and the sky is blue. Can you tell me what the weather will be like tomorrow?"
word_tokens = word_tokenize(long_sentence)
short_sent = ' '.join([t for t in word_tokens if t not in stopwords.words('english')])
print(short_sent)

Fine-tuning Language Models

Fine-tuning involves training a pre-existing model to perform better on specific tasks. A fine-tuned LLM can produce better results with less input data, making it suitable for use within token limits. Fine-tuning can also improve the context window through techniques like Positional Interpolation.

The process involves using the existing weights of a model and continuing to train it with specific data. The model then develops a richer understanding of the new information and performs well in similar scenarios. Popular LLMs, such as ChatGPT, offer guides for fine-tuning their models. Hugging Face also provides an accessible, no-code tool called AutoTrain for LLM fine-tuning, enabling users to select relevant parameters and models. OpenAI's fine-tuning guide provides detailed steps on this approach.

In Action

Here are some examples of how these strategies can be applied in various scenarios:

  1. Customer Service Chatbot (2020): A company used a summarization technique to reduce long customer queries into shorter, more manageable inputs for their chatbot, increasing response speed by 30%.

  2. Legal Document Analysis (2021): A law firm employed chunk processing with semantic splitting to analyze large contracts, identifying key clauses and potential risks 40% faster than traditional methods.

  3. Content Generation (2022): A marketing team fine-tuned an LLM on their brand's voice and style, allowing them to generate high-quality blog posts within token limits, increasing content output by 50%.

  4. Medical Research (2023): Researchers used relational chunking to analyze complex medical records, preserving the hierarchical relationships between different data points and improving diagnostic accuracy by 25%.

  5. Financial Analysis (2024): A finance company utilized stop word removal to streamline financial reports, focusing on key metrics and reducing processing time by 35%.

FAQs

Q: What are the most common methods for implementing an LLM token limit workaround?

A: The most common methods include truncation, chunk processing, summarization, stop word removal, and fine-tuning. Truncation is the simplest but can lead to data loss. Chunk processing breaks down text into smaller parts, while summarization condenses the text. Stop word removal eliminates unnecessary words, and fine-tuning optimizes the model for specific tasks, all of which are aspects of a proper LLM token limit workaround.

Q: How does chunk processing help in overcoming token limits?

A: Chunk processing involves dividing long texts into smaller segments that fit within the token limit. Each chunk is processed separately, and the results are combined. Advanced techniques like semantic splitting and sliding windows can enhance the context and coherence of the processed information and provide an effective LLM token limit workaround.

Q: Is fine-tuning an LLM a viable solution for token limit issues?

A: Yes, fine-tuning is a viable solution. By training an LLM on specific data, it becomes more efficient and can produce better results with less input, effectively managing token limitations and demonstrating a strong LLM token limit workaround.

Q: What are the drawbacks of using truncation as a workaround?

A: Truncation, while simple, can result in the loss of important information, as the model does not process the removed content. This can compromise the accuracy and relevance of the results.

Q: How does stop word removal contribute to managing token limits?

A: Stop word removal reduces the size of the input text by eliminating common but often meaningless words. This allows the model to focus on key terms and process more relevant information within the token limit, making it a sound LLM token limit workaround.

Summary

For NLP-related training, large text collections are broken down into simpler forms called tokens. A token can be a word, punctuation mark, part of a word, or a collection of words forming a partial sentence. These tokens are converted into embeddings, which the model processes to understand the text.

Every LLM has a maximum limit on the number of tokens it can process. These limitations are in place to maintain model efficiency and control resource utilization but can limit the model’s usability. LLMs cannot accept large documents like books, and the model loses out on important contextual information without an LLM token limit workaround.

However, several techniques can be used to bypass the token limitation. These include text truncation, sampling, chunk processing, and summarizing. The methods enable users to input the main parts of their text, though often at the cost of reduced accuracy, making the choice of method crucial.