Have you ever wondered why ChatGPT sometimes provides an incomplete output? ChatGPT may provide a truncated output if the input or output exceeds the maximum sequence length or token limit of the pre-trained model. In addition, if the model encounters an unfamiliar or ambiguous context, it may struggle to generate a coherent response, which could also lead to truncated output. It's important to keep these limitations in mind when using ChatGPT and to ensure that your input and output are within the token limits of the pre-trained model.

What are Tokens in LLMs?

Tokens are the fundamental units of text that a Large Language Model or LLM processes. In OpenAI Playground, the LLMs use a pre-trained model that has a fixed limit on the number of tokens it can process as input and generate as output.

What this means in practice is that the length of your input determines the maximum length of your output. If you are using Chat-GPT,

Please provide a code sample for plotly-dash dashboard.

LLM-X's response would be limited to 945 characters.

Tokens are generally composed of a sequence of text characters. In some cases, tokens can be described as words, but other classifications of tokens exist as well.

Let's discuss some of the different kinds of tokens that can be used by LLMs.

1. Word tokens are the most common type of token in LLMs and represent individual words in a sentence. Word tokens can be useful for tasks like language translation and sentiment analysis.

2. Sub-word tokens are tokens that represent parts of words or phrases, rather than entire words. Sub-word tokens are useful for handling rare or out-of-vocabulary words and can help improve the accuracy of language modeling tasks.

3. Character tokens are tokens that represent individual characters in a sentence. Character tokens can be useful for handling misspelled or informal language, and can also help with tasks like named entity recognition where a single term can consist of multiple discrete words.

4. Byte-pair encoding (BPE) tokens represent variable-length character sequences. BPE tokens are commonly used in NLP tasks and can improve the performance of LLMs on languages with complex character sets.

Different types of tokens may be more suitable for different tasks, and the choice of tokenization method can have a significant impact on the performance of an LLM. It's important to select an appropriate tokenization method based on the specific requirements of your NLP task.

Maximum Sequence Length Parameter

The maximum sequence length parameter is a key factor in limiting the number of tokens an LLM can process or generate. This parameter sets the maximum number of tokens the model can accept as input or generate as output. If the input or output exceeds this limit, the model will truncate the sequence and ignore any tokens beyond the limit.

In addition to the maximum sequence length, LLMs may also have other restrictions on the tokens they can process or generate, such as limitations on certain types of characters or words, depending on how they were pre-trained.

It's important to keep these limitations in mind when using LLMs in OpenAI Playground, as exceeding the token limit can result in incomplete or truncated output, or cause the model to fail to generate any output at all.

Token Limitations and Prompt Strategy

Let's say you are using the GPT-2 LLM in OpenAI Playground to generate some text based on an input prompt. The maximum sequence length for GPT-2 is 1024 tokens for both input and output.

If you provide an input prompt that is longer than 1024 tokens, the model will truncate the input and ignore any tokens beyond the limit. For example, if your input prompt is:

"Today I woke up early and decided to go for a long walk in the park. As I was walking, I saw a group of people practicing tai chi, and I decided to join them. We spent the next hour practicing different movements and breathing exercises, and I felt incredibly refreshed and energized afterwards."

This input prompt is 137 tokens long, which is well within the token limit for GPT-2. However, if your input prompt is longer than 1024 tokens, the model will only process the first 1024 tokens and ignore the rest of the input.

Similarly, if you ask the model to generate output that is longer than 1024 tokens, the model will truncate the output and return only the first 1024 tokens. For example, if you ask the model to generate a 2000-word essay, the model will only return the first 1024 words and ignore the rest.

As a user, you can maximize the performance and accuracy of ChatGPT by ensuring that your input and output are within the token limits of the pre-trained model, and by providing clear and unambiguous input. By keeping these limitations in mind, you can achieve optimal results in your NLP tasks with ChatGPT.

🤖 Christine Egan | linkedin | [email protected]