Research & Innovation 🧮

Ahoy, fellow token voyager! 🚀 Let's delve into the whimsical world of tokens. Tokens, those snippets of words, aren't exactly freebies, economically or computationally speaking. So, since tokens come with a price tag, it's crucial to optimize their usage without sacrificing the responses of our AI agents. Some platforms like Langchain and others have developed tools to keep tabs on token usage. But hey, customizing the format to use as few symbols and spaces as possible can significantly slash costs by using fewer tokens. Now, doesn't that sound like a thrilling challenge? 🤓💰

But wait, there's more! Deep down in the tech mug, models can't crunch tokens like a cereal breakfast. Instead, we represent them numerically as embeddings. Each token in a language model snuggles up with its very own embedding, capturing its essence and context amidst the sea of tokens in the training corpus. So, let's chat about making our data shorter and snappier.

Recently, Zhiquan Tan et al. investigated whether a token embedding contains all the information from its preceding context. The article "The Information of Large Language Model Geometry" investigates the information encoded in the embeddings of large language models (LLMs). The authors conduct a comprehensive study on the geometry of LLM embeddings and analyze their information-theoretic properties. They explore the relationship between the geometry of LLM embeddings and the information content of the underlying language. The findings of the study provide insights into how LLMs capture and represent information in natural language. The implications of these findings can be valuable for various applications of LLMs in natural language processing tasks. While specific details and findings are not provided in the given context, the article offers a deeper understanding of the information encoded in LLMs and its relevance to language processing.

The results of the study, as presented in the given context, indicate that as the input length increases, the normalized information gain also increases. However, this increase is more moderate when the models are larger. The difference in information gain decreases as the input length increases, but this decrease is less pronounced when the models are larger.📊🔍 Sometimes outperforms the closely related attention weights in identifying context tokens with high information content. I let you the paper below, enjoy!

🎉^{_{Little surprise:}}

We have received many comments about token consumption when loading a document in Playground! 🚀 Many are dealing with the uncertainty between the number of tokens and the file's weight. Keep in mind that it is not only important to consider the file size but also the amount of text it contains. Therefore, with the great help of Jose Gilarte, one of our users, we implemented support to help you approximate this calculation. Use it wisely Kimosabi 🥋, now work accurately with context documents in CodeGPT Plus.

The repo! 👾

🌟 Today, we recommend Tiktoken! 🚀 , the OpenAI library that was used to make the app to calculate the tokens. 🤖📜.

New at CodeGPT 🎁

Exciting updates have arrived in the V2 playground! Additionally, our marketplace is ablaze with possibilities as you can now create and share your agents with the vibrant CodeGPT community. Here are the latest trends:

• GPT-4 agent: Unveiling OpenAI's most potent model, CodeGPT introduces GPT-4. It surpasses ChatGPT in handling quantitative questions (math and physics), creative writing, and a plethora of other challenging tasks.

• Python agent: Your go-to hub for comprehensive Python programming guidance.

• OpenAI API agent: Your ultimate resource for navigating the OpenAI ecosystem. Dive into resources, tutorials, API documentation, and dynamic examples to leverage OpenAI's developer platform to the fullest.

• JavaScript agent: Delve into the fundamentals of JavaScript with confidence. Our comprehensive and authoritative source is here to address all your JavaScript-related queries.

  Grab the magic and more here! 🚀✨

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