Understanding Language Models

The introduction of how language models work needs to come with a basic explanation. LLMs utilize advanced deep learning techniques for processing and generating text. In practice, this means that based on the provided text fragment, the model can predict the following words or entire sentences with a certain probability. For example, if the model receives the sentence fragment "The color of the sky is," it can predict that the next word will be "blue" with a probability of 91%. Such a prediction illustrates the model's ability to understand context and generate responses that are meaningful and consistent with the expected meaning.

The Process of Transforming a Prompt into a Response in an LLM

The process of transforming a prompt into a response in an LLM involves several key steps:

  1. Prompt: The initial query or statement provided by the user.
  2. Tokenization: Breaking down the input text into smaller units called tokens, which can be words or subwords.
  3. Embedding: Converting tokens into numerical vectors that capture their semantic meaning.
  4. Processing through a Transformer: Utilizing transformer architecture to process these embeddings, leveraging self-attention mechanisms to understand the context.
  5. Generating a Response: Creating a coherent and contextually appropriate response based on the processed information.
  6. Detokenization: Converting the generated numerical vectors back into human-readable text.

What’s a token?

The description above indicates that tokenization is a crucial step, which allows an LLM to handle our prompt. Using tools like Tiktokenizer, we can precisely observe how the tokenization process works in practice. A sentence is broken down into tokens, and each token is assigned a numerical value. For example, the question "What is RAG and long context LLM?" is tokenized into discrete units, which are then processed by the language model to understand and generate responses.

Token representation

We can illustrate the ranges of tokens and their equivalents in the number of words, along with usage examples:

  • 1 token: Represents approximately ¾ of a word, useful for processing individual words or their parts.
  • 100 tokens: Equivalent to around 75 words, suitable for short paragraphs.
  • 2048 tokens: Approximately 1536 words, ideal for short articles or essays.
  • 128,000 tokens: Can encompass about 96,000 words, sufficient for processing entire books or extensive reports.

Long-context LLM Features

Long-context LLM-s are such LLM-s that are capable of processing a much larger number of tokens at a time. The comparison between the two covers various aspects:

  • Attention Mechanism: Traditional LLMs use full attention, while long-context LLMs employ more efficient attention mechanisms.
  • Computational Complexity: Traditional models have quadratic complexity (O(n^2)), whereas long-context models are optimized for better performance.
  • Sequence Length Limit: Traditional LLMs handle up to 2,048 tokens, while long-context LLMs can process up to 1,000,000 tokens or more.
  • Memory Reduction Techniques: Long-context LLMs use segmentation and context compression techniques to handle large contexts.
  • Applications: Traditional LLMs are suitable for short texts and translations, whereas long-context LLMs excel in processing long documents and complex analysis.

Retrieval Augmented Generation (RAG)

We’ve already discussed LLM-s, but RAG is another technique used in generative AI models. It integrates retrieval mechanisms with generation models to improve accuracy and context relevance. In contrast to LLM-s, this process involves:

  • Embedding: Converting queries and documents into vectors.
  • Vectorstore: Storing these vectors in a searchable database.
  • Retrieval: Finding relevant chunks of information based on the query.
  • Filtering/Compressing: Ensuring that only the most pertinent information is used.
  • Generating a Response: Combining the retrieved information to generate a coherent response.

Applications, Benefits, and Challenges of RAG

RAGs, due to their operation through the use of vectors, perform extremely well with tasks like analyzing FAQ systems, document translation, report generation, internet search support, or providing answers based on an input text. Additionally, they have access to current data, and their answer precision is considerably improved. This technology is also characterized by reduced hallucinations and easier debugging.

However, RAGs are not perfect, as they struggle with multiple challenges. Problems that have been noticed while using the technology are data quality management, text segmentation, selecting the right embeddings, and determining the number of responses to use.

Comparison of Long-context LLM-s and RAG

The comparison highlights differences in processing time, flexibility, use cases, data privacy, and costs. Below, you can see the most notable distinctions between the technologies:

  • Processing Time: Long-context LLMs are optimized but can face delays with larger contexts. RAG can potentially reduce delays through targeted data retrieval.
  • Flexibility and Adaptation: Long-context LLMs are simpler for teams unfamiliar with RAG, while RAG offers greater flexibility but can be complex to build and maintain.
  • Applications: Long-context LLMs are better for scenarios requiring broad context understanding, while RAG excels in targeted information retrieval.
  • Data Security and Privacy: Long-context LLMs may rely on external providers, while RAG allows for better control over data security.
  • Costs: Long-context LLMs involve higher infrastructure and training costs, while RAG can be more resource-efficient with lower computational costs.

Conclusion

In summary, long-context LLMs and RAG represent advanced techniques in the field of natural language processing, each with its unique strengths and applications. Long-context LLMs excel in processing extensive texts and maintaining context, while RAG enhances precision by integrating external data retrieval. Understanding their respective features and use cases can help in selecting the appropriate model for specific tasks, balancing between context depth, computational efficiency, and flexibility.

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