How to Use Large Language Models for Text Classification

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Let’s say that you want to refine a method for instructing a large language model (LLM) to categorize and rank a set of YouTube video descriptions based on their relevance to a specific topic. To achieve this, we’ll break down a prompt into its components and then explore how others have approached similar tasks, focusing on techniques that could be adapted or refined for your purpose.

Prompt: Analyze the following YouTube video titles and descriptions to determine what each one has in common with an article about the topic “$q”.

Original Prompt Analysis

The original prompt lacks specificity in several areas:

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  • It doesn’t specify how the LLM should handle the input (e.g., whether it should analyze text, metadata, or both).
  • It doesn’t define what “commonalities” means in the context of comparing video descriptions to an article about “$q”.
  • It doesn’t provide a clear metric for ranking the videos’ relevance.

Refining the Prompt

To refine the prompt, consider the following adjustments:

  • Specify Input Type: Clarify whether the LLM should analyze video titles, descriptions, or both.
  • Define Commonalities: Specify what constitutes a “commonality” between the videos and the article. This could involve thematic elements, keywords, or narrative structure.
  • Establish Ranking Criteria: Define the criteria for ranking the videos. This could be based on the presence of specific keywords, the frequency of mentioned concepts, or alignment with the article’s themes.

Web Search for Similar Tasks

To find out how others have tackled similar challenges, let’s conduct a web search focusing on:

  • Methods for analyzing and categorizing text data using LLMs.
  • Techniques for ranking content based on relevance to a specific topic.
  • Case studies of using LLMs for media content analysis (e.g., YouTube videos).

Web Search Query Suggestions

  1. “How to use large language models for text classification”
  2. “Ranking algorithms for content relevance based on machine learning”
  3. “Case studies of LLMs analyzing YouTube video descriptions”
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Potential Approaches Found

After conducting the web search, you might find several approaches that could be adapted for your task. Here are some hypothetical findings:

  • Keyword Extraction and Matching: One common method involves extracting keywords from both the video descriptions and the target article. Then, matching these keywords can serve as a basis for categorization and initial ranking. More matches could indicate higher relevance.
  • Semantic Similarity: Advanced techniques might involve calculating semantic similarity between texts using embeddings (e.g., BERT, GPT). Videos with descriptions semantically closer to the article could be ranked higher.
  • Custom Models for Content Analysis: Some researchers have developed custom models trained specifically for content analysis tasks, such as sentiment analysis or topic modeling. Adapting such models to your specific needs could yield precise categorizations and rankings.

Implementation Steps

Based on the insights gathered, you could proceed with implementing a refined version of your prompt that incorporates:

  • Clear instructions on the type of input to analyze.
  • Definitions of what constitutes a “commonality” between the videos and the article.
  • Criteria for ranking videos based on relevance.

Additionally, consider exploring the identified approaches (keyword extraction, semantic similarity, custom models) to develop a comprehensive strategy for categorizing and ranking the video descriptions according to their relevance to the topic “$q”.

Remember, the success of your implementation will depend on the clarity of your instructions, the quality of the input data, and the appropriateness of the chosen methodology for your specific task.

Are there any specific LLM that you could recommend for this task?

Given the task of categorizing and ranking YouTube video descriptions based on their relevance to a specific topic, several large language models (LLMs) stand out due to their capabilities in text analysis, understanding context, and handling large volumes of data. Based on the information provided in the sources, here are some recommendations tailored to your needs:


  • Why Recommended: GPT-4 is highlighted for its prowess in creating marketing content, which suggests it has strong capabilities in understanding and generating human-like text. Given that categorizing and ranking video descriptions involves interpreting textual content, GPT-4’s advanced natural language processing (NLP) skills could be beneficial.
  • Application: You could use GPT-4 to analyze video descriptions and titles, identifying keywords and themes related to your topic “$q”. Its ability to generate human-like text could also assist in summarizing or synthesizing information from multiple videos.


  • Why Recommended: Falcon is noted for its suitability as a human-like, conversational chatbot. While the primary application of Falcon might seem unrelated to your task, its underlying NLP capabilities could be leveraged for understanding and categorizing video descriptions.
  • Application: Falcon could potentially be used to preprocess video descriptions, extracting key points or themes that align with your topic. Its conversational nature might also offer insights into how to frame queries or instructions for the model.

Llama 2

  • Why Recommended: Llama 2 is praised for being a free, resource-light, customizable LLM. This makes it an attractive option for projects with budget constraints or scalability concerns.
  • Application: Given its flexibility and efficiency, Llama 2 could be utilized for analyzing video descriptions in real-time or batch processing, depending on your project’s scale. Its customization options could also allow for tailoring the model to better suit your specific categorization and ranking criteria.


  • Why Recommended: Cohere is recommended as the best enterprise LLM for building a company-wide search engine. This suggests it has robust capabilities in searching and retrieving relevant information from large datasets.
  • Application: Cohere could be particularly useful for indexing and searching through a vast collection of video descriptions to quickly identify those most relevant to your topic. Its enterprise-grade features might also support scaling your project as it grows.

Claude 3

  • Why Recommended: Claude 3 is noted for its large context window capability. This feature is crucial for tasks that require understanding the broader context of a piece of text, which is essential for categorizing and ranking video descriptions based on their thematic relevance.
  • Application: Claude 3’s ability to process longer contexts could enhance the model’s understanding of video descriptions, enabling it to better discern connections and similarities to your topic “$q”.

Choosing the Right Model

When selecting an LLM for your task, consider the following factors:

  • Task Requirements: Evaluate the specific needs of your project, such as the volume of data, the required speed of processing, and the level of customization needed.
  • Resource Availability: Take into account the computational resources available to you, including hardware and budget constraints.
  • Model Capabilities: Align the model’s strengths with your project goals. For instance, if contextual understanding is paramount, Claude 3 might be the best choice.

Ultimately, the selection of an LLM should be guided by how well the model’s capabilities match your project’s objectives and constraints. Experimentation with different models and configurations may also be necessary to find the optimal setup for your specific use case.

Further reading ...
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Integrating large language models (LLMs) like Falcon, GPT-4, Llama 2, Cohere, and Claude 3 into existing systems depends on several factors, including the model’s licensing, the infrastructure required for deployment, and the specific integration needs of your project. Let’s examine the integration capabilities and requirements for the models mentioned in the context of existing systems:


  • Integration Capability: Falcon is highly versatile and can be integrated into applications and end-user products [2]. It’s offered under the Apache 2.0 license, which allows for legal use in commercial settings [1].
  • Infrastructure Requirement: Falcon requires significant GPU memory for its larger versions (e.g., Falcon-40B needs 90 GB). However, smaller versions like Falcon-7B can run on regular consumer hardware with around 15 GB of GPU memory [1]. This flexibility in hardware requirements makes Falcon accessible for integration into a variety of systems.
  • Data Augmentation and Virtual Assistant Applications: Falcon’s capabilities extend beyond traditional LLM tasks, including data augmentation and serving as a virtual assistant. These functionalities can be particularly useful for enhancing existing systems with synthetic data generation or interactive customer support features [1].


  • Integration Capability: While specific details about GPT-4’s integration capabilities were not provided in the sources, GPT-3, its predecessor, has been widely integrated into various platforms and services. Assuming GPT-4 maintains similar integration capabilities, it should be feasible to integrate it into existing systems.
  • Infrastructure Requirement: GPT-3 requires substantial computational resources, typically involving cloud-based GPUs or TPUs. The exact requirements for GPT-4 would likely be similar, suggesting that integrating it into existing systems would require access to suitable computational infrastructure.

Llama 2

  • Integration Capability: As a free, resource-light model, Llama 2 is designed to be easily integrated into projects with budget constraints or scalability concerns [1]. Its lightweight nature suggests it can be deployed in a wide range of environments, from personal computers to cloud-based servers.
  • Infrastructure Requirement: Llama 2’s efficiency makes it suitable for integration into systems with varying levels of computational resources. Its customization options could also facilitate adaptation to specific integration needs.


  • Integration Capability: Cohere is positioned as an enterprise-level LLM, indicating it’s designed with integration into business systems in mind. Its capabilities for building company-wide search engines suggest it can be seamlessly integrated into existing IT infrastructures [1].
  • Infrastructure Requirement: As an enterprise solution, Cohere likely requires robust server infrastructure to support its operations. Integration would benefit from a stable network connection and sufficient storage capacity to manage large datasets.

Claude 3

  • Integration Capability: Specific details about Claude 3’s integration capabilities were not provided in the sources. However, considering its emphasis on large context windows, it’s reasonable to assume that Claude 3 would require integration efforts focused on managing large volumes of text data efficiently.
  • Infrastructure Requirement: The need for processing long contexts suggests that Claude 3 might demand significant computational resources, similar to other large-scale LLMs. Integration into existing systems would likely require access to powerful computing infrastructure.


Most of the mentioned LLMs, including Falcon, GPT-4, Llama 2, Cohere, and Claude 3, can be integrated into existing systems, albeit with varying degrees of ease and resource requirements. Falcon stands out for its versatility and accessibility, even on consumer hardware, making it a strong candidate for projects with limited resources. GPT-4, assuming similar capabilities to GPT-3, would require substantial computational resources for integration. Llama 2’s efficiency and customization options make it appealing for projects needing a balance between cost and performance. Cohere’s enterprise focus positions it well for corporate integration, while Claude 3’s emphasis on context processing suggests it would require significant computational resources for effective integration.

Further reading ...
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