RE: LeoThread 2024-09-10 12:25

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RE: LeoThread 2024-09-10 12:25

ahmadmanga (72)in LeoFinance • 2 months ago

So, I asked #ChatGPT on how to feed an LLM valuable information. Where should I start and how? ChatGPT's response in comments below.

2 months ago in LeoFinance by ahmadmanga (72)

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ahmadmanga (72) 2 months ago

Prompt: "I have an LLM program that always finetunes itself on the data in my server. I want to feed it as much valuable information from my own language as possible. I can only put text files in my server, and not actual datasets. The LLM is based on Llama3.x. If I want everything to provide to it to be valuable and accurate, where I should and how?"

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ahmadmanga (72) 2 months ago

ChatGPT's response:

To ensure that the information provided to your LLM is valuable and accurate, consider the following steps:

1. Curate the Text Sources

Domain-Specific Texts: Include texts that are highly relevant to your domain or area of expertise. These can be articles, research papers, or technical documentation in your language. For example, if your platform focuses on specific industries or services, compile relevant documents.
Quality Control: Ensure the text comes from reputable and verified sources to avoid feeding the model with inaccuracies or biases.

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ahmadmanga (72) 2 months ago

2. Preprocessing the Texts

Clean the Texts: Before putting them in the server, make sure to remove irrelevant content (e.g., advertisements, footnotes, repeated sections). You can use text-cleaning scripts to automate this process.
Structured Information: Segment texts into structured categories (e.g., tutorials, conversational dialogues, FAQs). This allows the LLM to better learn from the context and purpose of each text.

3. Diversity and Variety

Language Variety: Provide a broad spectrum of writing styles, such as formal, informal, technical, and creative writing, to help the model learn from different registers of language use.
Different Formats: Include different types of documents such as blog posts, essays, conversations, and narratives. This makes the model more versatile in understanding and generating various text types.

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ahmadmanga (72) 2 months ago

4. Language-Specific Texts

Native Language Content: If the goal is to strengthen the model in your own language, include as much native language content as possible, such as cultural books, literature, websites, and social media dialogues.

5. Regular Updates

Dynamic Content: Continuously add new, updated, and relevant information from credible sources to ensure the LLM stays updated with current knowledge in your domain.

6. Monitor the Finetuning

Track Performance: Regularly evaluate the LLM’s outputs after it finetunes on new data to ensure that the quality and accuracy remain high. You can create benchmark tasks for this purpose.

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