What is the difference between fine-tuning and search in training a model for question answering?

Fine-tuning is a transfer learning method that teaches a model a new task, while semantic search uses semantic embeddings to search based on content and context.

Why is fine-tuning not suitable for QA?

Fine-tuning only unfreezes a small portion of the model and does not retrain the entire model, making it unsuitable for QA.

Why is semantic search a better option than fine-tuning?

Semantic search is fast, easy, and cheap, allowing for scalable searches in large databases, making it a better option than fine-tuning.

Is fine-tuning reliable for acquiring new knowledge?

Fine-tuning is not reliable for acquiring new knowledge as it does not have a theory of knowledge or mind, making it an unreliable knowledge door.

How does one approach QA using semantic search?

To do QA using semantic search, start with a question, forage for information, compile a relevant corpus, extract salient bits, and produce an answer using a Dewey Decimal System for semantic search.

Maximizing QA Efficiency: GPT-3 Fine-tuning vs Semantic Search

Name: Maximizing QA Efficiency: GPT-3 Fine-tuning vs Semantic Search
Uploaded: 2023-03-18 21:34:20
Description: Fine-tuning language models is a successful transfer learning method, but it may not reliably lead to new knowledge, and semantic search is a better and cheaper option for QA.

This article is a summary of a YouTube video "OpenAI Q&A: Finetuning GPT-3 vs Semantic Search - which to use, when, and why?" by 4IR with David Shapiro

TLDR Fine-tuning language models is a successful transfer learning method, but it may not reliably lead to new knowledge, and semantic search is a better and cheaper option for QA.

Key insights

🎸

Fine tuning is like tweaking a guitar to improve the final performance, teaching the model a new task rather than new information.
🔍

Semantic search allows for searching based on the actual content and context of records, not just keywords or indexes, making it a powerful tool for next-gen databases.
💰

Fine-tuning is expensive and difficult, but it is a valuable skill that most people don't understand.
🤔

The question of how much information to share in the AI field is still open, with concerns about dangerous players using it for nefarious purposes.
💰

Fine tuning is slow, difficult, and expensive, while semantic search is fast, easy, and cheap, making it a more scalable option.
💡

Fine-tuning is good for teaching a model a pattern-based task, such as writing emails or coding in a specific language.
📚

Semantic search can be used to distill a large corpus of books down to just a few relevant pages of information.

Q&A

What is the difference between fine-tuning and search in training a model for question answering?

— Fine-tuning is a transfer learning method that teaches a model a new task, while semantic search uses semantic embeddings to search based on content and context.
Why is fine-tuning not suitable for QA?

— Fine-tuning only unfreezes a small portion of the model and does not retrain the entire model, making it unsuitable for QA.
Why is semantic search a better option than fine-tuning?

— Semantic search is fast, easy, and cheap, allowing for scalable searches in large databases, making it a better option than fine-tuning.
Is fine-tuning reliable for acquiring new knowledge?

— Fine-tuning is not reliable for acquiring new knowledge as it does not have a theory of knowledge or mind, making it an unreliable knowledge door.
How does one approach QA using semantic search?

— To do QA using semantic search, start with a question, forage for information, compile a relevant corpus, extract salient bits, and produce an answer using a Dewey Decimal System for semantic search.

Timestamped Summary

📝

00:00
Fine-tuning is a transfer learning method used in NLU and NLG tasks to teach a model a new task.
🔍

02:33
Semantic search uses semantic embeddings for fast and scalable searches in large databases, while fine-tuning is not suitable for QA.
🧠

04:41
Transfer learning is useful for adapting knowledge to new tasks, but fine-tuning may not reliably lead to new knowledge due to limitations in LLMs and OpenAI's lack of investment in cognitive architecture.
💡

07:02
Fine-tuning language models is successful but requires a new discipline to master.
💻

08:42
Fine tuning AI models is difficult and expensive, and sharing too much information can be dangerous.
💡

10:05
Semantic search is a better and cheaper option than fine-tuning for QA.
🤖

12:36
Fine-tuning with Curie can teach a model pattern-based tasks effectively.
📚

14:04
Use a Dewey Decimal System for semantic search to find relevant information, compile data, and answer your question.