Chain of Thought (CoT) meets Instruction Fine-Tuning

Explore the concept of "Chain-of-Thought" (CoT) combined with "instruction fine-tuning" as techniques to improve the performance of large language models (LLMs).
These techniques involve optimizing prompt structures and training the models to follow specific instructions, leading to enhanced capabilities in solving unseen tasks.
The combination of chain of thought and instruction fine-tuning has shown promising results in improving the model's performance and understanding of complex language tasks, also for smaller language models.
Furthermore, the video discusses the potential of AI models, particularly GPT-4, in simulating physical laws and human behavior. By leveraging the power of human language, these models can potentially describe and predict various simple aspects of human behavior in the real world. While there are limitations and challenges associated with accurately modeling human behavior, the video emphasizes the significance of language understanding and simulation as crucial steps in the current evolution of AI systems.
The video also mentions ongoing research and studies in the field, including the exploration of dynamic programming problems and the application of chain-of-thought methodologies. These studies demonstrate that models with chain of thought augmentation have the ability to solve decision-making problems and learn complex patterns more effectively.
Additionally, the video highlights the importance of prompt optimization and the potential of AI models to generate step-by-step explanations, thereby enhancing their ability to tackle complex tasks.
00:00 Intro
02:55 CoT and Instruct FT
05:17 CoT Example data set
06:13 Instruct Fine-tuning data set
08:54 FlanT5 fine-tuned on CoT Collection data set
11:32 CoT + Instruct FT for logical reasoning
17:40 Tree of Thoughts (ToT) for advanced reasoning
19:01 ToT and human behavior simulation
#languagemodel
#gpt-4
#promptengineering
#naturallanguageprocessing
#logic
#reasoning

[@danfox7356]

You have officially become my favorite channel. ❤

[@Smytjf11]

It's a hidden gem. I love the energy.

[@densonsmith2]

Thank goodness your website is finally up!

[@ricardocosta9336]

Nice my dude! As usual

[@henkhbit5748]

Thanks for your cot👍

[@code4AI]

Appreciated!

[@JonathanYankovich]

Really love it. Do you have a link to the code for having multiple instances of GPT4 talk to itself? I have been wanting to something similar, probably with LocalAI. Any existing code would be super helpful, even if it’s rough!

[@code4AI]

I'll have some videos touching upon it.

[@rafb145]

Does anyone have a fully working model?

[@nadavnesher8641]

love it

[@blablabic2024]

Did you test QLoRA? Idea of fine tuning LLaMA model on (almost) sub-1,000 $ GPU card (RX 7900 XTX) is rather tantalizing and possibly worth of 3,000-4,000 US$ workstation investment.

[@pensiveintrovert4318]

How about confirming the speculation in whatever crazy paper that academia paper mills produces? Collect lots of examples, not a single cherry-picked one. These models have memorized a lot of word trajectories. Some appear as reasoning to enthusiastic aiphiles.

[@code4AI]

I do not read crazy papers, therefore ....