Відео

Always wanted to see this type of content

Unfortunately even the so called power users of LLM with their own UA-cam channels, always seem to have a small set of stock prompts, which get repeated with every new review. If LLMs were trained on these specific questions then they're going start appearing super intelligent! Things like 'why is the sky blue', or write a 'snake game in python' are hardly a test of machine intelligence, as all that is needed is to be trained in accurate code or factual data.

I really value what you do my dude <3

Bot designer ltd crashed, I not able to access

Why not just use RAPTOR instead?

Excellent! I didn't know you before and I just stumbled upon your video while searching for material on prompting LLMs (for a local LLM project). You now have a new sub :)

can you discuss DSPy and give your opinion on it given how it is related to prompting

Nice video thank you very much !

Excellent review! Thank you. I really enjoyed this book and have been recommending it to people in order to help them set expectations about “AI” without excessive optimism or pessimism. Currently reading Deep Utopia; hope to see your review soon!

I think the main problem is how they traverse the graph by asking LLM at each step. I'm not sure if this is feasible in production.

Great paper, thanks for covering!

Excellent content. Well explained.

Sir, Do you explore the vision domain as well or do you any such youtuber whom i can follow for paper understanding.

very well "ragged"... both on the local domain (details) and global domain (overview of pros-cons) 😉😎

Thank you for the video, that was a great paper to go through. I find RAG research techniques have so much insight to how we can develop and identify our own cognitive impediments to our own judgement. The Comprehensiveness, Diversity of perspective, Empowerment and Directness is such a good mental model to use in our own human judgement.

super excellent video

Amazing explanation Vivek

great presentation Vivek. Some questions: - is graphRAG production ready ? if not, would it be difficult to upgrade RAG methods once we are in production ? - is there a RAG provider/stack that you prefer ? (datastax, pinecone, weaviate + a bunch of others who are all competing for attention) - what are your thoughts on LangChain vs LangGraph ?

Awesome review - thanks for the great work!

can you pontificate on the combination of upcoming transformer inference ASICs with deep agentic workflows employing GraphRAG style strategies? Seems like we will be close to our personal assistants writing a PhD thesis in the background whenever we ask a question. SOHU is reporting 500,000 tokens per second with Llama3 70B....

Seems clear that for 'current events' rag is going to win, but for broader, domain specific themes or logic, how does fine tuning stack up? E.g. create code using our internal suite of APIs... If context is big enough, icl should be fine, but rag may miss some key docs based on semantic similarity alone... I guess... I should write a paper 😂

Very interesting papers, thanks for covering!

It's surprising to see ChatGPT achieving below 99% when Wolfram Alpha can basically answer anything just by having specific knowledge. It's also surprising to see that "playing" with the word prompt does anything at all yet alone give a better result. It makes no sense especially when we can clearly see from the research the information entropy is basically the same between prompts with and without extra steps.

Is it flattening out because it maxes at %100?

Thank you for saving my group meeting! Your video helps a lot!

wouldn't it cut to the chase to train an llm on your own data? theres your graph use one of these OpenAI's GPT-3/4 Hugging Face Transformers (e.g., GPT-2, GPT-3 via third-party providers) Google's T5 (Text-to-Text Transfer Transformer) Meta's BART and BlenderBot Anthropic's Claude every week update the llm summarization is the death of real data, better off one level of summarization? Just a thought!

Excellent content summarizing cutting edge approaches, thank you!

Very interesting paper! Looks like team lead model and a bunch of juniors 😅 Thanks for covering!

Are there any models where we can try this think and exe method for ourselves?

Nice presentation thank you!

Really great explanation, I’m currently struggling to decide on my own KG structure for a 2000 medical pdf corpus, so this was very helpful

Thanks for sharing the review Vivek! Would you mind sharing your book list as well?

Really appreciate your in depth review of the book! This provides more thoughtful reading when I start the book.

Thanks for sharing the paper. In my experience with using Llama3-8B, in my benchmark dataset, I noticed that LLM has learned an incorrect fact or in contradiction with my application. I tried to clarify that in the prompt, but noticed the LLM is actually quite stubborn, and lead to quite fragile responses, i.e. the LLM sometimes get it right sometimes get it wrong with minimal changes in the prompt, could be as small as adding spaces. I wonder if you have come across similar situation or papers that discuss this behavior. Thanks.

I love this kind of content, please never stop !!!

Awesome work! Thanks🤗

More agent paper please. Thanks 😊

Nice video ty

You ask vanilla questions if you can't un-cloak a machine response!. The reasoning is not there with language models, how stupid are people to not be able to ask the right questions? I call lies on these claims. Show the test as proof, I doubt you can or will show the actual test. This is absurd.

But why is the paper's fine tuning different than the original pre-training and alignment fine tuning that came before it. All expose the model to a mix of existing and new data...

Interesting! Tnx

Very interesting paper, thanks for covering!

Just wanted to say I really appreciate your videos. Everything is short and concise and I love that you’re always using papers as the foundation for the conclusions. Keep it up!

Good brother 😊thanks for an excellent explanation , Peer 2 peer of BTC formula. I’ll eat this info into my brain 🧠 until my neurons starting a new circuit.😂.

Very interesting paper, thanks for covering!

Great video! Why the > 1 was a mistake? Didn't it return False as it should?

Does this also mean that experts in their field might choose to wait for improved AI abilities so that they can do more than just superficial improvements? I predict that we will see a tsunami of low quality generations followed by a true paradigm leap in terms of content.