Nehanth G.’s Post

Unveiling the Power of Retrieval-Augmented Generation (RAG) in Enhancing Large Language Models (LLMs) #RAG #LLMs #megnasoft https://lnkd.in/gKR5Jghq

Extending the Context Length of Large Language Models - An Overview Large language models (LLMs) like GPT-3 have revolutionized AI capabilities, but their predefined context lengths limit efficiency for long-form tasks. I explored techniques researchers are using to enhance LLMs' context capacity in my latest Medium article: 🚀 Summarization and chained prompts 🚀 Vector databases to store custom data 🚀 Fine-tuning with custom data 🚀 Developing smaller, data-centric models 🚀 NTK-aware methods that preserve high-frequency details 🚀 Dynamically scaling adjustments based on sequence length 🚀 Interpolating positional encodings for stability 🚀 Efficient long-context fine-tuning with LongLoRA Extending context length allows on-the-fly learning and more tailored performance when processing user-specific data. Analogy: like computer RAM retaining software context, extended context maintains an LLM's broader scope of user data. This enables more accurate and creative results without recalibration. Click below for the full analysis of techniques and implications for enhancing LLM context length: https://lnkd.in/gx3Dr9Zu Let me know your thoughts! What other methods show promise for improving LLMs' efficiency with long-form data? #LLM #AI #machinelearning #NLP #naturallanguageprocessing #deeplearning #artificialintelligence #languagemodels #transformers #attention #contextlength #efficiency #longform #personalization #finetuning #interpolation #modeltraining #modeloptimization #ondeviceAI #futureofAI #AItrends

🌐 Extending the Context Length of Large Language Models (LLMs): Unleashing Long-Form Efficiency Large Language Models (LLMs) like GPT-3 have redefined AI capabilities, but their fixed context lengths pose challenges for long-form tasks. 🚀 In my latest Medium article, I delve into the innovative techniques researchers are employing to augment the context capacity of LLMs: 📋 Key Techniques Explored: Summarization and Chained Prompts Vector Databases for Custom Data Storage Fine-Tuning with Custom Data Development of Smaller, Data-Centric Models NTK-Aware Methods Preserving High-Frequency Details Dynamically Scaling Adjustments Based on Sequence Length Interpolating Positional Encodings for Stability Efficient Long-Context Fine-Tuning with LongLoRA Extending context length empowers LLMs with on-the-fly learning, catering to more tailored performance when dealing with user-specific data. It's akin to computer RAM retaining software context, allowing LLMs to maintain a broader scope of user data for accurate and creative results without recalibration. 🚀 Read the full analysis: Extending the Context Length of Large Language Models 🤔 Share your thoughts! What other methods do you believe hold promise for enhancing LLM efficiency with long-form data? #LLM #AI #MachineLearning #NLP #NaturalLanguageProcessing #DeepLearning #ArtificialIntelligence #LanguageModels #Transformers #AttentionMechanism #ContextLength #Efficiency #LongForm #Personalization #FineTuning #Interpolation #ModelTraining #ModelOptimization #OnDeviceAI #FutureOfAI #AITrends 🌐🧠

🚀 Exciting Announcement! 🚀 I am thrilled to share with you all my latest blog post on Large Language Models (LLMs), a fascinating topic shaping the future of natural language processing! 📝✨ In this article, I delve into the intricacies of LLMs, exploring their significance, workings, and real-world applications. From understanding the basics of language modeling to unveiling the transformative power of models like GPT-3 and BERT, this blog takes you on an insightful journey through the realm of artificial intelligence and language processing. I owe a huge debt of gratitude to Kanav Bansal Sir for his invaluable guidance and mentorship throughout the process of crafting this piece. His expertise and encouragement have been instrumental in shaping my understanding of LLMs, and I am truly grateful for his unwavering support. I invite you all to read the blog and join the conversation on LinkedIn! Let's explore the endless possibilities that LLMs offer and celebrate the remarkable advancements in the field of natural language processing together. 💬🌟 #LLMs #ArtificialIntelligence #NLP #GPT3 #BERT #LinkedIn #Blog #Gratitude #Mentorship #AI #LanguageModeling

🚀 AI isn't just about using the tools—it's about understanding them too! 🤖 In the fast-evolving world of AI, Large Language Models (LLMs) like GPT have become integral in transforming how we interact with technology. While using these tools is crucial, knowing how they work behind the scenes can give you an edge in understanding their capabilities and limitations. 💡 Do you know that LLMs don't directly generate text, but instead calculate something called logits before selecting the next word? I've written a new article that debunks common misconceptions about LLMs and dives deep into their core mechanics—covering topics like: 1. Tokenization and embedding of text 2. How logits and probabilities are used 3. Different decoding strategies used for generating text 👉 Check out the article here

Enhancing Text Embeddings in Small Language Models: A Contrastive Fine-Tuning Approach with MiniCPM LLMs excel in natural language understanding but are resource-intensive, limiting their accessibility. Smaller models like MiniCPM offer better scalability but often need targeted optimization to perform. Text embeddings, vector representations that... https://lnkd.in/dsuxnkv2 #AI #ML #Automation

Fine-Tuning Large Language Models 🚀 The following one-hour workshop by Oren Sultan provides an introduction to fine-tuning large language models. The workshop covers topics such as fine-tuning approaches, prompt engineering, RAG methods, and demo fine-tuning process using LLaMA 2-7b-chat LLM. 📽️ https://lnkd.in/g3nhNmAb ⭐️ 𝑱𝒐𝒊𝒏 𝒎𝒚 𝑫𝒂𝒕𝒂 𝑺𝒄𝒊𝒆𝒏𝒄𝒆 𝑪𝒉𝒂𝒏𝒏𝒆𝒍 👉🏼 https://lnkd.in/g_GdP-pf #llm #datascience #rag #ai

Is "does AI understand meaning?" an inadvertently deceptive question? We are intent on chasing and catching butterflies with a jar made out of butterflies! Yes, that may just have to be the nature of any expansive #philosophy of language but the least we might do is to acknowledge the core intractability of seeking explanatory closure in this manner. Debates on machine #intelligence and comprehension are fascinating, if ultimately (and arguably) unresolvable. Meaning being pretty much whatever it is defined as, we find ourselves trying to assert certainties about #language, with language, while possessing absolutely nothing resembling an objective Archimedes point and inalienable anchor of semantic #certainty. We ask whether a neural network understands #meaning, even while we ourselves seem quite content to use language without ever once fully understanding it. Endemic relational (as semantic) ambiguity and #uncertainty in language is a core mechanism by and through which yet more language and communicative indeterminacy is invoked, assured, evolved.

Retrieval augmented generation (RAG) has improved the function of large language models (LLM). It empowers #generativeAI to create more coherent and contextually relevant content. Let’s take a deeper look at understanding #RAG : https://hubs.la/Q02jJ5Xp0

Introducing CulturePark: Boosting Cross-cultural Understanding in Large Language Models https://lnkd.in/gKRfQdx8 💡Agent-based data generation, filtering, and fine-tuning for cultural topics 💡GPT-3.5-based models outperforms GPT-4 in CulturePark 💡Verified in human learning