Recent discussions in the AI community have focused on the challenges of reducing false positives and hallucinations in large language models (LLMs). Various approaches, such as fine-tuning LLMs with new knowledge or using retrieval-augmented generation (RAG) techniques, have been explored. New research and methodologies, like Lamini Memory Tuning, aim to improve LLM accuracy and reduce hallucinations significantly. These advancements are crucial for enhancing the reliability and performance of AI systems.
Are you in SF and interested in building with AI? You should join us at RAG++ next week! https://t.co/vBVgXmsC5G There will be talks from @LangChainAI, @DataStax, @UnstructuredIO, and @swyx, as well as hacking, dinner, drinks, demos and a whole bunch of awesome people to meet.
🤖🇺🇸 Supercharge Your Chatbot: Making AI Smarter with RAG! Dive into this brilliant guide on using retrieval augmented generation (RAG) to enhance your pre-trained LLMs for more relevant, context-aware responses! More info: https://t.co/ZkIEyOZ5aH https://t.co/OjsQhG6cVv
Was a blast to bring @weaviate_io and @neo4j together. RAG is even better when you combine vectors and graphs. Build sophisticated RAG applications with us in San Francisco. I do a hack night every month with Weaviate! https://t.co/fLIRE5R2P3
🚨 Hallucinations in medical AI are dangerous, but there's hope! Our new method (https://t.co/zPjUahXGtg) achieves up to ~5x reduction in hallucination errors in medical reports. Two steps to do this:👇
Learn how to harness Retrieval-Augmented Generation (RAG) with Llama 3. Build a RAG system, set up the model, and monitor performance using Weights & Biases. Read more: https://t.co/sbQ2zfwA6j https://t.co/vxqVdLf35y
Advanced Strategies for Minimizing AI Hallucinations with RAG Technology #AI #AItechnology #artificialintelligence #llm #machinelearning #RAG https://t.co/mWglGQtW6l https://t.co/XTda5Q7poO
A High Level Intro to Retrieval Augmented Generation (RAG) https://t.co/fRCqJsei40
🚨 AI breakthrough alert Startup Lamini has unveiled a new methodology that reduces hallucinations in large language models by 95%.
Reduce #AI #Hallucinations With This Neat Software Trick https://t.co/mXRdDhpbU5 #fintech #GenAI @reece___rogers @wired
Retrieval-augmented generation (RAG) is a leading way to curb "hallucination" in large language models. At this year's @icmlconf, Amazon researchers will show how to leverage item response theory to automatically generate "exams" for evaluating RAG approaches. #ICML2024
I'm excited for our RAG developers event next week. Come to learn from our ML team about how to build a scalable enterprise-ready RAG with @vectara. Have questions? Bring them with you https://t.co/J3CbsyR2xG
🌌 AI Fact of the Day: Unraveling the Mystery of AI Hallucinations! 🌌 Hallucinations can have real-world implications, affecting everything from medical diagnosis to judicial decision-making systems, emphasizing the importance of accuracy & reliability https://t.co/29ubEFxwL7
Huge if true! Basically, the way they significantly reduce hallucination is by tuning millions of expert adapters (e.g., LoRAs) to learn exact facts and retrieve them from an index at inference time. https://t.co/rDyNpky5VT
A buzzy process called retrieval augmented generation, or RAG, is taking hold in Silicon Valley and improving the outputs from large language models. How does it work? I asked the experts: https://t.co/XYiaVl8K7E
In addition... Lamini, a new way to embed facts into LLMs that improves factual accuracy and reduces hallucinations, claims 95% accuracy compared to 50% with other approaches and hallucinations reduced from 50% to 5% https://t.co/L9MTyn9sAn https://t.co/CVWGrpetLN
RAG is a common approach to reduce hallucinations in language models. While simpler and faster to implement than fine-tuning, its effectiveness depends on the quality of knowledge, implementation, and the size of the context window. Fine-tuning the model with large amounts of… https://t.co/gRxIrJnau3
RAG is one of the two most common ways to reduce hallucinations. RAG is a simpler approach, easier and faster to implement. However, it will depend on 3 things: the quality of knowledge, the quality of implementation, and inevitably, the size of the context window. No matter how… https://t.co/FCJuz2wpaV https://t.co/2Zd4pi8rF0
RAG combines real-time data retrieval with LLMs to reduce AI hallucinations by ensuring context-specific accuracy and is being refined for better scalability and efficiency. https://t.co/E2y3es3JwP
Excited to really start delving into our #SummerOfRAG content with this high level intro to retrieval augmented generation! In this #livecoding stream, we will cover an end-to-end RAG pipeline in a very high level, basic state. This will help to lay the groundwork and intuition… https://t.co/AbiHZ96RjS
🤖🇺🇸 Fight AI Hallucinations with This Clever Software Hack! Developers are turning to Retrieval Augmented Generation (RAG), a technique that could make generative AI tools far more reliable. Here's how it's done! https://t.co/JGJVz1MYMo
The countdown is on for RAG++, our high-energy hack party featuring the leading technology experts in generative AI ... and you! 🔥 Enjoy food, drinks, networking, and coding, and discover how to boost your development speed by 100x. 🚀 And that’s not all! Enter the raffle for… https://t.co/2OEHnBY2P1
This @LaminiAI memory tuning looks quite incredible Lamini Memory Tuning tunes a massive mixture of memory experts on any open-source LLM. Each memory expert acts like a LoRA adapter that functionally operates as memory for the model. Together, the memory experts specialize… https://t.co/puMcy1WHGX
Reduce AI Hallucinations With This Neat Software Trick https://t.co/gh5MZCPXNQ
Tech enthusiasts, buckle up! 🚀 The rise of RAG (retrieval augmented generation) in Silicon Valley is revolutionizing large language models. 🤖✨ #AI #RAG #TechInnovation https://t.co/JOAu606mSE
A buzzy process called retrieval augmented generation, or RAG, is taking hold in Silicon Valley and improving the outputs from large language models. How does it work? https://t.co/FsBVMXlR9j
Boost your conversational AI with Multi-Agent RAG! 🚀 Discover how this architecture: ⚡ Reduced latency 🎯 Improved relevance 🌟 Optimized prompting 🎥 Watch here: https://t.co/mjaKC6GzMq #ConversationalAI #Chatbots #AI #MachineLearning
How Retrieval Augment Generation (RAG) makes LLMs smarter than before https://t.co/Vx1NyACnCX #bigdata, #datascience, #datascience #ds, inoreader
Introducing Lamini Memory Tuning: 95% LLM Accuracy, 10x Fewer Hallucinations https://t.co/FqfPp8xxri
https://t.co/ieRsvT8eHP introduces Memory Tuning: 95% LLM Accuracy, 10x Fewer Hallucinations https://t.co/l9L814Kr9g
Something new and different: "Lamini Memory Tuning": - Train millions of LoRAs for specific facts - Retrieve top N for a query at inference time - Combine and run This apparently significantly reduces hallucinations. Great for limited/scoped domains. https://t.co/LNxJOcd3qY https://t.co/R77MXPKPpR
Build a #RAG system using #Milvus and @llama_index! See the full guide: https://t.co/najkirw917 https://t.co/UAr794gvzr
Tried an open LLM lately? They're not so great at the facts. We could use RAG, or fine-tuning, but is there another way? @LaminiAI thinks so, with a new Memory Tuning strategy. By creating a 1-million-way-expert tuned model, hallucinations reduced by 10x https://t.co/vcfYrczyCw https://t.co/iNlOIbnppo
Next week we will demystify RAG and show it in action in our livestream https://t.co/H6Rz2mb0VB #neo4j #graphrag
5 Ways To Battle AI Hallucinations And Defeat False Positives https://t.co/LX8cRpqFUy
RAG is not enterprise ready for LLM/Generative AI applications. This is not surprising for those of us who work with it. Yet, the hype machine goes on. Key findings, critical reflections, and suggestions based on this work 🧵⤵️ 1/4 https://t.co/7BTgaCmZXk
really interesting novel approach to reduce LLM hallucinations without fine tuning and loss of generality: train a mixture of a million experts, each LoRA’d with a fact that you want to memorize. https://t.co/mrVIftv0lA
Real-time #RAG apps need more than traditional vector DBs. #Redis offers: Vector search up to 50x faster (benchmarks soon) Semantic cache reducing LLM calls by 30% Advanced LLM memory for personalized and more accurate responses Designed for speed, scalability, accuracy, and… https://t.co/qrptHKnNrK
Introducing Unified RAG — a practical solution to the pitfalls of typical LLM apps. https://t.co/An9WSLx1dR
New AI research on removing hallucinations from LLMs: "Banishing LLM Hallucinations Requires Rethinking Generalization" by @LaminiAI Full paper: https://t.co/oCNJCenYRN Lamini-1 LLM weights: https://t.co/fYgW8UZnK9 tl;dr: https://t.co/Dwc4o3fv32 https://t.co/644je5Ac04
By creating an #AIassistant using @MyScaleDB and @LangChainAI with real-time data from Hacker News, let’s explore how to enhance your #RAG applications with advanced SQL vector queries, enhancing both the accuracy and efficiency of the data retrieval process. https://t.co/IOwUZrYDPJ
Excited to announce @LaminiAI Memory Tuning, a new research breakthrough! 🎉 ◽95%+ accuracy, cutting hallucinations by 10x ◽Turns any open LLM into a 1M-way adapter MoE (paper & Lamini-1 model weights on @Huggingface) ◽Fortune 500 customer case study on how they memory-tuned…
Let's learn how to evaluate a RAG application:
💡 Optimizing your AI's retrieval process can drastically reduce errors and boost performance. @atitaarora explains in this article the advanced strategies for optimizing RAG systems through an evaluation-based methodology. Here are some of the insights we found using… https://t.co/YYoOsHqPWs
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? ◼ New study reveals large language models struggle to learn new facts via fine-tuning, learning slower than pre-known data. This method also increases the risk of generating incorrect facts. Findings suggest… https://t.co/HHx9OIkwkV
Graph RAG in the wild! Our friends at @pingcap have put together a top-quality RAG application about their TiDB database using LlamaIndex’s knowledge graph functionality, and it’s open source! Try it out: https://t.co/JKOa6ab1Uh Or check out the code on GitHub:… https://t.co/MpV67F0JLs
From @DataScienceDojo >> #GenerativeAI — Optimizing Natural Language Interfaces — Comparing DSF (Domain-Specific Finetuning) and RAG (Retrieval-Augmented Generation) Techniques: https://t.co/1HFnk345tL #AI #GenAI #DeepLearning #MachineLearning #LLMs #DataScience #NLProc https://t.co/8H9tH2aEgF
This paper investigates the widely debated question. 🤔 Does Introducing new factual knowledge through fine-tuning an LLM will increase the risk of hallucinations. ❓ And the paper concludes - LLMs mostly acquire factual knowledge through pre-training, whereas fine-tuning… https://t.co/EkM5vbS4Hm
RAG directly from your CLI using LlamaIndex:
New paper 📢 Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? by Zorik Gekhman et al at Google Research 🧵 https://t.co/PDwt6mb5ei
Battling #AI Hallucinations : Detect And Defeat False Positives. #Forbes https://t.co/FgjG7EWgQl
Check out this insightful blog post on how to "Detect And Defeat False Positives" in AI hallucinations. The article provides valuable tips on identifying and addressing false positives in AI systems. Learn more here: https://t.co/gK8Zg7og0d