Marc Pickett
Bethesda, Maryland, United States
1K followers
385 connections
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Coming out soon... Tomorrow at the AI Engineer World’s Fair, we will unveil our Orchestrator Agent—a meta-agent designed to intelligently route…
Coming out soon... Tomorrow at the AI Engineer World’s Fair, we will unveil our Orchestrator Agent—a meta-agent designed to intelligently route…
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CyberCureME - Cyber Security Marketplace
Mechanistic Interpretability 101: Written by Ken Huang, CEO of DistributedApps.ai and VP of Research at CSA GCR.Why are neural networks so notoriously difficult to interpret, and how have researchers attempted to crack this black box in the past? This blog post is an initial attempt to discuss this and introduce Mechanistic Interpretability (MI), a potential approach that may improve our understanding of AI. What makes MI different from traditional methods, and could it really outperform them? We'll explore the limitations of...
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NYU Center for Data Science
Researchers at CDS have unveiled that transformer language models can solve complex problems without clear intermediate reasoning, challenging existing assumptions about their operation. Led by CDS PhD students Jacob Pfau and William Merrill, and CDS Associate Professor of Linguistics and Data Science Sam Bowman, this study highlights the use of nonsensical filler tokens that achieve perfect accuracy in specific tasks, raising concerns about interpretability and accountability in LLMs. Explore the full findings: https://lnkd.in/eERzzKsU
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Rohit Agarwal
🚀 Exploring Extrinsic Hallucinations in LLMs! 🚀 The article delves into the causes and solutions for hallucinations in large language models (LLMs). 🌟Key highlights: - Causes of Hallucinations: Addressing issues in pre-training data and the challenges of fine-tuning with new knowledge. - Detection Methods: Utilizing techniques like retrieval-augmented evaluation and sampling-based detection to identify hallucinations. - Anti-Hallucination Techniques: Implementing strategies such as Retrieval-Augmented Generation (RAG), fine-tuning for factuality, and employing retrieval methods to enhance accuracy. https://lnkd.in/g3j5kU5b For more updates, follow Rohit Agarwal! #AI #LLMs #MachineLearning #TechInnovation #DataScience #ModelAccuracy #FactualAI #HallucinationDetection
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Marktechpost Media Inc.
Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models Quick read: https://lnkd.in/gWKCv38m Researchers from Northeastern University, the University of California, Arizona State University, and New York University present this survey thoroughly examining diverse PEFT algorithms and evaluating their performance and computational requirements. It also provides an overview of applications developed using various PEFT methods and discusses common strategies employed to reduce computational expenses associated with PEFT. Beyond algorithmic considerations, the survey delves into real-world system designs to explore the implementation costs of different PEFT algorithms. As an invaluable resource, this survey equips researchers with insights into PEFT algorithms and their system implementations, offering detailed analyses of recent progressions and practical uses. The researchers categorized PEFT algorithms into additive, selective, reparameterized, and hybrid fine-tuning based on their operations. Major additive fine-tuning algorithms include adapters, soft prompts, and others, which differ in the additional tunable modules or parameters they utilize. Selective fine-tuning, in contrast, involves selecting a small subset of parameters from the backbone model, making only these parameters tunable while leaving the majority untouched during downstream task fine-tuning. Selective fine-tuning is categorized based on the grouping of chosen parameters: Unstructural Masking and Structural Masking. Reparametrization involves transforming model parameters between two equivalent forms, introducing additional low-rank trainable parameters during training, which are then integrated with the original model for inference. This approach encompasses two main strategies: Low-rank Decomposition and LoRA Derivatives. Hybrid fine-tuning explores different PEFT methods’ design spaces and combines their advantages. Paper: https://lnkd.in/gh8qfi4k
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AI Planet
BeyondLLM now supports Anthropic's new Claude 3.5 Sonnet model 🚀 🔹Claude 3.5 Sonnet sets new industry benchmarks for graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval). 🔹The model improvements are most noticeable in tasks requiring visual reasoning, like interpreting charts, graphs, or transcribing text from imperfect images. 🔗 GitHub: https://lnkd.in/g5yZwN2y 🔗 Cookbooks: https://lnkd.in/gNAkue35 Don't forget to ⭐️ the repo! #Anthropic #Sonnet #Claude3 #ClaudeSonnet #AI #GenerativeAI #LLM #LargeLanguageModels #BeyondLLM #RAG
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Analytics Vidhya
"Learn Your Reference Model for Real Good Alignment," introduces Trust Region Direct Preference Optimization (TR-DPO). Developed by a visionary team led by Alexey Gorbatovski at Tinkoff, this paper pioneers a method that significantly advances the training of language models by updating the reference model during the training process itself. Why This Matters: ➡️ Enhanced Model Alignment: TR-DPO introduces dynamic updates to the reference model, which allow for more accurate and adaptive training compared to the traditional Direct Preference Optimization (DPO). ➡️ Improved Performance Metrics: Through rigorous testing, TR-DPO has demonstrated a 19% improvement in performance over DPO, making it a groundbreaking tool for achieving higher coherence, correctness, and detail in model outputs. Key Insights: ➡️ Dynamic Reference Model Updates: Unlike static methods, TR-DPO continuously adjusts the reference model during training, which keeps the model relevant and highly effective across changing data sets and conditions. ➡️ Human-Centric Results: The model notably excels in producing outputs that are coherent, detailed, helpful, and harmless—key qualities for any application from chatbots to complex decision-making systems. #analyticsvidhya #datascience #generativeai #researchpaper
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THE DECODER - EVERYTHING AI
1/ Researchers at Carnegie Mellon and Tel Aviv University have discovered that the results of large language models (LLMs) improve the more examples you give them directly in the input (prompt) as context. This method, called "In-Context Learning" (ICL), could be an alternative to time-consuming fine-tuning. 2/ In ICL with a large number of examples in the prompt, the performance of the language models increases further, especially for tasks with many possible answers. Retrieval methods for selecting relevant examples further improve the results. Finetuning requires more data than ICL, but can provide even better results in some cases. 3/ The researchers believe that ICL with long contexts will be a powerful tool for many tasks as language models get better at handling extremely long texts. Ultimately, it is also a question of cost whether ICL or fine-tuning is used. The study confirms earlier results from Google Deepmind on many-shot prompts.
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eInfochips (An Arrow Company)
🔗 https://lnkd.in/g7CC33ur 🔍 This blog delves into Retrieval-Augmented Generation (RAG), a cutting-edge #AI model that improves huge language models with real-time data retrieval for greater accuracy and relevance. It describes RAG's design, practical applications, and implementation with Azure's AI services, such as Azure AI Enrichments, Prompt Flow, Open AI, and custom solutions. 📝 Sakib Ali Choudhary | Chirag Prajapati #AI #RAG #AzureAI #Innovation #TechTrends #Azure #OpenAI #TechBlog
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PowerArena
New PowerTalk Episode Alert! Curious about the latest advancements in AI and LLMs (Large Language Models)? We have Mike, our very own Data Scientist, on the show to break down the performance of Anthropic Claude 3.5 and offers a user-friendly guide to understanding LLMs. From AI biases and how to manage them to personal growth in the rapidly evolving AI landscape, Mike’s insights are invaluable for anyone navigating today’s AI-driven world. Don’t miss out! Catch the full episode here: https://lnkd.in/gSj4DBKs PowerTalk 新集數上線! 對 AI 和大型語言模型 (LLM) 的最新趨勢感到好奇嗎?在我們最新一集的 PowerTalk 中,PowerArena 的資料科學家 Mike 提供了一個淺顯易懂的 LLMs 理解指南,讓所有語言模型使用者簡單理解模型表現、 AI 偏見和個人在 AI 時代中的應對策略。 立即觀看完整集數: https://lnkd.in/gSj4DBKs #AI #LLM #AIbias #Claude3.5 #TechInsights #PowerArena #AIInnovation #AITrends #PowerTalk
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