Maxime Vermeir’s Post

Retrieval-augmented fine-tuning offers a powerful approach to building effective Large Language Models. But with options like Document-level Semantic Matching and Retrieval-Augmented Generation available, how do you choose the right technique to maximize model effectiveness? 🤔 👉 Join the conversation: https://hubs.la/Q02wg4sL0 Our upcoming live talk, featuring Tianjun Zhang, provides clarity on confidently selecting between DSF and RAG. Gain a deep understanding of the strengths and weaknesses of each approach, and discover how to tailor your RAFT implementation for optimal performance in your specific use case. #GenerativeAI #AIApplications #DomainSpecificFinetuning #RAGmethodology #ArtificialIntelligence #llama2 #ai #llm

Hello everyone. In this presentation, I explore the concepts of Retrieval-Augmented Generation (RAG), an advanced artificial intelligence (AI) framework that enhances the capabilities of large language models (LLMs) by connecting them to external knowledge bases. I also present a case study on how Retrieval-Augmented Generation can be used to improve services provided by Law Techs. I hope you enjoy it. Cheers, #RAG #artificialintelligence #AI #product #ML #LLM

Discover diverse methods to assess bias in Large Language Models (LLMs) – from human evaluation to specialized techniques like Logic-Aware Language Models and LLMOps. Explore real-world examples and metrics for fair and ethical Generative AI. Read more in our latest article: https://lnkd.in/gHfxvu3p #generativeai #largelanguagemodels

Ever wondered how Large Language Models are able to understand us? Wait no more, dive into the fascinating world of Language Model Visualisation! Explore how tokens are predicted, text is generated, and words are brought to life through a comprehensive breakdown of components such as token embedding, position embedding, layer normalisation, self-attention, and more. Try here: https://bbycroft.net/llm #AI #GPTModels #LanguageModel #machinelearning #llms

Excited to share a new blog post on detecting AI-generated text! As the use of large language models (LLMs) grows, so does the potential for disinformation. Check out this post on Contrastive Domain Adaptation (ConDA), a framework that addresses the challenge of detecting AI-generated news text. By leveraging unlabeled data and blending domain adaptation techniques with contrastive learning, ConDA achieves impressive results. Don't miss out on this innovative approach! Read more: https://bit.ly/462U0Mk #AI #textdetection #domainadaptation

Discover how to set up #modelfinetunning for large language models (LLMs) and how it can enhance model capabilities and deliver more predictable results. In this hands-on workshop, Artem Kobrin shows real-world comparisons between the baseline #GPT-3.5 Turbo from OpenAI and a fine-tuned model. He also explores the potential of combining fine-tuning with retrieval augmented generation to achieve even better results. https://lnkd.in/ebyuHB9t #GenerativeAI #GenAI #AI #FineTuning #OpenAI #GenAIforEnterprises #KnowledgeDataBases #NeuronsLab

Just as we were chatting about the multi-modal capabilities of large language models (LLMs) on a call, Anthropic goes and drops a bombshell! Claude 3.5 Sonnet. I'm particularly curious about its improved vision capabilities, like Document Visual Q&A feature – scoring 95.2% vs GPT4o's 92.8% on 0-shot prompting! Pretty impressive, right? 🚀 Can't wait to dive in and compare it with others LLMs. Might provide hands-on insights soon! #GenAI #Innovation #TechTalk #AI #Consulting #Claude

Large Language Models (LLMs) are dramatically changing the way users interact with systems, and this transformation is happening faster than ever. For me, as a technologist, it's fascinating to see how we can leverage LLMs as a key ingredient in our strategy to enhance user experiences by providing more relevant information and improving service quality. One promising strategy (Retrieval Augmented Generation) involves adopting a layered approach that connects existing data with the capabilities offered by Large Language Models. This entails extracting and summarizing information from user-provided input to guide queries and transform system outputs into easily comprehensible responses. For a deeper dive into the world of LLMs and AI experimentation, my colleague Justin van Grootveld explores this topic in this first article in a series, titled 'Large Language Models are an excellent way to taste AI'. https://lnkd.in/eN6MmzwB #iOagency #artificialintelligence #largelanguagemodels #futureofai

CTO Spotlight: Recordure's Kit Ruparel examines the battleground between the makers of Large Language Models (LLMs) and the capabilities of GPTs to accept and generate human language. So take a beat to find out more with our expert insight into the utility of the current generation of language AI. Read on: https://bit.ly/3WItt5B #artificialintelligence #AI #retailfinance #financialservices #technology #algorithms #algorithmicbias #tacklingdiscrimination #datatraining #datascience #compliance #data #AItraining #RegTech #GPT #ML #machinelearning

🧠 Memory in LLMs: Enhancing Conversational AI 🤖 Memory plays a pivotal role in modern Large Language Models (LLMs). It's revolutionizing LLM frameworks by expanding their contextual understanding. These models now retrieve concepts from past interactions, making conversations more nuanced. In essence, LLM memory is like a treasure trove of context-target pairs, fueling better predictions for the next token. 💡 The exciting part? There are numerous avenues to incorporate memory into LLMs, each with unique effects. Stay tuned as we delve deeper into the fascinating world of AI memory! 🚀 #AI #LLM #ConversationalAI #machinelearning