Artificial Corner

JULY 30, 2023

In this world of complex terminologies, someone who wants to explain Large Language Models (LLMs) to some non-tech guy is a difficult task. So that’s why I tried in this article to explain LLM in simple or to say general language. No need to train the LLM but one only has to think about Prompt design.

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John Snow Labs

DECEMBER 23, 2023

In the era of rapidly evolving Large Language Models (LLMs) and chatbot systems , we highlight the advantages of using LLM systems based on RAG (Retrieval Augmented Generation). RAG LLMs have the advantage of reducing hallucinations, by explaining the source of each fact, and enabling the use of private documents to answer questions.

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John Snow Labs

DECEMBER 23, 2023

In the era of rapidly evolving Large Language Models (LLMs) and chatbot systems, we highlight the advantages of using LLM systems based on RAG (Retrieval Augmented Generation). RAG LLMs have the advantage of reducing hallucinations, by explaining the source of each fact, and enabling the use of private documents to answer questions.

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LLM Chatbots Large Language Models NLP 52

AWS Machine Learning Blog

OCTOBER 17, 2023

One challenge that agents face is finding the precise information when answering customers’ questions, because the diversity, volume, and complexity of healthcare’s processes (such as explaining prior authorizations) can be daunting. Then we explain how the solution uses the Retrieval Augmented Generation (RAG) pattern for its implementation.

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John Snow Labs

OCTOBER 16, 2023

The recent NLP Summit served as a vibrant platform for experts to delve into the many opportunities and also challenges presented by large language models (LLMs). billion by 2028, LLMs play a pivotal role in this growth trajectory. At the recent NLP Summit, experts from academia and industry shared their insights.

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Bugra Akyildiz

MARCH 30, 2024

Before going further, taking a step back to explain the key concepts in Mamba: State: Mamba maintains a state that captures the essential information about the past that is relevant for predicting the next element in the sequence. The logic of the prompt specifies what the LLM should do, while the wording is how the prompt is phrased.

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AI Weekly

APRIL 11, 2024

artificialintelligence-news.com Meta confirms that its Llama 3 open source LLM is coming in the next month On Tuesday, Meta confirmed that it plans an initial release of Llama 3 — the next generation of its large language model used to power generative AI assistants — within the next month. No legacy process is safe.

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Heartbeat

FEBRUARY 13, 2024

Learning Path to Building LLM-Based Solutions — For Practitioner Data Scientists As everyone would agree, the advent of LLM has transformed the technology industry, and technocrats have had a huge surge of interest in learning about LLMs. Langchain is very comprehensive, and its applications are evolving rapidly.

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John Snow Labs

AUGUST 31, 2023

With the release of NLP Lab 5.3, we’re taking a leap forward by integrating one of the most advanced Large Language Models (LLMs) available today: Open AI’s GPT models. Join me on this journey to explore the transformative potential of ChatGPT prompts for entity recognition in NLP Lab 5.3.

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Marktechpost

MAY 30, 2024

Natural Language Processing on Google Cloud This course introduces Google Cloud products and solutions for solving NLP problems. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow. Learners will gain hands-on experience with image classification models using public datasets.

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Marktechpost

JULY 20, 2023

Natural language processing (NLP) has seen a paradigm shift in recent years, with the advent of Large Language Models (LLMs) that outperform formerly relatively tiny Language Models (LMs) like GPT-2 and T5 Raffel et al. on a variety of NLP tasks. Figure 1 depicts a sample of the summarising job.

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Unite.AI

MAY 16, 2024

However, none can help explain the specific meaning behind each of your nighttime visions. While you can technically use a large language model (LLM) to decipher them, its output would only be partially accurate at best. Realistically, it’s probably a combination of multiple ideas. On the one hand, it’s fast and straightforward.

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NYU Center for Data Science

MARCH 21, 2024

What this shows is that it’s true that the word is unpredictable, but unpredictability doesn’t explain why it’s so difficult to process,” Linzen remarked in an interview. But can the predictive power of language models truly capture the nuances of human language processing?

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Chatbots Life

SEPTEMBER 11, 2023

It provides a detailed overview of each library’s unique contributions and explains how they can be combined to create a functional system that can detect and correct linguistic errors in text data. This limitation has paved the way for more advanced solutions that harness the power of Natural Language Processing (NLP).

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ODSC - Open Data Science

SEPTEMBER 6, 2023

Most people who have experience working with large language models such as Google’s Bard or OpenAI’s ChatGPT have worked with an LLM that is general, and not industry-specific. CaseHOLD is a new dataset for legal NLP tasks. The CaseHOLD dataset was created to address the lack of large-scale, domain-specific datasets for legal NLP.

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Large Language Models LLM NLP BERT 98

Mlearning.ai

JUNE 14, 2023

Learning Large Language Models The LLM (Foundational Models) space has seen tremendous and rapid growth. I used this foolproof method of consuming the right information and ended up publishing books , artworks , Podcasts and even an LLM powered consumer facing app ranked #40 on the app store. With so much information out there.

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Marktechpost

SEPTEMBER 1, 2023

LLMs have become increasingly popular in the NLP (natural language processing) community in recent years. The paper explains why any technique for addressing undesirable LLM behaviors that do not completely eradicate them renders the model vulnerable to adversarial quick attacks. Check out the Paper.

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AWS Machine Learning Blog

FEBRUARY 26, 2024

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis. Monitoring the performance and behavior of LLMs is a critical task for ensuring their safety and effectiveness.

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ODSC - Open Data Science

JANUARY 31, 2024

But, now that you have the makings of an LLM or even a model, how will your team turn your model into an AI beast that can enhance your company’s productivity? There are now many groups who are ready to fill in the gap and help companies unlock the potential of their LLMs. Let’s explore some of the top contenders.

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Ehud Reiter

DECEMBER 28, 2023

Human+LLMs make fewer mistakes than humans on their own Coming back to NLP, some of my students have seen that doctors who work with LLMs can produce more accurate reports. LLMs are poor at some types of reasoning and can be confused by statistically unlikely situations. So But that doesnt mean that it is useless.

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AWS Machine Learning Blog

AUGUST 7, 2023

It will explain the thought process and experimentation behind the solution, including the model training and development process. Part 2 will delve into the productionized solution, explaining the design decisions, data flow, and illustration of the model training and deployment architecture.

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ODSC - Open Data Science

FEBRUARY 6, 2024

Check out some of the LLM-focused training sessions, workshops, and talks you’ll find at the conference. NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning Dr. Jon Krohn | Chief Data Scientist | Nebula.io

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Marktechpost

MAY 29, 2024

Generative AI Explained This course provides an overview of Generative AI, its concepts, applications, challenges, and opportunities. Participants will learn to design dialog management systems, utilize embeddings for content retrieval, and implement advanced LLM pipelines using tools like LangChain and Gradio.

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Neural Network Large Language Models Natural Language Processing Prompt Engineer 84

Unite.AI

AUGUST 1, 2023

Let's create an advanced prompt where ChatGPT is tasked with summarizing key takeaways from AI and NLP research papers. Using the few-shot learning approach, let's teach ChatGPT to summarize key findings from AI and NLP research papers: 1.

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Towards AI

SEPTEMBER 6, 2023

Learn to build your own end-to-end production-ready LLM workflows Photo by Brett Jordan on Unsplash In a world increasingly shaped by artificial intelligence, Large Language Models (LLMs) have emerged as the crown jewels of the machine learning realm. This invaluable skill enables you to tailor your models to specific applications.

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IBM Journey to AI blog

SEPTEMBER 28, 2023

included the Slate family of encoder-only models useful for enterprise NLP tasks. However, choosing the “right” LLM from a collection of thousands of open-source models is not an easy endeavor and requires a careful examination of the tradeoffs between cost and performance. ” The initial release of watsonx.ai

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Unite.AI

NOVEMBER 3, 2023

This article delves into the details of these research papers, elucidating the concepts, methodologies, and implications of the proposed techniques, making it accessible even to readers with limited knowledge in AI and NLP. One critical consideration is the order in which knowledge and exemplars are generated.

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Towards AI

APRIL 2, 2024

As we have discussed, there have been some signs of open-source AI (and AI startups) struggling to compete with the largest LLMs at closed-source AI companies. This is driven by the need to eventually monetize to fund the increasingly huge LLM training costs. So far, a lot of focus has been on fine-tuning with open-source LLM projects.

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Allen AI

JUNE 19, 2023

However, LLMs also carry risks that have already led to real harm, and while it shouldn’t be the responsibility of the user to figure out these risks on their own, current tools often don’t explain these risks or provide safeguards. But the key takeaway is that LLMs are trained to produce text that looks good to humans.

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ODSC - Open Data Science

SEPTEMBER 19, 2023

Engineers provide insights into the technical feasibility and challenges of proposed features, scientists contribute their understanding of NLP techniques, and product managers bring the user perspective, helping to shape the direction of LLM development. If anything, it’s more important.

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Ehud Reiter

MARCH 26, 2023

One of the things I most dislike about academic NLP research is the focus on leaderboards. A leaderboard is essentially a scorecard of how well different systems do on an NLP task, ie how well they perform on a fixed dataset as measured by specified evaluation metrics. I explained my dislike of leaderboards ia a previous blog.

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John Snow Labs

JANUARY 29, 2024

Several such case studies were presented by the US Veteran’s Administration , ClosedLoop , and WiseCube at John Snow Labs’ annual Natural Language Processing (NLP) Summit , now the world’s largest gathering of applied NLP and LLM practitioners.

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IBM Journey to AI blog

OCTOBER 16, 2023

The rise of the foundation model ecosystem (which is the result of decades of research in machine learning), natural language processing (NLP) and other fields, has generated a great deal of interest in computer science and AI circles. The development and use of these models explain the enormous amount of recent AI breakthroughs.

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ODSC - Open Data Science

SEPTEMBER 20, 2023

For example, if you’re discussing a medical topic, you might begin with, “Considering recent advances in medical research, explain the potential benefits of gene therapy for inherited diseases.” Now if you want to take your prompting to the next level, then you don’t want to miss ODSC West’s LLM Track.

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Unite.AI

MAY 31, 2023

Since LLM neurons offer rich connections that can express more information, they are smaller in size compared to regular NNs. Hence, it becomes easier for researchers to explain how an LNN reached a decision. Consider sentiment analysis, an NLP task that aims to understand the underlying emotion behind text.

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TheSequence

DECEMBER 13, 2023

Quick bio Lewis Tunstall is a Machine Learning Engineer in the research team at Hugging Face and is the co-author of the bestseller “NLP with Transformers” book. Thom Wolf is also a co-author, so where better to learn transformers than from the person who created the Hugging Face Transformers library?

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Artificial Corner

AUGUST 29, 2023

In this article, we will explore beam.cloud , an easy and powerful swiss-army-knife for running LLMs and code directly on the cloud. We’ll discuss how it streamlines workflows for NLP tasks, enables real-time conversations with giant conversational models, and much more. I will explain the process in a bit.

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