Marktechpost

MAY 30, 2024

Google plays a crucial role in advancing AI by developing cutting-edge technologies and tools like TensorFlow, Vertex AI, and BERT. Transformer Models and BERT Model This course introduces the Transformer architecture and the BERT model, covering components like the self-attention mechanism.

Artificial Intelligence 131

Artificial Intelligence Artificial Intelligence BERT Computer Vision 131

Unite.AI

JANUARY 11, 2024

More recent methods based on pre-trained language models like BERT obtain much better context-aware embeddings. Existing methods predominantly use smaller BERT-style architectures as the backbone model. For model training, they opted for fine-tuning the open-source 7B parameter Mistral model instead of smaller BERT-style architectures.

Large Language Models 147

Large Language Models Prompt Engineer Prompt Engineering BERT 147

ODSC - Open Data Science

JANUARY 29, 2024

The role of prompt engineer has attracted massive interest ever since Business Insider released an article last spring titled “ AI ‘Prompt Engineer Jobs: $375k Salary, No Tech Backgrund Required.” It turns out that the role of a Prompt Engineer is not simply typing questions into a prompt window.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Data Science LLM 52

Unite.AI

AUGUST 8, 2023

These advanced AI deep learning models have seamlessly integrated into various applications, from Google's search engine enhancements with BERT to GitHub’s Copilot, which harnesses the capability of Large Language Models (LLMs) to convert simple code snippets into fully functional source codes.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

Flipboard

DECEMBER 6, 2023

In this post, we focus on the BERT extractive summarizer. BERT extractive summarizer The BERT extractive summarizer is a type of extractive summarization model that uses the BERT language model to extract the most important sentences from a text. It works by first embedding the sentences in the text using BERT.

BERT 167

BERT Large Language Models Artificial Intelligence Artificial Intelligence 167

AWS Machine Learning Blog

APRIL 29, 2025

However, when transitioning between different foundation models, the prompts created for your original model might not be as performant for Amazon Nova models without prompt engineering and optimization. To meet these needs, we designed a data-aware optimization architecture as shown in the following diagram.

Auto-classification 97

Auto-classification LLM Generative AI Auto-complete 97

Towards AI

APRIL 28, 2025

Techniques like prompt engineering, Chain-Of-Thought reasoning, and Few-Shot Learning can further improve performance. Theyre faster, more cost-effective than LLMs, consistent, and their specialization can give them an edge in performance for specific tasks (see example here).

LLM 70

LLM Machine Learning BERT Prompt Engineer 70

Unite.AI

DECEMBER 27, 2023

Facebook's RoBERTa, built on the BERT architecture, utilizes deep learning algorithms to generate text based on given prompts. Another important trend is prompt engineering, which focuses on creating high-quality prompts for generative AI models.

Generative AI 217

Generative AI Chatbots Large Language Models OpenAI 217

Marktechpost

APRIL 1, 2024

It provides codes for working with various models, such as GPT-4, BERT, T5, etc., The Only ChatGPT Prompts Book You’ll Ever Need This book teaches how to craft effective prompts for maximum impact using prompt engineering techniques. and explains how they work.

ChatGPT 117

ChatGPT Large Language Models Prompt Engineer Prompt Engineering 117

Marktechpost

NOVEMBER 26, 2023

While pre-training a model like BERT from scratch is possible, using an existing model like bert-large-cased · Hugging Face is often more practical, except for specialized cases. Perhaps the easiest point of entry for adapting models is prompt engineering.

LLM 139

LLM BERT Large Language Models Prompt Engineer 139

Marktechpost

DECEMBER 19, 2023

The study employs pre-trained CLIP models in experiments across Playhouse and AndroidEnv, exploring encoder architectures such as Normalizer-Free Networks, Swin, and BERT for language encoding in tasks like Find, Lift, and Pick and Place. The study examines the role of prompt engineering in VLM reward performance.

Prompt Engineer 117

Prompt Engineer Prompt Engineering BERT Artificial Intelligence 117

Unite.AI

JANUARY 23, 2024

Prompt engineering is crucial to steering LLMs effectively. Techniques like Word2Vec and BERT create embedding models which can be reused. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context. LLMs utilize embeddings to understand word context.

Large Language Models 100

Large Language Models Prompt Engineer Prompt Engineering LLM 100

IBM Journey to AI blog

FEBRUARY 13, 2024

The quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial. The result will be unusable if a user prompts the model to write a factual news article.

Generative AI 343

Generative AI AI AI Chatbots 343

Unite.AI

SEPTEMBER 25, 2023

An illustration of the pretraining process of MusicLM: SoundStream, w2v-BERT, and Mulan | Image source: here Moreover, MusicLM expands its capabilities by allowing melody conditioning.

Generative AI 296

Generative AI Deep Learning Algorithm AI 296

Marktechpost

APRIL 16, 2024

The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc. It explains the fundamentals of LLMs and generative AI and also covers prompt engineering to improve performance. LangChain Crash Course This is a short book covering the fundamentals of LangChain.

Large Language Models 104

Large Language Models Auto-complete Prompt Engineer Prompt Engineering 104

Towards AI

DECEMBER 28, 2023

Major language models like GPT-3 and BERT often come with Python APIs, making it easy to integrate them into various applications. Prompt engineering refers to the practice of designing and crafting effective prompts/questions to elicit desired responses from language models or natural language processing systems.

Large Language Models 122

Large Language Models Natural Language Processing Deep Learning Prompt Engineer 122

Ehud Reiter

AUGUST 25, 2024

” BERT/BART/etc can be used in data-to-text, but may not be best approach Around 2020 LSTMs got replaced by fine-tuned transformer language models such as BERT and BART. This is a much better way to build data-to-text and other NLG systems, and I know of several production-quality NLG systems built using BART (etc).

BERT 135

BERT NLP Natural Language Processing Prompt Engineer 135

Snorkel AI

MARCH 26, 2025

Data scientists and SMEs use this ground truth to guide iterations on the LLM-as-judge prompt template. The team may embed some of the SMEs labels and explanations directly in the template as a form of prompt engineering known as few shot learning. Update and fine-tune prompt templates for fairer assessments.

LLM 52

LLM Data Scientist Prompt Engineer Prompt Engineering 52

AWS Machine Learning Blog

JUNE 25, 2024

Advantages of adopting generative approaches for NLP tasks For customer feedback analysis, you might wonder if traditional NLP classifiers such as BERT or fastText would suffice. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.

Automation 132

Automation Categorization Prompt Engineer Prompt Engineering 132

Artificial Corner

JULY 30, 2023

Prompts design is a process of creating prompts which are the instructions and context that are given to Large Language Models to achieve the desired task. Prompt engineering is a technique used in artificial intelligence to optimize and refine language models for specific activities and intended outcomes.

Large Language Models 98

Large Language Models LLM Natural Language Processing Deep Learning 98

Marktechpost

JUNE 24, 2024

The study also identified four essential skills for effectively interacting with and leveraging ChatGPT: prompt engineering, critical evaluation of AI outputs, collaborative interaction with AI, and continuous learning about AI capabilities and limitations.

ChatGPT 116

ChatGPT Generative AI BERT NLP 116

Marktechpost

SEPTEMBER 15, 2024

The pre-train and fine-tune paradigm, exemplified by models like ELMo and BERT, has evolved into prompt-based reasoning used by the GPT family. Techniques like Uprise and DaSLaM use lightweight retrievers or small models to optimize prompts, break down complex problems, or generate pseudo labels.

BERT 120

BERT LLM Large Language Models Categorization 120

ODSC - Open Data Science

MARCH 10, 2025

Starting with BERT and accelerating with the launch of GPT-3 , conference sessions on LLMs and transformers skyrocketed. The release of GPT-4 and other advanced LLMs sparked a surge in research on fine-tuning, prompt engineering, and the use of LLMs in real-world applications.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

Unite.AI

AUGUST 28, 2023

Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. Deep learning techniques further enhanced this, enabling sophisticated image and speech recognition. Transformers and Advanced NLP Models : The introduction of transformer architectures revolutionized the NLP landscape.

Auto-complete 240

Auto-complete ChatGPT Large Language Models Neural Network 240

Towards AI

JANUARY 30, 2024

GPT4, Stable Diffusion, Llama, BERT, Gemini Large Language Models (LLMs) Foundation models, trained on the “Transformer Architecture”, that can perform a wide array of Natural Language Processing (NLP) tasks like text generation, classification, summarisation etc. Examples: GPT 3.5,

Generative AI 102

Generative AI LLM Prompt Engineer Prompt Engineering 102

AWS Machine Learning Blog

JUNE 27, 2024

After testing available open source models, we felt that the out-of-the-box capabilities and responses were insufficient with prompt engineering alone to meet our needs. Specifically, in our testing with open source models, we wanted to make sure each model was optimized for a ReAct/chain-of-thought style of prompting.

Software Engineer 133

Software Engineer Large Language Models AI AI 133

Marktechpost

MAY 2, 2025

Such models approach the language-understanding performance of large pre-trained transformers like BERT, but are tailored to the specific intents/entities of the chatbot. For example, Rasa introduced the DIET (Dual Intent and Entity Transformer) architecture, a lightweight transformer network for intent classification and entity extraction.

Conversational AI 75

Conversational AI Chatbots Large Language Models LLM 75

Mlearning.ai

JULY 24, 2023

The likes of BERT, GPT4, Llama 2, have undergone pre-training on a large corpus of data. The result of pre-training are Foundation Models 2️⃣ Prompt Engineering : Once the foundation model is ready, text can be generated by providing the model with a prompt. The model generates a completion on the prompt.

Generative AI 52

Generative AI Prompt Engineer Prompt Engineering Large Language Models 52

AWS Machine Learning Blog

MAY 31, 2023

In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. For text tasks such as sentence classification, text classification, and question answering, you can use models such as BERT, RoBERTa, and DistilBERT.

Automation 102

Automation Python Machine Learning BERT 102

Mlearning.ai

JANUARY 17, 2024

350x: Application Areas , Companies, Startups 3,000+: Prompts , Prompt Engineering, & Prompt Lists 250+: Hardware, Frameworks , Approaches, Tools, & Data 300+: Achievements, Impacts on Society , AI Regulation, & Outlook 20x: What is Generative AI?

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

deepsense.ai

FEBRUARY 6, 2024

Prompt engineering : the provided prompt plays a crucial role, especially when dealing with compound nouns. By using “car lamp” as a prompt, we are very likely to detect cars instead of car lamps. The first concept is prompt engineering. Text: The model accepts text prompts. Source: [link].

Prompt Engineer 105

Prompt Engineer Prompt Engineering NLP Computer Vision 105

Bugra Akyildiz

APRIL 20, 2025

Text descriptions encode via Sentence-BERT with contrastive learning to separate dissimilar items. How : Prompt Engineering : Converts user histories into natural language sequences with explicit instructions like "Recommend items similar to [target]." The fallback mechanism maintains robustness.

LLM 59

LLM Metadata Algorithm BERT 59

AWS Machine Learning Blog

APRIL 17, 2023

Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models. For more information, refer to EMNLP: Prompt engineering is the new feature engineering.

Prompt Engineer 107

Prompt Engineer Prompt Engineering Deep Learning Machine Learning 107

How to Learn Machine Learning

MAY 1, 2025

The most famous proprietary LLMs have been developed by top technology companies such as OpenAI (GPT-3, GPT-4), Google ( BERT , PaLM), and Anthropic (Claude). They are not available for public modification or redistribution, often have accessibility to models behind paywalls or subscription services. What Are Proprietary LLMs?

Data Science 52

Data Science LLM NLP OpenAI 52

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. When prompted for a classification task, a genAI LLM may give a reasonable baseline, but prompt engineering and fine-tuning can only take you so far. BERT for misinformation. In-context learning. A GPT-3 model—82.5%

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. When prompted for a classification task, a genAI LLM may give a reasonable baseline, but prompt engineering and fine-tuning can only take you so far. BERT for misinformation. In-context learning. A GPT-3 model—82.5%

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. When prompted for a classification task, a genAI LLM may give a reasonable baseline, but prompt engineering and fine-tuning can only take you so far. BERT for misinformation. In-context learning. A GPT-3 model—82.5%

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52