Marktechpost

NOVEMBER 19, 2024

Current LLM-based methods for anomaly detection include prompt engineering, which uses LLMs in zero/few-shot setups, and fine-tuning, which adapts models to specific datasets. It leverages BERT to extract semantic vectors and uses Llama, a transformer decoder, for log sequence classification.

Large Language Models 79

Large Language Models BERT Prompt Engineering Prompt Engineer 79

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

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.

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ChatGPT Large Language Models Prompt Engineering Prompt Engineer 115

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 Engineering Prompt Engineer LLM 100

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 215

Generative AI Chatbots Large Language Models OpenAI 215

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 103

Large Language Models Auto-complete Prompt Engineering Prompt Engineer 103

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.

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Prompt Engineering Prompt Engineer BERT Artificial Intelligence 117

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.

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Generative AI Deep Learning Algorithm AI 291

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

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 111

ChatGPT Generative AI BERT NLP 111

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 118

Automation Prompt Engineering Prompt Engineer Categorization 118

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 98

Large Language Models Natural Language Processing Deep Learning Prompt Engineering 98

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 87

Large Language Models LLM Natural Language Processing Deep Learning 87

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.

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Auto-complete ChatGPT Large Language Models Neural Network 240

AWS Machine Learning Blog

DECEMBER 11, 2024

Generating improved instructions for each question-and-answer pair using an automatic prompt engineering technique based on the Auto-Instruct Repository. The value of Amazon Bedrock in text generation for automatic prompt engineering and text summarization for evaluation helped tremendously in the collaboration with Tealium.

Generative AI 68

Generative AI Chatbots Prompt Engineering Prompt Engineer 68

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 81

Generative AI LLM Prompt Engineering Prompt Engineer 81

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 Engineering Prompt Engineer 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 Engineering 105

Prompt Engineering Prompt Engineer NLP Computer Vision 105

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 Engineering Prompt Engineer Large Language Models 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 Engineering 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 Engineering 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 Engineering 52

deepsense.ai

NOVEMBER 9, 2024

Prompt engineering Let’s start simple. With this in mind, we strongly recommend starting with prompt engineering. Tell me what you don’t know When prompting LLMs to solve a chosen problem, we can add an instruction to return an “I don’t know” answer when the model is in doubt.

Prompt Engineering 59

Prompt Engineering Prompt Engineer LLM ChatGPT 59

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 76

Automation Python BERT Prompt Engineer 76

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.

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Prompt Engineer Prompt Engineering Deep Learning Machine Learning 90

Heartbeat

MARCH 19, 2024

Like machine learning operations, LLMOps involves efforts from several contributors, like prompt engineers, data scientists, DevOps engineers, business analysts, and IT operations. This is, in fact, a baseline, and the actual LLMOps workflow usually involves more stakeholders like prompt engineers, researchers, etc.

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Large Language Models Prompt Engineering Prompt Engineer Natural Language Processing 52

Jay Alammar

MARCH 6, 2022

The company trains massive language models (both GPT-like and BERT-like) and offers them as an API (which also supports finetuning). It establishes the difference between generative (GPT-like) and representation (BERT-like) models and examples use cases for them. A little less than a year ago, I joined the awesome Cohere team.

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BERT Large Language Models Explainability Prompt Engineering 40

Unite.AI

AUGUST 4, 2023

In this article, we will delve deeper into these issues, exploring the advanced techniques of prompt engineering with Langchain, offering clear explanations, practical examples, and step-by-step instructions on how to implement them. Prompts play a crucial role in steering the behavior of a model.

Prompt Engineer 245

Prompt Engineer Prompt Engineering Python NLP 245

Pickl AI

SEPTEMBER 19, 2024

How Prompt Tuning Fits into the Broader Context of AI and Machine Learning In the broader context of AI and Machine Learning , prompt tuning is part of a larger strategy known as “prompt engineering.” Prompt tuning is a more focused method compared to full model fine-tuning.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Machine Learning Artificial Intelligence 52

ODSC - Open Data Science

NOVEMBER 8, 2023

Prompt Engineering Another buzzword you’ve likely heard of lately, prompt engineering means designing inputs for LLMs once they’re developed. You can even fine-tune prompts to get exactly what you want. Don’t go in aimlessly expecting it to do everything. Plan accordingly!

Generative AI 52

Generative AI Prompt Engineering Prompt Engineer LLM 52

Heartbeat

AUGUST 2, 2023

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc., It has become the backbone of many successful language models, like GPT-3, BERT, and their variants. They are usually trained on a massive amount of text data. Benefits of Using Language Models 1.

Large Language Models 52

Large Language Models Prompt Engineering Prompt Engineer OpenAI 52

AssemblyAI

MAY 23, 2023

This trend started with models like the original GPT and ELMo, which had millions of parameters, and progressed to models like BERT and GPT-2, with hundreds of millions of parameters. Large Language Models In recent years, LLM development has seen a significant increase in size, as measured by the number of parameters. months on average.

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Large Language Models Neural Network LLM Chatbots 130

Snorkel AI

MARCH 23, 2023

” This paper focuses on how Foundation Models like BERT, GPT-3, and SimCLR can be pre-trained on a variety of data domains and still achieve state-of-the-art results when fine-tuned on a single domain. Learn More Follow Snorkel AI on LinkedIn , Twitter , and YouTube to be the first to see new posts and videos!

BERT 52

BERT Prompt Engineering Prompt Engineer Explainability 52

The MLOps Blog

FEBRUARY 15, 2024

Due to the rise of LLMs and the shift towards pre-trained models and prompt engineering, specialists in traditional NLP approaches are particularly at risk. TL;DR In 2023, the tech industry saw waves of layoffs, which will likely continue into 2024. Or should they start to look for new career opportunities?

NLP 59

NLP Natural Language Processing Data Scientist Software Engineer 59

Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

ODSC - Open Data Science

NOVEMBER 19, 2024

LLMs from Prototype to Production — LLMOps, Prompt Engineering, and Moving LLMs to the Cloud with Sinan Ozdemir Slides Sinan Ozdemir’s comprehensive session on LLMOps addressed the practicalities of deploying LLMs like GPT, Llama, and BERT.

Deep Learning 52

Deep Learning Data Science Neural Network BERT 52