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

Unite.AI

AUGUST 28, 2023

Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. link] The process can be categorized into three agents: Execution Agent : The heart of the system, this agent leverages OpenAI’s API for task processing.

Auto-complete 240

Auto-complete ChatGPT Large Language Models Neural Network 240

The MLOps Blog

SEPTEMBER 26, 2024

Effective mitigation strategies involve enhancing data quality, alignment, information retrieval methods, and prompt engineering. Broadly speaking, we can reduce hallucinations in LLMs by filtering responses, prompt engineering, achieving better alignment, and improving the training data. In 2022, when GPT-3.5

LLM 72

LLM Auto-complete Prompt Engineer Prompt Engineering 72

Explosion

MAY 17, 2023

We want to aggregate it, link it, filter it, categorize it, generate it and correct it. For instance, you can design a number of different prompts, and run a tournament between them, by answering a series of A/B evaluation questions where you pick which of two outputs is better without knowing which prompt produced them.

LLM 135

LLM NLP Large Language Models OpenAI 135

deepsense.ai

NOVEMBER 14, 2023

In short, EDS is the problem of the widespread lack of a rational approach to and methodology for the objective, automated and quantitative evaluation of performance in terms of generative model finetuning and prompt engineering for specific downstream GenAI tasks related to practical business applications. There is a ‘but’, however.

Generative AI 111

Generative AI Prompt Engineer Prompt Engineering ML 111

Unite.AI

SEPTEMBER 20, 2023

To install and import the library, use the following commands: pip install -q transformers from transformers import pipeline Having done that, you can execute NLP tasks starting with sentiment analysis, which categorizes text into positive or negative sentiments. We choose a BERT model fine-tuned on the SQuAD dataset.

LLM 342

LLM BERT NLP Python 342

Flipboard

DECEMBER 6, 2023

Types of summarizations There are several techniques to summarize text, which are broadly categorized into two main approaches: extractive and abstractive summarization. In this post, we focus on the BERT extractive summarizer. It works by first embedding the sentences in the text using BERT.

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BERT Large Language Models Artificial Intelligence Artificial Intelligence 167

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. These sources can be categorized into three types: textual documents (e.g., KD methods can be categorized into white-box and black-box approaches.

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BERT LLM Large Language Models Categorization 122

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

AWS Machine Learning Blog

NOVEMBER 1, 2023

Key strengths of VLP include the effective utilization of pre-trained VLMs and LLMs, enabling zero-shot or few-shot predictions without necessitating task-specific modifications, and categorizing images from a broad spectrum through casual multi-round dialogues. This model achieves a 91.3%

Auto-classification 120

Auto-classification LLM Auto-complete Generative AI 120