O'Reilly Media

JUNE 14, 2024

And Miso had already built an early LLM-based search engine using the open-source BERT model that delved into research papers—it could take a query in natural language and find a snippet of text in a document that answered that question with surprising reliability and smoothness.

BERT 117

BERT LLM Metadata Generative AI 117

Unite.AI

JANUARY 23, 2024

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries. LLMs utilize embeddings to understand word context.

Large Language Models 100

Large Language Models Prompt Engineer Prompt Engineering LLM 100

Marek Rei

NOVEMBER 4, 2024

link] The paper investigates LLM robustness to prompt perturbations, measuring how much task performance drops for different models with different attacks. link] The paper proposes query rewriting as the solution to the problem of LLMs being overly affected by irrelevant information in the prompts. ArXiv 2023. Oliveira, Lei Li.

Machine Learning 80

Machine Learning NLP Large Language Models LLM 80

AWS Machine Learning Blog

JUNE 25, 2024

Whether you’re a developer seeking to incorporate LLMs into your existing systems or a business owner looking to take advantage of the power of NLP, this post can serve as a quick jumpstart. The raw data is processed by an LLM using a preconfigured user prompt. The LLM generates output based on the user prompt.

Automation 117

Automation Prompt Engineer Prompt Engineering Categorization 117

Bugra Akyildiz

MARCH 1, 2025

device type, location), while the Pin tower encodes visual features(CNN-extracted embeddings), textual metadata(BERT embeddings), and statistical features(e.g., Fundamentally, the AI co-scientist system represents an improved implementation of multi-agent LLM network built upon the Gemini 2.0 historical engagement rates).

LLM 52

LLM Automation Neural Network BERT 52

AWS Machine Learning Blog

APRIL 3, 2024

The following is a high-level overview of how it works conceptually: Separate encoders – These models have separate encoders for each modality—a text encoder for text (for example, BERT or RoBERTa), image encoder for images (for example, CNN for images), and audio encoders for audio (for example, models like Wav2Vec).

Machine Learning 118

Machine Learning Metadata Generative AI ML 118

AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker Clarify now provides AWS customers with foundation model (FM) evaluations, a set of capabilities designed to evaluate and compare model quality and responsibility metrics for any LLM, in minutes. FMEval helps in measuring evaluation dimensions such as accuracy, robustness, bias, toxicity, and factual knowledge for any LLM.

Large Language Models 104

Large Language Models Algorithm LLM Responsible AI 104

AWS Machine Learning Blog

MARCH 6, 2023

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. LLMs’ generative abilities make them popular for text synthesis, summarization, machine translation, and more.

Large Language Models 92

Large Language Models LLM Machine Learning ML 92

Bugra Akyildiz

JULY 6, 2024

RemoteLogMetadataManager: An interface for managing the lifecycle of metadata about remote log segments with strongly consistent semantics. The RemoteLogManager determines the targeted remote segment based on the desired offset and leader epoch by querying the metadata store using the RemoteLogMetadataManager.

Computer Vision 52

Computer Vision Metadata Large Language Models BERT 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In November 2022, ChatGPT was released, a large language model (LLM) that used the transformer architecture, and is widely credited with starting the current generative AI boom. The following table shows the metadata of three of the largest accelerated compute instances. 32xlarge 0 16 0 128 512 512 4 x 1.9

ML 92

ML Deep Learning Algorithm Large Language Models 92

Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Really quickly, LLMs can do many things. Hope you can all hear me well.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Really quickly, LLMs can do many things. Hope you can all hear me well.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Unite.AI

AUGUST 4, 2023

This, coupled with the challenges of understanding AI concepts and complex algorithms, contributes to the learning curve associated with developing applications using LLMs. Nevertheless, the integration of LLMs with other tools to form LLM-powered applications could redefine our digital landscape. Two key LLM models are GPT-3.5

Prompt Engineering 245

Prompt Engineering Prompt Engineer Python NLP 245

Bugra Akyildiz

AUGUST 10, 2024

Large models like GPT-3 (175B parameters) or BERT-Large (340M parameters) can be reduced by 75% or more. Running BERT models on smartphones for on-device natural language processing requires much less energy due to resource constrained in smartphones than server deployments. The experience is designed to be similar to grep.

BERT 59

BERT Large Language Models Categorization Deep Learning 59

AWS Machine Learning Blog

AUGUST 8, 2023

For example, input images for an object detection use case might need to be resized or cropped before being served to a computer vision model, or tokenization of text inputs before being used in an LLM. Then we use a pre-trained BERT (uncased) model from the Hugging Face Model Hub to extract token embeddings. nvidia/pytorch:22.10-py3

BERT 88

BERT Deep Learning Auto-classification Python 88