Marktechpost

AUGUST 10, 2024

Despite this, LLMs’ use in requirement engineering has gradually increased, driven by advancements in contextual analysis and reasoning through prompt engineering and Chain-of-Thought techniques. The field of LLM-based agents lacks standardized benchmarks, impeding effective performance evaluation.

Software Engineer 111

Software Engineer LLM Large Language Models Categorization 111

Explosion

MAY 17, 2023

We want to aggregate it, link it, filter it, categorize it, generate it and correct it. I don’t want to undersell how impactful LLMs are for this sort of use-case. You can give an LLM a group of comments and ask it to summarize the texts or identify key themes. Ask for it in the prompt. Need the data in some format?

LLM 135

LLM NLP Large Language Models OpenAI 135

AWS Machine Learning Blog

NOVEMBER 1, 2024

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. By fine-tuning, the LLM can adapt its knowledge base to specific data and tasks, resulting in enhanced task-specific capabilities.

LLM 92

LLM Prompt Engineer Prompt Engineering Generative AI 92

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 training examples are needed in LLM Development but it’s needed in Traditional Development.

Large Language Models 98

Large Language Models LLM Natural Language Processing Deep Learning 98

Marktechpost

AUGUST 31, 2024

This approach aims to balance latency and interpretability by combining a small classification model, specifically a small language model (SLM), with a downstream LLM module called a “constrained reasoner.” ” The SLM performs initial hallucination detection, while the LLM module explains the detected hallucinations.

Large Language Models 126

Large Language Models Categorization LLM Explainability 126

AWS Machine Learning Blog

JUNE 25, 2024

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.

Automation 121

Automation Prompt Engineer Prompt Engineering Categorization 121

Marktechpost

NOVEMBER 19, 2024

Building a secure environment is essential to ensure the safe and reliable deployment of LLMs in various applications. Current methods to limit these LLM vulnerabilities include adversarial testing, red-teaming exercises, and manual prompt engineering.

LLM 82

LLM Prompt Engineer Prompt Engineering Large Language Models 82

Unite.AI

JANUARY 11, 2024

Synthetic Data Generation: Prompt the LLM with the designed prompts to generate hundreds of thousands of (query, document) pairs covering a wide variety of semantic tasks across 93 languages. Model Training: Fine-tune a powerful open-source LLM such as Mistral on the synthetic data using contrastive loss.

Large Language Models 144

Large Language Models Prompt Engineering Prompt Engineer BERT 144

AWS Machine Learning Blog

MAY 7, 2024

This post walks through examples of building information extraction use cases by combining LLMs with prompt engineering and frameworks such as LangChain. We also examine the uplift from fine-tuning an LLM for a specific extractive task.

Prompt Engineering 117

Prompt Engineering Prompt Engineer Large Language Models LLM 117

The MLOps Blog

SEPTEMBER 26, 2024

TL;DR Hallucinations are an inherent feature of LLMs that becomes a bug in LLM-based applications. Effective mitigation strategies involve enhancing data quality, alignment, information retrieval methods, and prompt engineering. What are LLM hallucinations? In 2022, when GPT-3.5

LLM 72

LLM Prompt Engineer Prompt Engineering Auto-complete 72

Marktechpost

MAY 11, 2024

IE tasks compel models to discern and categorize text in formats that align with predefined structures, such as named entity recognition and relation classification. However, existing LLMs typically falter when tasked with the nuanced understanding and alignment necessary for effective IE.

Large Language Models 107

Large Language Models Prompt Engineer Prompt Engineering Categorization 107

Unite.AI

AUGUST 28, 2023

LLM & Agents : At the core, the LLM processes these inputs, collaborating with specialized agents like Auto-GPT for thought chaining, AgentGPT for web-specific tasks, BabyAGI for task-specific actions, and HuggingGPT for team-based processing. Given an objective and a task, it prompts OpenAI's API and retrieves task outcomes.

Auto-complete 240

Auto-complete ChatGPT Large Language Models Neural Network 240

AWS Machine Learning Blog

OCTOBER 24, 2023

You can use LLMs in one or all phases of IDP depending on the use case and desired outcome. In this architecture, LLMs are used to perform specific tasks within the IDP workflow. Document classification – In addition to using Amazon Comprehend , you can use an LLM to classify documents using few-shot prompting.

IDP 133

IDP LLM Prompt Engineer Prompt Engineering 133

Unite.AI

JANUARY 19, 2024

Taxonomy of Hallucination Mitigation Techniques Researchers have introduced diverse techniques to combat hallucinations in LLMs, which can be categorized into: 1. Prompt Engineering This involves carefully crafting prompts to provide context and guide the LLM towards factual, grounded responses.

Large Language Models 111

Large Language Models LLM Prompt Engineering Prompt Engineer 111

AWS Machine Learning Blog

MARCH 13, 2024

The LLM analysis provides a violation result (Y or N) and explains the rationale behind the model’s decision regarding policy violation. The audio moderation workflow activates the LLM’s policy evaluation only when the toxicity analysis exceeds a set threshold. The LLM model returns an analysis result based on the prompt instructions.

Natural Language Processing 129

Natural Language Processing LLM Prompt Engineer Prompt Engineering 129

AWS Machine Learning Blog

NOVEMBER 7, 2024

Some components are categorized in groups based on the type of functionality they exhibit. Prompt catalog – Crafting effective prompts is important for guiding large language models (LLMs) to generate the desired outputs. The standalone components are: The HTTPS endpoint is the entry point to the gateway.

Generative AI 108

Generative AI Machine Learning AI AI 108

TheSequence

MARCH 11, 2024

As CTO of Humanloop , Peter has assisted companies such as Duolingo, Gusto, and Vanta in solving LLM evaluation challenges for AI applications with millions of daily users. Today, Peter shares his insights on LLM evaluations. This post is a shortened version of Peter’s original blog, titled 'Evaluating LLM Applications '.

LLM 52

LLM Categorization Software Development Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 1, 2023

Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. Operationalization journey per generative AI user type To simplify the description of the processes, we need to categorize the main generative AI user types, as shown in the following figure.

Generative AI 123

Generative AI Prompt Engineer Prompt Engineering ML 123

AWS Machine Learning Blog

SEPTEMBER 14, 2023

Document categorization or classification has significant benefits across business domains – Improved search and retrieval – By categorizing documents into relevant topics or categories, it makes it much easier for users to search and retrieve the documents they need. They can search within specific categories to narrow down results.

Categorization 116

Categorization Machine Learning Data Scientist Natural Language Processing 116

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.

Data Scientist 52

Data Scientist LLM Deep Learning Explainability 52

ODSC - Open Data Science

OCTOBER 27, 2023

Additionally, generative AI with Large Language Models (LLM) is adding powerful capabilities to IDP solutions often bridging gaps that once existed even with highly trained ML models. We will discuss some of the cloud based AI services such as Amazon Comprehend , Amazon Textract , and LLM models via Amazon Bedrock.

IDP 98

IDP LLM Large Language Models Data Science 98

Towards AI

SEPTEMBER 26, 2023

The LLM race is also continuing to heat up, with Amazon announcing significant investment into Anthropic AI. It also looks set to beat Amazon’s Alexa to market with a LLM-powered text-to-speech chatbot. – Louie Peters — Towards AI Co-founder and CEO Hottest News 1.OpenAI Five 5-minute reads/videos to keep you learning 1.Adept.ai

Prompt Engineer 64

Prompt Engineer Prompt Engineering OpenAI ChatGPT 64

AWS Machine Learning Blog

AUGUST 22, 2024

Verisk’s evaluation involved three major parts: Prompt engineering – Prompt engineering is the process where you guide generative AI solutions to generate desired output. Verisk framed prompts using their in-house clinical experts’ knowledge on medical claims.

Generative AI 106

Generative AI Auto-complete Software Development Automation 106

Bugra Akyildiz

AUGUST 27, 2023

LARs are a type of embedding that can be used to represent high-dimensional categorical data in a lower-dimensional continuous space. Together AI released Llama-2-7B-32K-Instruct , a long-range LLM that can be used for a variety of tasks, including question answering, summarization, and code generation. takes corrective actions (e.g.

OpenAI 52

OpenAI Large Language Models Categorization LLM 52

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering.

Data Science 64

Data Science Large Language Models Data Scientist Machine Learning 64

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering.

Data Science 64

Data Science Large Language Models Data Scientist Machine Learning 64

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. Sends the prompt to the LLM. In-context learning.

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. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. Sends the prompt to the LLM. In-context learning.

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. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. Sends the prompt to the LLM. In-context learning.

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52

Marktechpost

FEBRUARY 21, 2025

The lack of explicit planning mechanisms means that although LLMs generate human-like responses, their internal decision-making remains opaque. As a result, users often rely on prompt engineering to guide outputs, but this method lacks precision and does not provide insight into the model’s inherent response formulation.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering LLM 52

Snorkel AI

FEBRUARY 5, 2025

The simplest RAG system consists of a vector database, an LLM, a user interface, and an orchestrator such as LlamaIndex or LangChain. This final prompt gives the LLM more context with which to answer the users question. This final prompt gives the LLM more context with which to answer the users question.

Data Scientist 52

Data Scientist LLM Prompt Engineer Prompt Engineering 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.

Prompt Engineering 245

Prompt Engineering Prompt Engineer Python NLP 245

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering.

Data Science 52

Data Science Large Language Models Machine Learning Categorization 52

Marktechpost

FEBRUARY 13, 2025

Large language model (LLM)–based AI companions have evolved from simple chatbots into entities that users perceive as friends, partners, or even family members. Seven prevailing strategies were identified as user-driven alignment strategies, which were categorized into three broad approaches.

AI 60

AI AI Prompt Engineer Prompt Engineering 60

ODSC - Open Data Science

JANUARY 7, 2025

Classification techniques, such as image recognition and document categorization, remain essential for a wide range of industries. Classification techniques like random forests, decision trees, and support vector machines are among the most widely used, enabling tasks such as categorizing data and building predictive models.

Data Scientist 40

Data Scientist Data Science Deep Learning Machine Learning 40

AWS Machine Learning Blog

DECEMBER 5, 2023

The custom metadata helps organizations and enterprises categorize information in their preferred way. His focus area is AI/ML, and he helps customers with generative AI, large language models, and prompt engineering. Recently, her focus has been on exploring the potential of Generative AI and LLM.

Metadata 119

Metadata Auto-classification Auto-complete Content Enrichment 119

Snorkel AI

MAY 25, 2023

At inference time, users provide “prompts” to the LLM—snippets of text that the model uses as a jumping-off point. First, the model converts each token in the prompt into its embedding. The interactive chat simulator allowed non-technical users to prompt the LLM and quickly receive a response.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52