Analytics Vidhya

JANUARY 4, 2024

Introduction Developing open-source libraries and frameworks in machine learning has revolutionized how we approach and implement various algorithms and models. What is […] The post Exploring MPT-7B/30B: The Latest Breakthrough in Open-Source LLM Technology appeared first on Analytics Vidhya.

LLM 306

LLM Machine Learning Algorithm Generative AI 306

AI News

NOVEMBER 28, 2024

Addressing unexpected delays and complications in the development of larger, more powerful language models, these fresh techniques focus on human-like behaviour to teach algorithms to ‘think. OpenAI and other leading AI companies are developing new training techniques to overcome limitations of current methods.

Large Language Models 321

Large Language Models Big Data OpenAI AI Modeling 321

AI News

JANUARY 15, 2025

As you look to secure a LLM, the important thing to note is the model changes. The solution is self-optimising, using Ciscos proprietary machine learning algorithms to identify evolving AI safety and security concernsinformed by threat intelligence from Cisco Talos.

Big Data 300

Big Data AI AI LLM 300

AI News

JUNE 28, 2023

They have contributed to popular open-source foundational models like MPT-30B, as well as the training algorithms powering MosaicML’s products. The post Databricks acquires LLM pioneer MosaicML for $1.3B Check out AI & Big Data Expo taking place in Amsterdam, California, and London. appeared first on AI News.

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LLM Large Language Models Big Data Neural Network 296

Unite.AI

MARCH 26, 2025

This shift has been driven by advances in sensors, computing power, and algorithms. The integration of LLMs is beginning to redefine what embodied AI can achieve, making robots more capable of learning and adapting. This evolution of LLMs is enabling engineers to evolve embodied AI beyond performing some repetitive tasks.

Robotics 194

Robotics Large Language Models LLM Natural Language Processing 194

Unite.AI

JUNE 14, 2023

The Magic of LLM in Security Generative AI is an advancement over older models used in machine learning algorithms that were great at classifying or clustering data based on trained learning of synthetic samples. The modern LLMs are trained on millions of examples from big code repositories, (e.g., appeared first on Unite.AI.

LLM 270

LLM Generative AI Machine Learning Automation 270

AWS Machine Learning Blog

JANUARY 28, 2025

Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk.

LLM 94

LLM Large Language Models ML Algorithm 94

Analytics Vidhya

JULY 12, 2023

By distributing this poisoned model on Hugging Face, a widely-used platform for […] The post PoisonGPT: Hugging Face LLM Spreads Fake News appeared first on Analytics Vidhya.

LLM 268

LLM Large Language Models Algorithm Artificial Intelligence 268

Unite.AI

NOVEMBER 11, 2024

However, despite these abilities, how LLMs store and retrieve information differs significantly from human memory. In contrast, LLMs rely on static data patterns and mathematical algorithms. LLMs do not have explicit memory storage like humans. LLMs, on the other hand, are static after training.

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LLM Large Language Models Natural Language Processing BERT 176

Unite.AI

APRIL 7, 2025

Operating on a shoestring budget, DeepSeek developed its own LLM, and a ChatGPT-type application for queries with a far smaller investment than those for similar systems built by American and European companies. The approach of DeepSeek opens up a window into LLM development for smaller organizations that dont have billions to spend.

LLM 157

LLM ChatGPT Data Scientist AI Modeling 157

Flipboard

MARCH 22, 2025

The large language models (LLMs) that underpin products like OpenAI's ChatGPT, for instance, need to devour enormous datasets of written words to fine tune an algorithm to follow the rules of language. They're so hungry for raw data, in fact, that original material for these algorithms to gobble up is becoming hard to come by.

Software Engineer 162

Software Engineer Large Language Models LLM Artificial Intelligence 162

Unite.AI

DECEMBER 19, 2024

For instance, theyve used LLMs to look at how small changes in input data can affect the models output. By showing the LLM examples of these changes, they can determine which features matter most in the models predictions. You dont need to understand complex algorithms or data to get answers.

Large Language Models 205

Large Language Models Explainable AI Explainability Conversational AI 205

Marktechpost

FEBRUARY 1, 2025

Some of the most prominent RL algorithms include: Q-Learning: Agents learn a value function Q(s, a) , where s state and a action. Actor-Critic Methods: Combining the strengths of value-based and policy-based methods, actor-critic algorithms maintain both a policy (the actor) and a value function estimator (the critic).

LLM 101

LLM Robotics Neural Network Large Language Models 101

Towards AI

OCTOBER 10, 2024

The evolution of Large Language Models (LLMs) allowed for the next level of understanding and information extraction that classical NLP algorithms struggle with. This is where LLMs come into play with their capabilities to interpret customer feedback and present it in a structured way that is easy to analyze.

Metadata 119

Metadata LLM Algorithm Large Language Models 119

Unite.AI

APRIL 25, 2024

Recent innovations include the integration and deployment of Large Language Models (LLMs), which have revolutionized various industries by unlocking new possibilities. More recently, LLM-based intelligent agents have shown remarkable capabilities, achieving human-like performance on a broad range of tasks.

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LLM Large Language Models Software Development BERT 196

Analytics Vidhya

DECEMBER 13, 2023

vLLM, an open-source library for fast LLM inference and serving, addresses these challenges by working with a novel attention algorithm called PagedAttention. This algorithm effectively […] The post Decoding vLLM: Strategies for Supercharging Your Language Model Inferences appeared first on Analytics Vidhya.

Large Language Models 318

Large Language Models LLM Algorithm Generative AI 318

Unite.AI

MARCH 3, 2025

Imandra is an AI-powered reasoning engine that uses neurosymbolic AI to automate the verification and optimization of complex algorithms, particularly in financial trading and software systems. This feature is based on a mathematical technique called Cylindrical Algebraic Decomposition, which weve lifted to algorithms at large.

Automation 162

Automation Algorithm Explainability Large Language Models 162

AWS Machine Learning Blog

FEBRUARY 12, 2025

The evaluation of large language model (LLM) performance, particularly in response to a variety of prompts, is crucial for organizations aiming to harness the full potential of this rapidly evolving technology. Both features use the LLM-as-a-judge technique behind the scenes but evaluate different things.

LLM 88

LLM Generative AI Automation Machine Learning 88

Unite.AI

FEBRUARY 13, 2025

Reinforcement Learning from Human Feedback (RLHF) One of the most widely used RL techniques in LLM training is RLHF. Instead of relying solely on predefined datasets, RLHF improves LLMs by incorporating human preferences into the training loop. This approach has been employed in improving models like ChatGPT and Claude.

Large Language Models 185

Large Language Models LLM Machine Learning ChatGPT 185

AWS Machine Learning Blog

FEBRUARY 21, 2025

Fine-tuning a pre-trained large language model (LLM) allows users to customize the model to perform better on domain-specific tasks or align more closely with human preferences. You can use supervised fine-tuning (SFT) and instruction tuning to train the LLM to perform better on specific tasks using human-annotated datasets and instructions.

LLM 78

LLM AI AI Data Scientist 78

Unite.AI

MAY 17, 2024

This training data exposes the LLM to the intricate patterns and nuances of human language. At the heart of these LLMs lies a sophisticated neural network architecture called a transformer. This allows the LLM to understand each word's context and predict the most likely word to follow in the sequence.

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LLM Large Language Models Neural Network Artificial Intelligence 157

The MLOps Blog

MARCH 20, 2025

TL;DR LLM agents extend the capabilities of pre-trained language models by integrating tools like Retrieval-Augmented Generation (RAG), short-term and long-term memory, and external APIs to enhance reasoning and decision-making. The efficiency of an LLM agent depends on the selection of the right LLM model.

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LLM OpenAI Python Large Language Models 59

Marktechpost

APRIL 3, 2025

First, network topologies should align with LLM trainings structured traffic patterns, which differ from traditional workloads. cost efficiency with minimal performance trade-offs in LLM training. For AllReduce, a Multi-Ring algorithm minimizes congestion by efficiently mapping paths and utilizing idle links to enhance bandwidth.

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LLM Algorithm ML AI 64

Flipboard

MARCH 7, 2025

has developed a series of large language models that can rival algorithms from OpenAI and Anthropic PBC, multiple publications reported today.Sources told Bloomberg that the LLM se Microsoft Corp.

LLM 154

LLM OpenAI Large Language Models Algorithm 154

Unite.AI

APRIL 1, 2025

However, an LLM may hide or deny its inability to actually watch videos, unless you call them out on it: Having been asked to provide a subjective evaluation of a new research paper's associated videos, and having faked a real opinion, ChatGPT-4o eventually confesses that it cannot really view video directly.

LLM 144

LLM AI AI Large Language Models 144

Unite.AI

JANUARY 4, 2025

Their study , published in Science , employed OpenAIs GPT-4 Turbo , a large language model (LLM), to engage conspiracy believers in personalized, evidence-based conversations. The success of AI also depends on the quality of its training data and algorithms.

AI 229

AI AI Large Language Models Generative AI 229

Unite.AI

OCTOBER 27, 2024

Developers can easily connect their applications with various LLM providers, databases, and external services while maintaining a clean and consistent API. The framework's tokenization and stemming algorithms support multiple languages, making it valuable for international applications. TensorFlow.js TensorFlow.js environments.

Neural Network 157

Neural Network Machine Learning NLP Natural Language Processing 157

Towards AI

MARCH 12, 2025

🔎 Decoding LLM Pipeline Step 1: Input Processing & Tokenization 🔹 From Raw Text to Model-Ready Input In my previous post, I laid out the 8-step LLM pipeline, decoding how large language models (LLMs) process language behind the scenes. tokenize("Let's learn about LLMs! GPT-4 and GPT-3.5

LLM 54

LLM BERT Neural Network Metadata 54

Ehud Reiter

MARCH 2, 2025

Reasoning algorithms can use the BN to calculate the probability of an unknown node from observables; for example if a person has bad Smoking, ChestPain, and WeightLoss, but has not been exposed to heavy Pollution, how likely is he to have LungCancer? For GPT4 (the best performing LLM in this context, F-score averages around 0.6,

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Explainability Large Language Models Algorithm LLM 129

Snorkel AI

MARCH 4, 2025

Misaligned LLMs can generate harmful, unhelpful, or downright nonsensical responsesposing risks to both users and organizations. This is where LLM alignment techniques come in. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

LLM 52

LLM Large Language Models Data Quality Prompt Engineer 52

Unite.AI

JULY 19, 2024

Some rely on machine learning algorithms, while others use rule-based systems or statistical methods. To deal with this issue, various tools have been developed to detect and correct LLM inaccuracies. The tool employs advanced algorithms to deliver precision hallucination detection.

LLM 274

LLM AI AI Data Quality 274

Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). Impact of the LLM Black Box Problem 1.

LLM 261

LLM Machine Learning Explainability Algorithm 261

Marktechpost

JUNE 16, 2024

The focus is on creating methods that can more accurately guide LLMs through each step of the reasoning process. Existing research includes various frameworks and models to improve LLM reasoning capabilities. Chain-of-Thought (CoT) prompting guides LLMs to break down tasks into intermediate steps, enhancing performance.

Algorithm 138

Algorithm Large Language Models LLM Automation 138

IBM Journey to AI blog

SEPTEMBER 3, 2024

Hay argues that part of the problem is that the media often conflates gen AI with a narrower application of LLM-powered chatbots such as ChatGPT, which might indeed not be equipped to solve every problem that enterprises face. “A lot of research is going into developing more computationally efficient algorithms,” Smolinksi adds.

Generative AI 308

Generative AI LLM Large Language Models AI 308

ML @ CMU

SEPTEMBER 13, 2024

If a certain phrase exists within the LLM training data (e.g., is not itself generated text) and it can be reproduced with fewer input tokens than output tokens, then the phrase must be stored somehow within the weights of the LLM. We show that it appropriately ascribes many famous quotes as being memorized by existing LLMs (i.e.,

LLM 129

LLM Neural Network Large Language Models OpenAI 129

Marktechpost

FEBRUARY 16, 2025

The model achieves this through a hierarchical token pruning algorithm, which dynamically removes less relevant context tokens. The framework enhances LLM capabilities by integrating hierarchical token pruning, KV cache offloading, and RoPE generalization. Also, decoding throughput is increased by 3.2 on enterprise-grade GPUs (L40S).

LLM 82

LLM AI Researcher AI Research Large Language Models 82

Bugra Akyildiz

DECEMBER 28, 2024

Speculative decoding applies the principle of speculative execution to LLM inference. The process involves two main components: A smaller, faster "draft" model The larger target LLM The draft model generates multiple tokens in parallel, which are then verified by the target model.

LLM 52

LLM Large Language Models BERT Python 52