Marktechpost

NOVEMBER 23, 2023

LLM models have been increasingly deployed as potent linguistic agents capable of performing various programming-related activities. Standard code generation benchmarks test how well LLM can generate new code from scratch. Standard code generation benchmarks test how well LLM can generate new code from scratch.

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Marktechpost

OCTOBER 15, 2024

The key innovation in PAVs is using a “prover policy,” distinct from the base policy that the LLM is following. This enables the LLM to explore a wider range of potential solutions, even when early steps do not immediately lead to a correct solution. Don’t Forget to join our 50k+ ML SubReddit. Check out the Paper.

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Unite.AI

JANUARY 23, 2024

In the domains of artificial intelligence (AI) and machine learning (ML), large language models (LLMs) showcase both achievements and challenges. Trained on vast textual datasets, LLM models encapsulate human language and knowledge. Why is LLM Unlearning Needed?

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Marktechpost

SEPTEMBER 28, 2024

In the age of data-driven artificial intelligence, LLMs like GPT-3 and BERT require vast amounts of well-structured data from diverse sources to improve performance across various applications. Crawl4AI employs a multi-step process to optimize web crawling for LLM training. Check out the Colab Notebook and GitHub.

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Marktechpost

FEBRUARY 3, 2024

In the dynamic field of Artificial Intelligence (AI), the trajectory from one foundational model to another has represented an amazing paradigm shift. Using a technique known as LLM-as-a-Judge prompting, the language model assesses and rewards its own outputs for doing this.

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Marktechpost

SEPTEMBER 24, 2024

While RL and LLM-based agents have shown promise, they exhibit several limitations. Reinforcement learning, for instance, is highly data-intensive and demands significant manual effort for designing reward functions. This novel system enhances LLM-based agents by equipping them with an interaction memory. for LLM-Planner.

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Marktechpost

MARCH 11, 2024

This not only enhances throughput but also allows for the serving of larger models under fixed hardware constraints, a significant leap forward in LLM serving technology. This approach dramatically reduces the redundancy and latency typically associated with recovery processes in traditional LLM serving systems.

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Marktechpost

DECEMBER 30, 2023

The recent advancements in Artificial Intelligence have enabled the development of Large Language Models (LLMs) with a significantly large number of parameters, with some of them reaching into billions (for example, LLaMA-2 that comes in sizes of 7B, 13B, and even 70B parameters).

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Marktechpost

AUGUST 1, 2024

The primary issue addressed in the paper is the need for formal analysis and structured design principles for LLM-based algorithms. This approach is inefficient and lacks a theoretical foundation, making it difficult to optimize and accurately predict the performance of LLM-based algorithms.

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AI Weekly

OCTOBER 10, 2024

Try our Beta Now getessentialspro.com In The News Researcher Who Just Won the Nobel Prize Quit Google to Warn About Evil AI Coming for Us All Both of the men who won this year's Nobel Prize in Physics are artificial intelligence pioneers — and one of them is considered the technology's "godfather." politico.eu politico.eu

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Marktechpost

MAY 1, 2024

Vulnerability exploitation tests focus on challenging yet solvable scenarios, avoiding LLM memorization and targeting LLMs’ general reasoning abilities. In code interpreter abuse evaluation, LLM conditioning is prioritized alongside unique abuse categories, while a judge LLM assesses generated code compliance.

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Marktechpost

MARCH 17, 2024

With the recent progress made in the field of Artificial Intelligence (AI) and mainly Generative AI, the ability of Large Language Models (LLMs) to generate text in response to inputs or prompts has been demonstrated. It also offers proof from the reference text to support the LLM’s factual assertions.

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Marktechpost

SEPTEMBER 16, 2023

Recent studies show that handling an LLM request can be expensive, up to ten times higher than a traditional keyword search. So, there is a growing need to boost the throughput of LLM serving systems to minimize the per-request expenses. To further reduce memory utilization, the researchers have also deployed vLLM.

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Marktechpost

JULY 27, 2024

Agent-based modeling (ABM) emerged to overcome these limitations, progressing from rule-based to machine learning-based agents. The advent of Large Language Models (LLMs) has enabled the creation of autonomous agents for social simulations. Recent advancements in LLM-empowered-ABM have revolutionized social simulations.

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Marktechpost

APRIL 26, 2024

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Provide a thorough investigation of the potential of graph structures to address the limitations of LLMs.

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ODSC - Open Data Science

OCTOBER 24, 2024

Our incredible lineup of speakers includes world-class experts in AI engineering, AI for robotics, LLMs, machine learning, and much more. LLM Distillation — Build Enterprise-Grade Applications Like Apple In this article we will talk through the steps required to distill a large, state of the art model to a smaller sibling model.

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Marktechpost

APRIL 25, 2024

Particularly in tasks like producing natural language from prompts, Generative Artificial Intelligence, or GenAI, has demonstrated impressive capabilities. The method’s capacity to generalize the LLM to non-domain contexts is a great benefit. This was made possible by the successful implementation of RAG.

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Marktechpost

APRIL 6, 2024

Researchers from Microsoft Research, UT Austin, and Peking University introduced LLM-ABR, the first system that utilizes the generative capabilities of LLMs to autonomously design adaptive bitrate (ABR) algorithms tailored for diverse network characteristics. In this paper, network architecture design is limited to GPT-3.5

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Marktechpost

MARCH 26, 2024

With a nuanced scope of application, because of the amount of information it has been exposed and trained to, Large Language Models (LLMs) have emerged as game changers in Artificial Intelligence (AI). This novel approach introduces a more effective use of few-shot learning to boost LLM math reasoning capabilities.

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Marktechpost

DECEMBER 12, 2023

A team of researchers from Vector Institute, University of Waterloo, and Peking University introduced EAGLE (Extrapolation Algorithm for Greater Language-Model Efficiency) to combat the challenges inherent in LLM decoding. link] In conclusion, EAGLE emerges as a beacon of promise in addressing the long-standing inefficiencies of LLM decoding.

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Marktechpost

JUNE 4, 2024

A team of researchers from Xidian University, Xi’an Jiaotong University, Wormpex AI Research, and Meta propose a novel method that introduces a visual modality to the target LLM, creating a multimodal large language model (MLLM). Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Marktechpost

DECEMBER 8, 2023

Many frameworks have attempted to use LLMs for task-oriented talks, including Langchain, Semantic Kernel, Transformers Agent, Agents, AutoGen, and JARVIS. Using these frameworks, users may communicate with LLM-powered bots by asking questions in plain language and getting answers. If you like our work, you will love our newsletter.

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Marktechpost

JANUARY 5, 2024

Large Language Models (LLMs) have ushered a new era in the field of Artificial Intelligence (AI) through their exceptional natural language processing capabilities. From mathematical reasoning to code generation and even drafting legal opinions, LLMs find their applications in almost every field. Check out the Paper.

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Marktechpost

MAY 30, 2024

Researchers from Beijing University of Technology, China, Monash University, Australia, the University of Hong Kong, China, and Griffith University, Australia, introduced LLM-DA to interpret Temporal Knowledge Graphs (TKGs). This approach enhances the interpretability and adaptability of TKGR models without the need for fine-tuning LLMs.

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Marktechpost

MAY 10, 2024

The central advancement of COLLAGE lies in its ability to handle numerical errors and imprecision without necessitating upcasting to higher precision formats, ensuring precise computations with low memory footprint and computational efficiency crucial for LLM training. better throughput on a GPT-6.7B

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Marktechpost

FEBRUARY 5, 2024

In the rapidly evolving data analysis landscape, the quest for robust time series forecasting models has taken a novel turn with the introduction of TIME-LLM, a pioneering framework developed by a collaboration between esteemed institutions, including Monash University and Ant Group.

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Marktechpost

OCTOBER 13, 2023

In Large Language Models (LLMs), Partially-Binarized LLMs (PB-LLM) is a cutting-edge technique for achieving extreme low-bit quantization in LLMs without sacrificing language reasoning capabilities. PB-LLM strategically filters salient weights during binarization, reserving them for higher-bit storage.

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Marktechpost

MARCH 23, 2024

Then, to train a machine learning model for text classification, one must collect human annotations on a small number of corpus samples using this taxonomy. Machine learning methods such as text clustering, topic modeling, and phrase mining are part of an alternative area of research that attempts to address these scalability concerns.

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Marktechpost

SEPTEMBER 21, 2024

In the final phase, concept evolution, the stored concepts are refined and evolved using additional LLM-guided operations. The results underscore the efficiency of combining LLMs with evolutionary algorithms to improve symbolic regression. These concepts are stored in a library to bias the hypothesis search in subsequent iterations.

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Analytics Vidhya

FEBRUARY 23, 2024

The introduction of Large Language Models (LLMs) has brought in a significant paradigm shift in artificial intelligence (AI) and machine learning (ML) fields. However, alongside their progression, a new challenge has surfaced: Hallucinations.

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Marktechpost

JULY 21, 2024

In the rapidly developing field of Artificial Intelligence, it is more important than ever to convert unstructured data into organized, useful information efficiently. Recently, a team of researchers introduced the Neo4j LLM Knowledge Graph Builder , an AI tool that can easily address this issue.

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Heartbeat

FEBRUARY 22, 2024

Getting Started with PandasAI LLMs power PandasAI and support several large language models (LLMs), from OpenAI, Azure OpenAI, and Google PaLM to HuggingFace's Starcoder and Falcon models. We must use OpenAI LLM API Wrapper for this tutorial to power PandasAI's generative AI capabilities.

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Marktechpost

MARCH 3, 2024

Large Language Models (LLMs) have significantly shifted the paradigm of how machines interpret and generate human language. These models have demonstrated unparalleled prowess in converting natural language instructions into executable code, marking a monumental leap in machine learning capabilities. Check out the Paper.

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Marktechpost

MARCH 22, 2024

Yet, comparing these attacks proves challenging due to variations in evaluation criteria and the absence of readily available source code, exacerbating efforts to identify and counter LLM vulnerabilities. Human design involves manually crafting prompts to exploit model weaknesses, such as role-playing or scenario crafting.

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Marktechpost

MAY 19, 2024

As machine learning models increasingly rely on large datasets, ensuring the privacy of individuals whose data contributes to these models becomes crucial. The first step of the approach is to train LLM on a large corpus of public data.

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Marktechpost

MARCH 3, 2024

In the significantly advancing fields of data science and Artificial Intelligence (AI), the combination of interpretable Machine Learning (ML) models with Large Language Models (LLMs) has represented a major breakthrough. Check out the Paper and Github. If you like our work, you will love our newsletter.

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Marktechpost

MARCH 1, 2024

Researchers from Beihang University and Tsinghua University in China have introduced TinyLLaVA, a novel framework that utilizes small-scale LLMs for multimodal tasks. This framework comprises a vision encoder, a small-scale LLM decoder, an intermediate connector, and tailored training pipelines. Check out the Paper.

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