Large Language Models, ML and Natural Language Processing

Large Language Models

Natural Language Processing

Will Large Language Models End Programming?

Unite.AI

NOVEMBER 14, 2023

In areas like image generation diffusion model like Runway ML , DALL-E 3 , shows massive improvements. The belief that natural language processing by AI can fully replace the precision and complexity of formal mathematical notations and traditional programming is, at best, premature. Introducing, Motion Brush.

Will Large Language Models End Programming?

SepLLM: A Practical AI Approach to Efficient Sparse Attention in Large Language Models

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A Unified Acoustic-to-Speech-to-Language Embedding Space Captures the Neural Basis of Natural Language Processing in Everyday Conversations

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A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models

Arena Learning: Transforming Post-Training of Large Language Models with AI-Powered Simulated Battles for Enhanced Efficiency and Performance in Natural Language Processing

SmolLM2 Released: The New Series (0.1B, 0.3B, and 1.7B) of Small Language Models for On-Device Applications and Outperforms Meta Llama 3.2 1B

Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

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EasyQuant: Revolutionizing Large Language Model Quantization with Tencent’s Data-Free Algorithm

Guiding Instruction-Based Image Editing via Multimodal Large Language Models

Meta AI Releases Llama Guard 3-1B-INT4: A Compact and High-Performance AI Moderation Model for Human-AI Conversations

Combining the Best of Both Worlds: Retrieval-Augmented Generation for Knowledge-Intensive Natural Language Processing

Enhancing the Accuracy of Large Language Models with Corrective Retrieval Augmented Generation (CRAG)

T-FREE: A Tokenizer-Free Approach for Efficient and Scalable Text Encoding in Large Language Models

From RAG to ReST: A Survey of Advanced Techniques in Large Language Model Development

This AI Paper by Scale AI Introduces GSM1k for Measuring Reasoning Accuracy in Large Language Models LLMs

This AI Paper Introduces TelecomGPT: A Domain-Specific Large Language Model for Enhanced Performance in Telecommunication Tasks

Researchers at Stanford Introduces In-Context Vectors (ICV): A Scalable and Efficient AI Approach for Fine-Tuning Large Language Models

Leveraging Linguistic Expertise in NLP: A Deep Dive into RELIES and Its Impact on Large Language Models

Understanding the Inevitable Nature of Hallucinations in Large Language Models: A Call for Realistic Expectations and Management Strategies

AutoTRIZ: An Artificial Ideation Tool that Leverages Large Language Models (LLMs) to Automate and Enhance the TRIZ (Theory of Inventive Problem Solving) Methodology

Researchers from the University of Maryland Introduce an Automatic Text Privatization Framework that Fine-Tunes a Large Language Model via Reinforcement Learning

Cobra for Multimodal Language Learning: Efficient Multimodal Large Language Models (MLLM) with Linear Computational Complexity

LLM2LLM: UC Berkeley, ICSI and LBNL Researchers’ Innovative Approach to Boosting Large Language Model Performance in Low-Data Regimes with Synthetic Data

Microsoft AI Research Introduces Generalized Instruction Tuning (called GLAN): A General and Scalable Artificial Intelligence Method for Instruction Tuning of Large Language Models (LLMs)

Microsoft Researchers Combine Small and Large Language Models for Faster, More Accurate Hallucination Detection

Google AI Introduces Tx-LLM: A Large Language Model (LLM) Fine-Tuned from PaLM-2 to Predict Properties of Many Entities that are Relevant to Therapeutic Development

This AI Paper from China Introduces a Groundbreaking Approach to Enhance Information Retrieval with Large Language Models Using the INTERS Dataset

NVIDIA AI Researchers Explore Upcycling Large Language Models into Sparse Mixture-of-Experts

Researchers at Arizona State University Evaluates ReAct Prompting: The Role of Example Similarity in Enhancing Large Language Model Reasoning

Can AI Think Better by Breaking Down Problems? Insights from a Joint Apple and University of Michigan Study on Enhancing Large Language Models

BurstAttention: A Groundbreaking Machine Learning Framework that Transforms Efficiency in Large Language Models with Advanced Distributed Attention Mechanism for Extremely Long Sequences

Hugging Face Releases Picotron: A Tiny Framework that Solves LLM Training 4D Parallelization

LLMOps: The Next Frontier for Machine Learning Operations

This AI Paper by NVIDIA Introduces NVLM 1.0: A Family of Multimodal Large Language Models with Improved Text and Image Processing Capabilities

Adam-mini: A Memory-Efficient Optimizer Revolutionizing Large Language Model Training with Reduced Memory Usage and Enhanced Performance

Meet PowerInfer: A Fast Large Language Model (LLM) on a Single Consumer-Grade GPU that Speeds up Machine Learning Model Inference By 11 Times

Can Large Language Models Understand Context? This AI Paper from Apple and Georgetown University Introduces a Context Understanding Benchmark to Suit the Evaluation of Generative Models

What are Small Language Models (SLMs)?

Michelangelo: An Artificial Intelligence Framework for Evaluating Long-Context Reasoning in Large Language Models Beyond Simple Retrieval Tasks

Neural Processing Units (NPUs): The Driving Force Behind Next-Generation AI and Computing

NVIDIA Researchers Introduce Order-Preserving Retrieval-Augmented Generation (OP-RAG) for Enhanced Long-Context Question Answering with Large Language Models (LLMs)

Trust-Align: An AI Framework for Improving the Trustworthiness of Retrieval-Augmented Generation in Large Language Models

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