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?

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

Webinars

Trending Sources

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

Webinars

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

Guiding Instruction-Based Image Editing via Multimodal Large Language Models

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

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

Top Large Language Models LLMs Courses

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

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

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)

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

IBM Research Unveils SimPlan: Bridging the Gap in AI Planning with Hybrid Large Language Model Technology

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

LLMOps: The Next Frontier for Machine Learning Operations

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

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

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

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

Llama-3-Nanda-10B-Chat: A 10B-Parameter Open Generative Large Language Model for Hindi with Cutting-Edge NLP Capabilities and Optimized Tokenization

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

Researchers from IBM and MIT Introduce LAB: A Novel AI Method Designed to Overcome the Scalability Challenges in the Instruction-Tuning Phase of Large Language Model (LLM) Training

RakutenAI-7B: A Suite of Japanese-Oriented Large Language Models that Achieve the Great Performance on the Japanese Language Model

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

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

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

Meet DualFocus: An Artificial Intelligence Framework for Integrating Macro and Micro Perspectives within Multi-Modal Large Language Models (MLLMs) to Enhance Vision-Language Task Performance

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

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

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

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

Top 6 Kubernetes use cases

Logic-of-Thought: Enhancing Logical Reasoning in Large Language Models through Propositional Logic Augmentation

Baichuan-Omni: An Open-Source 7B Multimodal Large Language Model for Image, Video, Audio, and Text Processing

SarcasmBench: A Comprehensive Evaluation Framework Revealing the Challenges and Performance Gaps of Large Language Models in Understanding Subtle Sarcastic Expressions

Stay Connected