LLMs Training SLMs

Microsoft Research shows how they used LLMs to train SLMs without any RLHF…

Large Impact: The Rise of Small Language Models

The growth of products and services related to Large Language Models (LLMs) has been remarkable. And it’s evident that...

RAG, Data Privacy, Attack Methods & Safe-Prompts

A study explored RAG security vulnerabilities and ways in which private data can be accessed via retrieval datasets....

MultiHop-RAG

This direction in RAG architecture to establish a wider context via a process of orchestration and chains over...

Time-Aware Adaptive RAG (TA-ARE)

Understanding Time-Aware Adaptive Retrieval (TA-ARE)

Introduction

With the advent of Large Language Models (LLMs)...

Proxy Fine-Tuning LLMs

Proxy fine-tuning achieves the results of directly tuning a LLM, but by accessing only its prediction…

Demonstrate, Search, Predict (DSP) for LLMs

This study which is just over a year old from Stanford, makes for interesting reading and illustrates how far we have...

T-RAG = RAG + Fine-Tuning + Entity Detection

The T-RAG approach is premised on combining RAG architecture with an open-source fine-tuned LLM and an entities tree...

Beyond Chain-of-Thought LLM Reasoning

This approach can be implemented on a prompt level and does not require any dedicated frameworks or pre-processing.

Comparing Human, LLM & LLM-RAG Responses

A fairly recent study, focused on the healthcare & preoperative medicine compared expert human feedback with LLM...