LLM

Statistical or Sentient Part 2- Tokens and Thoughts

What drives the eerily human-like capabilities of large language models like GPT-3? Their artificial "minds" operate on a simple principle - predicting the next most likely token. Understanding these basic mechanics provides the key to unlocking their full potential through prompt engineering.

In a previous article, we explored modelling the "mind" of large language models (LLMs) and how they process information. As debates continue about artificial general intelligence and whether LLMs could ever be truly sentient, it is important to dive deeper into understanding their core functions.

How do these artificial neural networks actually think and reason? What are the implications for properly steering their capabilities through prompt engineering?

This article will break down the fundamental nature of LLMs as next token predictors. Grasping this concept is key to utilizing them effectively and pushing AI advancements forward. By better understanding the mechanisms

Measuring How Much Leading AI Chatbots Hallucinate

Vectara recently introduced a unique leaderboard that ranks AI chatbots based on how well they avoid 'hallucinations.' Find out which AI comes out on top and why it matters!

Introduction

One of the concerns with modern AI chatbots is their tendency to "hallucinate" - to generate fictional facts and information that has no basis in reality. This issue came to prominence recently when a law firm got in trouble for submitting fake legal opinions generated by the AI tool ChatGPT. To better understand this problem, the company Vectara has created an "AI Hallucination Leaderboard" that ranks various leading chatbots based on their rate of hallucination.

Vectara's Evaluation Approach

Methodology

Vectara's approach involves feeding over 800 short reference documents to various LLMs and requesting factual summaries. The responses are then

Optimizing Large Language Models to Maximize Performance

Getting the most out of large language models requires the artful application of optimization techniques like prompt engineering, retrieval augmentation, and fine-tuning. This guide explores proven methods for maximizing LLM performance.

In the previous articles, we explored the process of developing effective prompts from scratch. However, there are many cases where you inherit existing prompts that have degraded over time or are no longer optimal for current large language models.

Optimizing large language models (LLMs) for real-world production applications remains one of the most persistent challenges in deploying

Leveraging Associative Memory in AI for Effective Prompt Engineering Featured Post

Your brain's magic memory has more in common with ChatGPT than you may think! Let's explore the surprising parallels between human and AI intelligence.

You know the feeling: you're racking your brain for a specific memory or piece of information, but it just won't come to you. Then, out of the blue—maybe you're chatting with a friend, reading a book, or even listening to a song—the right words trigger that elusive memory, making it crystal clear. This phenomenon isn't limited to us humans; surprisingly, it bears a resemblance to how large language models (LLMs) like ChatGPT function.

Associative memory in humans operates in a manner that's strikingly similar to the way LLMs function. While LLMs rely on statistical data and probabilities to

SLiCK: A Framework for Understanding Large Language Models Featured Post

Peek under the hood of LLMs with SLiCK- a conceptual framework that segments AI operations into distinct components, shedding light on the inner workings of these complex "black box" systems.

Large language models (LLMs) like GPT-4 have demonstrated remarkable proficiency in generating human-like text. However, as AI systems grow more advanced, their inner workings become increasingly complex and opaque. This has led to concerns about bias, accountability, and the "black box" nature of LLMs.

To address these issues, it can be useful to view LLMs through the lens of a familiar computing construct – the Central Processing Unit (CPU) of a computer. Much like a CPU processes instructions, an LLM processes textual prompts to produce relevant outputs. Exploring this CPU analogy provides a conceptual framework to demystify LLMs and unlock their

The Black Box Problem: Opaque Inner Workings of Large Language Models

Large language models like GPT-4 are powerful but opaque "black boxes." New techniques for explainable AI and transparent design can help unlock their benefits while auditing risks.

Large language models (LLMs) like GPT-3 have demonstrated impressive natural language capabilities, but their inner workings remain poorly understood. This "black box" nature makes LLMs potentially problematic when deployed in sensitive real-world applications.

What is the LLM Black Box Problem?

Language Learning Models (LLMs) are powerful tools that rely on deep learning to process and analyse vast amounts of text. Today they're the brains behind everything from customer service chatbots to advanced research tools.

Yet, despite their utility, they operate as "black boxes," obscuring the logic behind their decisions. This opacity isn't just a tech puzzle; it's a problem with

Statistical or Sentient Part 2- Tokens and Thoughts

Measuring How Much Leading AI Chatbots Hallucinate

Introduction

Vectara's Evaluation Approach

Methodology

Optimizing Large Language Models to Maximize Performance

Leveraging Associative Memory in AI for Effective Prompt Engineering Featured Post

SLiCK: A Framework for Understanding Large Language Models Featured Post

The Black Box Problem: Opaque Inner Workings of Large Language Models

What is the LLM Black Box Problem?

Featured

Reasoners - A New Approach to Smarter AI

Generative AI - The New Compiler

How Prompt Keywords (Magic Words) Optimize Language Model Performance

Popular Tags

News

Prompt Engineering

LLM

ChatGPT

Lesson

LLM

Posts tagged with LLM

Introduction

Vectara's Evaluation Approach

Methodology

What is the LLM Black Box Problem?

Prompt Engineering Institute

Featured

Reasoners - A New Approach to Smarter AI

Generative AI - The New Compiler

How Prompt Keywords (Magic Words) Optimize Language Model Performance

Popular Tags

News

Prompt Engineering

LLM

ChatGPT

Lesson