Token 1.15: What are Hallucinations: a Critical Challenge or an Opportunity?

Introduction

To set the right tone for 2024, we decided to start with hallucinations 😉 

With the rise of interest in foundation models (FMs) that are extraordinarily impressive at producing data across various modalities – text, images, video, and audio – another phenomenon has appeared: Hallucinations. Though the term anthropomorphizes the algorithms, it has become widely accepted in both the business and academic sectors.

In this article, we want to understand what causes them and how to deal with them, as well as provide you with some ideas on how hallucinations can be beneficial. Let's immerse ourselves in our first deep dive of this interesting year!

• What exactly are hallucinations in the context of foundation models?

• Why do hallucinations occur?

• Strategies and methods for identifying when a model is hallucinating.

• Why hallucinations are generally problematic, but can be beneficial.

• Ways to reduce or possibly eliminate hallucinations.

• Bonus Resources: A curated list of datasets, libraries, and tools for dealing with hallucinations.

As most research and literature on hallucinations currently focus on text-based models – Large Language Models aka LLMs – we also center our article around this subset of foundation models.

Defining Hallucinations: What exactly are hallucinations in the context of foundation models?

In LLM models, "hallucination" refers to instances where the model generates content that isn't based on real or accurate information. This can happen when a model produces text with details, facts, or claims that are fictional, misleading, or completely made up, instead of giving reliable and truthful information.

Hallucination cases in LLMs can be broken down into the following categories:

That’s a fun video to watch:

Thank you for reading, please feel free to share with your friends and colleagues. In the next couple of weeks, we are announcing our referral program 🤍

Previously in the FM/LLM series:

• Token 1.0: Where are you in the FMOps Infrastructure Stack?

• Token 1.1: From Task-Specific to Task-Centric ML: A Paradigm Shift

• Token 1.2: Use Cases for Foundation Models and Key Benefits 

• Token 1.3: What is Retrieval-Augmented Generation (RAG)?

• Token 1.4: Foundation Models – The Building Blocks

• Token 1.5: From Chain-of-Thoughts to Skeleton-of-Thoughts, and everything in between

• Token 1.6: Transformer and Diffusion-Based Foundation Models

• Token 1.7: What Are Chain-of-Verification, Chain of Density, and Self-Refine?

• Token 1.8: Silicon Valley of AI Chips and Semiconductors

• Token 1.9: Open- vs Closed-Source AI Models: Which is the Better Choice for Your Business?

• Token 1.10: Large vs Small in AI: The Language Model Size Dilemma

• Token 1.13: Where to Get Data for Data-Hungry Foundation Models

• Token 1.14: What is Synthetic Data and How to Work with it?