Join This Project GitHub Repo

References

References for more details on testing, especially in the AI context. Note that outside references to particular tools are not shown here.

Table of contents

References

Adrian Cockcroft

Dean Wampler and Adrian Cockcroft exchanged messages on Mastodon about lessons learned at Netflix, which are quoted in several sections of this website. See also Dean Wampler

AI for Education

The AI for Education organization provides lots of useful guidance on how to evaluate AI for different education use cases and select benchmarks for them. See also their Hugging Face page

Alignment Forum

The Alignment Forum works on many aspects of alignment.

CVS Health

CVS, the US-based retail pharmacy and healthcare services company, has a large data science team. They recently open-sourced uqlm, where UQLM stands for Uncertainty Quantification for Language Models. It is a Python package for UQ-based LLM hallucination detection.

Among the useful tools in this repository are:

A concise summary of best practices and tools for synthetic data generation.
Tuning models to improve Chain of Thought reasoning.

Dean Wampler

In Generative AI: Should We Say Goodbye to Deterministic Testing? Dean Wampler (one of this project’s contributors) summarizes the work of this project. After posting the link to the slides, Dean and Adrian Cockcroft discussed lessons learned at Netflix, which have informed this project’s content.

Ekimetrics

ClairBot from the Responsible AI Team at Ekimetrics is a research project that compares several model responses for domain-specific questions, where each of the models has been tuned for a particular domain, in this case ad serving, laws and regulations, and social sciencies and ethics.

EleutherAI

EleutherAI’s definition of Alignment is quoted in the glossary definition for it.

Evan Miller

Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations is a research paper arguing that evaluations (see the Trust and Safety Evaluation Initiative for more details) should use proper statistical analysis of their results. It is discussed in Statistical Evaluation.

Hamel Husain

Your AI Product Needs Evals is a long blog post that discusses testing of AI applications and makes many of the same points this user guide makes.

IBM

This IBM blog post, What is retrieval-augmented generation? provides a good overview of RAG.

James Thomas

James Thomas is a QA engineer who posted a link to a blog post How do I Test AI? that lists criteria to consider when testing AI-enabled systems. While the post doesn’t provide a lot of details behind the list items, the list is excellent for stimulating further investigation.

Jiayi Yuan, et al.

Give Me FP32 or Give Me Death? Challenges and Solutions for Reproducible Reasoning paper examines the influence of floating point precision on the reproducibility of inference results, even when randomness is restricted, such as using a low “temperature”. Of course, the theme of our project is dealing with the inherent randomness of inference, but there are also times when limiting that randomness is important.

John Snow Labs and Pacific.ai

John Snow Labs has created langtest, a test generation and execution framework with “60+ test types for comparing LLM & NLP models on accuracy, bias, fairness, robustness & more.”

The affiliated company Pacific.ai offers a commercial testing system with similar features.

Merriam-Webster Dictionary

The Merriam-Webster Dictionary: is quoted in our Glossary for several terms.

Michael Feathers

Michael Feathers gave a talk recently called The Challenge of Understandability at Codecamp Romania, 2024, which is discussed in Abstractions Encapsulate Complexities.

MLCommons Glossary

The MLCommons AI Safety v0.5 Benchmark Proof of Concept Technical Glossary is used to inform our Glossary.

Nathan Lambert

How to approach post-training for AI applications, a tutorial presented at NeurIPS 2024 by Nathan Lambert. It is discussed in From Testing to Tuning. See also this Interconnects post.

NIST Risk Management Framework

The U.S. National Institute of Science and Technology’s (NIST) Artificial Intelligence Risk Management Framework (AI RMF 1.0) is used to inform our Glossary.

OpenAI

An OpenAI paper on reinforcement fine tuning is discussed in From Testing to Tuning.

Announcing OpenAI Pioneers Program announced OpenAI Pioneers Program, an effort designed to help application developers optimize model performance in their domains.

Patronus

The Patronus guide, LLM Testing: The Latest Techniques & Best Practices, discusses the unique testing challenges raised by generative AI and discusses various techniques for testing these systems.

PlurAI

Plurai.ai recently created an open-source project called Intellagent that demonstrates how to exploit some recent research on automated generation of test data, knowledge graphs based on the constraints and requirements for an application, and automated test generation to verify alignment of the system to the requirements. These techniques are designed to provide more exhaustive test coverage of behaviors, including catching corner cases.

University of Tübingen

Beyond Benchmarks: A Novel Framework for Domain-Specific LLM Evaluation and Knowledge Mapping is a research effort that explores an alternative approach to knowledge representations, like the Q&A pairs we use in this guide for benchmarks, without using LLMs for generating data.

Wikipedia

Several Wikipedia articles are used as references in our Glossary and other places.