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
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
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 repo 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 summarizes the work of this project. After posting the link to the slides, he and Adrian Cockcroft discussed lessons learned at Netflix.
EleutherAI
EleutherAI’s definition of alignment is quoted in our glossary definition.
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 Tests.
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.
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.
Wikipedia
Several Wikipedia articles are used as references in our glossary and other places.