ntqr 0.5.1

Tools for the logic of evaluation using unlabeled data

Logic tools to make your AI safer

~$: pip install ntqr

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The only evaluations possible for three LLMs (Claude, Mistral, ChatGPT) that graded a fourth one (PaLM2) doing the multistep-arithmetic test in the BIG-Bench-Mistake dataset. Using the axiom for the single binary classifier, we can reduce each LLMs possible evaluations as grader of the PaLM2 to the circumscribed planes inside the space of all possible evaluations. :::

Evaluation of noisy decision makers in unsupervised settings is a fundamental safety engineering problem. This library contains algorithms that treat this problem from a logical point of view by considering the axiomatic relationships that must govern how groups may be performing given how they agree and disagree in their answers to a common test.

A simple demonstration of the power of logic to clarify possible group evaluations for noisy experts is given by the example of two of them taking a common test and disagreeing on at least one answer. We can immediately deduce that it is impossible for both to be 100% correct. Notice the power of this elimination argument. We do not need to know anything about the test or its correct answers. By just looking at how they agree and disagree, we can immediately deduce what group evaluations are impossible. The NTQR package carries this out by formulating algebraic logical axioms that must be obeyed by all evaluations of a given type or model.

Using logic as an aid for safety is well known in other industries. Formal software verification is one requirement for certifying the software that carries out emergency shutdowns of nuclear plants. Autonomous vehicles are field tested using linear temporal logic.

But logic seems out of place with how we engineer AI agents nowadays, mostly as statistical algorithms that depend and rely on a well developed set of ideas from probability theory. In addition, for some readers it may remind them of the symbolic AI systems that were developed before probability theory became the basic tool for building algorithms. These used knowledge bases and tried to reason about the world and their actions. The logic of unsupervised evaluation we are talking about is none of those.

It is not a logic about how to make decisions. That is what the symbolic systems try to do. They do withs using a world model. These are hard define except in simulated worlds. Evaluations are not like that. A binary test is a binary test in any domain. The logic of unsupervised evaluation requires evaluation models. And these are trivial to specify and construct.

The current version is building out the axioms and logic for the case of binary classifiers and responders (R=2) and 3-class classifiers (R=3). The current version (v0.4) has taken a big step by using the case of 3-class classification to go back and rewrite the 2-class (binary) classification case. In the process, it has now become obvious how to create an alarm for any number of classes using just the algorithms used here that depend on just the single classifier axioms.

Brief guide:

1. Formal verification of unsupervised evaluations: The NTQR package is working out the logic for verifying unsupervised evaluations - what are the group evaluations that are consistent with how the test takers agree and disagree on multiple-choice exams? The page "Formal verification of evaluations" explains this further.
2. A way to stop infinite monitoring chains: Who grades the graders? Montioring unsupervised test takers raises the specter of endless graders or monitors. By having a logic of unsupervised evaluation, we can stop those infinite chains. We can verify that pairs of classifiers are misaligned, for example. Take a look at the "Logical Alarms" Jupyter notebook.
3. Jury evaluation theorems: Jury decision theorems - when does the crowd decide wisely? - go as far back as Condorcet's 1785 theorem proving that majority voting makes the crowd wiser for better than average jury members. The NTQR package contains jury evaluation theorems - when does the crowd evaluate itself wisely? It turns out it does this better than majority voting can decide. This has important consequences for how we shoud design safer Ai systems. Check out the "Evaluation is easier than decision" page.

Warning This library is under heavy development and is presently meant only for research and educational purposes. AI or any safety engineering is not solvable by any one tool. These tools are meant to be part of a broader safety monitoring system and are not meant as standalone solutions. NTQR algorithms are meant to complement, not supplant, other safety tools.