Pseudointelligence: A Unifying Lens on Language Model Evaluation (2023)

Abstract: With large language models surpassing human performance on an increasing number of benchmarks, we must take a principled approach for targeted evaluation of model capabilities. Inspired by pseudorandomness, we propose pseudointelligence, which captures the maxim that “(perceived) intelligence lies in the eye of the beholder.” That is, that claims of intelligence are meaningful only when their evaluator is taken into account. Concretely, we propose a complexity-theoretic framework of model evaluation cast as a dynamic interaction between a model and a learned evaluator. We demonstrate that this framework can be used to reason about two case studies in language model evaluation, as well as analyze existing evaluation methods.

## introduction recent works claim that gpt-4 achieves expertlevel performance on complex reasoning tasks @xcite , with some researchers concluding that it exhibits sparks of intelligence @xcite . but how should intelligence be evaluated? this question dates back to descartes (1637), formalized by @xcite , and continues to be the subject of recent discussion @xcite @xcite . however, none of these attempts prescribe a particular evaluator (e.g., sequence of questions) that guarantees the intelligence of the evaluated model. this is not a coincidence. we argue that intelligence is in the eye of the evaluator. this maxim is particularly important for the future of natural language processing (nlp): progress cannot be measured by static benchmarks @xcite @xcite , with contemporary models surpassing human performance on new evaluations within a few years @xcite , and benchmarks leaking into training data @xcite . instead, we define the notion of pseudointelligence. analogous to pseudorandomness @xcite , which measures a distribution by its distinguishability from true randomness, pseudointelligence applies to the evaluation of the capabilities of learned models. importantly, a claim that a model has learned a certain capability is innately entangled with the distinguishing ability of an evaluator. with the future of nlp in mind, we focus on learned evaluators. these evaluators are trained on samples specific to a given capability, much like the models they assess. notably, emerging evaluation methods, such as model-based evaluation @xcite and adversarial evaluation @xcite @xcite , can be viewed as specific instances of the framework we propose. our main takeaways are: p1: a claim of intelligence must be supplemented by an explicitly-defined evaluator and (intelligent) capabilities (section 3.1). ## background pseudorandomness. first, a brief introduction of pseudorandomness, which forms the conceptual backbone of our framework. for an extended introduction, see @xcite . tessa and obi are playing a game, and would like to decide who gets to go first. they agree to make the decision based on a coin toss: tessa tosses a coin, and obi calls heads or tails. if obi calls the outcome correctly he gets to go first, and otherwise tessa does. now consider two cases: tessa would not be happy with a coin toss in the second case, because obi could call the coin correctly with ease. in other words, the coin toss is no longer "random-enough" due to obi's increased computational power. more generally, a distribution is pseudorandom against a particular observer if she cannot distinguish it from a truly random. formally, one can view definition 2.1 as consisting of an ideal source (uniformly random elements), and a pseudoideal approximation to this source (pseudorandom elements). unlike randomness, intelligence does not have a canonical mathematical operationalization. ## existing evaluations through the lens of pseudointelligence pseudointelligence can serve a unifying role by allowing a direct comparison between different evaluation methods. we cast several existing evaluation paradigms into our framework. ## conclusion this paper introduces a principled framework for model evaluation, inspired by the theory of pseudorandomness. our main message is that claims about model capability must be supplemented with a thorough discussion of the resources given to the evaluator, especially in settings where model resources are largely unknown (e.g. openai, 2023). central to our framework is a model-based evaluator that is targeted at specific capabilities as well as specific models (via multi-round interactions). we hope our framework encourages rigorous analysis of llm evaluation, and helps unify the study of this increasingly-important topic.