Development of Cognitive Intelligence in Pre-trained Language Models (2024)

Abstract: Recent studies show evidence for emergent cognitive abilities in Large Pre-trained Language Models (PLMs). The increasing cognitive alignment of these models has made them candidates for cognitive science theories. Prior research into the emergent cognitive abilities of PLMs has been path-independent to model training, i.e. has only looked at the final model weights and not the intermediate steps. However, building plausible models of human cognition using PLMs also requires aligning their performance during training to the developmental trajectories of children’s thinking. Guided by psychometric tests of human intelligence, we choose four task categories to investigate the alignment of ten popular families of PLMs and evaluate each of their available intermediate and final training steps: Numerical ability, Linguistic abilities, Conceptual understanding, and Fluid reasoning. We find a striking regularity: regardless of model size, the developmental trajectories of PLMs consistently exhibit a window of maximal alignment to human cognitive development. Before that window, training appears to endow models with the requisite structure to be poised to rapidly learn from experience. After that window, training appears to serve the engineering goal of reducing loss but not the scientific goal of increasing alignment with human cognition.

## introduction large pre-trained language models (plms) like google's gemini @xcite , meta's llama 2 @xcite , and openai's @xcite show human-level or even super-human performance on many cognitive performance tasks. this is true in domains such as mathematical reasoning @xcite , language comprehension @xcite @xcite , concept understanding @xcite , and analogi-cal reasoning @xcite . these successes have contributed to the hype of reaching artificial general intelligence (agi). such claims deserve to be scrutinized. there is a massive disparity between the training data scale of plms and humans. however, plms unintentionally acquire human performance characteristics from the corpora they are trained on, through residues of the values, beliefs, and biases of the authors of the texts @xcite . we approach the human alignment of plms by grounding their evaluation in frameworks for psychometric intelligence. psychometric measures of intelligence include multiple subtests spanning a range of abilities, including mathematical thinking, language comprehension, spatial thinking, and fluid reasoning @xcite @xcite @xcite . in this work, we choose representative assessments of different facets of human intelligence, modified for the required textual modality, to evaluate the cognitive alignment of plms. a second goal of our work is to move beyond cognitive alignment to also evaluate the developmental alignment of plms. the claim that the final model state of a plm approximates adult performance leaves open the question of the path by which it arrived there. ideally, the model's performance improvements over training would also track the progression of cognitive abilities over @xcite . this potential parallelism would be stronger evidence for plms as cognitive science models. researchers are increasingly addressing this question by building plms trained on a developmentally plausible corpus of child-directed speech, transcribed dialogue, and children's literature @xcite @xcite . we ask the question of developmental alignment in a theoretically important way: is the cognitive alignment of plms achieved in a path-independent or path-dependent manner? prior studies focusing on the cognitive alignment of plms have only established path independence: that models at the end of training approximate adult performance across various domains. here, we also evaluate path dependence: do the performance improvements of plms over training track the growth of these abilities in children over development? we ask this question for models of different sizes and track their developmental alignment over millions and billions of training tokens. to summarize, our key contributions are as follows: ## a suite of psychometric intelligence tasks we assemble a suite of tasks that benchmark plms across four facets of psychometric intelligence. ## models under consideration we evaluate a wide range of language model families, shown in public availability: open-source models allow us to perform a thorough analysis by accessing the latent representation and the token probability during generation. we follow availability of multiple sizes: the availability of model sizes for the same architecture and training paradigms allows us to evaluate the emergent cognitive abilities of the models. we have multiple sizes available for the llama-2, qwen, and the pythia family of models. availability of intermediate training checkpoints: this allows us to evaluate the effects of pre-training on the model outputs. together, the availability of multiple model sizes and intermediate training checkpoints allow us to best evaluate the developmental alignment of plms. amber and pythia's family of models have available intermediate training checkpoints. while amber has 360 intermediate checkpoints, the checkpoints are at 4 billion tokens each and are not at the required granularity. pythia family of models: pythia @xcite is one of the first open-source projects with the goal of scientific and transparent model development. it has 8 model sizes ranging from 70 million to 12 billion parameters, with each model trained on 286 billion tokens. the models in the suite are equivalent (in size) to popular decoder architectures like gpt-neo-(125m, 1.3b, 2.7b) and opt-(125m, 350m, 1.3b, 2.7b, 6.7b), but with the added benefits of training on a known de-duplicated corpus @xcite , using the same training order for each model size, and having 154 intermediate checkpoints to study the learning trajectories of plms. thus, the pythia suite of models is ideal for studying the cognitive and developmental alignment of plms to humans. all open-source models are obtained from huggingface @xcite , while the gated models are obtained from their respective platforms through api calls. for each model in the pythia suite, the following intermediate checkpoints are available: [1, 2, 4, 8, ... 512; 1000, 2000, 3000 ... 143000 (exponential increase in checkpoint number until the 512th checkpoint and subsequent progression of 1000 steps until the last checkpoint)], with each checkpoint representing 2 million tokens seen. overall, we test 1232 intermediate checkpoints of the pythia suite of models across all the tasks. ## cognitive and developmental alignment of plms the suite of tasks enables comprehensive evaluation of a variety of plms on their cognitive alignment to humans across four domains of psychometric intelligence: numeric abilities, linguistic abilities, concept understanding, and fluid reasoning. for the evaluation of conceptual understanding in plms, we only report the results for the zero-shot surprisal values and latent representations. this is because we see similar results for zero-shot and few-shot surprisal value-based methods (see comprehensive results in appendix b.3). the cognitive alignment of plms on psychometrics assessments is summarized below: the developmental alignment of the plms on the tasks is shown in figure 4 . we make the following key observations: ## conclusions this paper investigates the appropriateness of using plms for human cognitive and developmental modeling. it uses representative assessments of four facets of psychometric intelligence: numeric abilities, linguistic abilities, conceptual understanding, and fluid reasoning. our experiments show that plms develop cognitive abilities purely through their experience in the world, indicating that the cognitive abilities tested are acquirable through mere exposure to language distributions and do not necessarily require innate human-like inductive biases. most significantly, we find a window of monotonic development in which all models improve approximately linearly on the four cognitive abilities. before that window, we interpret training as endowing "blank slate" models with the requisite structure for rapid learning. also notable is the finding of plm scaling effects for the assessments of linguistic abilities and fluid reasoning. we propose evaluation against these tasks as a prerequisite before treating plms as models of human cognition and its development. the greater the distance |x-y| between two numbers (x, y), the faster the comparison in humans @xcite . the time taken by humans to compare two numbers (x,y) is a decreasing function of the ratio of the larger number over the smaller number max(x,y) min(x,y) @xcite . beyond these effects, we investigate the development of the latent understanding of the concept of "numbers" in the plms. as plms see more data, the average values of the similarity become larger, indicating that models learn the distinctions among numbers better (refer to figure 5 ). this is further substantiated by figure 6 , where the similarities between number words develop to be greater than the similarity between (number, non-number) words and (non-number, non-number) words.