Title: Uncovering the computational ingredients that support human-like conceptual representations in large language models
Primary area: applications to neuroscience & cognitive science
Keywords: cognitive science, transformers, large language models, human-AI alignment, human-centered AI, benchmarking, cognitive benchmarking

Abstract: The ability to translate diverse patterns of inputs into structured patterns of behavior has been thought to rest on both humans’ and machines’ ability to learn robust representations of relevant concepts. The rapid advancement of transformer-based large language models (LLMs) has led to a diversity of computational ingredients — architectures, fine tuning methods, and training datasets among others — but it remains unclear which of these ingredients are most crucial for building models that develop human-like representations. Further, most current LLM benchmarks are not suited to measuring representational alignment between humans and models, making existing benchmark scores unreliable for assessing if current LLMs are making progress towards becoming useful cognitive models. Here, we address these limitations by first evaluating a set of over 70 models that widely vary in their computational ingredients on a triplet similarity task, a method well established in the cognitive sciences for measuring human conceptual representations,  using concepts from the THINGS database. Comparing human and model representations, we find that models that undergo instruction-finetuning and which have larger dimensionality of attention heads are among the most human aligned. We also find that factors such as choice of activation function, multimodal pretraining, and parameter size have limited bearing on alignment. Correlations between alignment scores and scores on existing benchmarks reveal that while some benchmarks (e.g., MMLU) are better suited than others (e.g., MUSR) for capturing representational alignment, no existing benchmark is capable of fully accounting for the variance of alignment scores, demonstrating their insufficiency in capturing human-AI alignment. Taken together, our findings help highlight the computational ingredients most essential for advancing LLMs towards models of human conceptual representation and address a key benchmarking gap in LLM evaluation.

TL;DR: We leverage a cognitive-science inspired evaluation paradigm to compare the effects of various LLM architecture and training components on alignment with human conceptual representations