From Representation to Response: Assessing the Alignment of Large Language Models with Human Judgment Patterns

Large language models (LLMs) are sophisticated artificial intelligence systems designed to process and understand natural language at a complex level. The recent progress of these models, culminating in chat-based LLMs, has democratized the accessibility of these sophisticated intelligent systems, showcasing how machine learning methods can help humans in daily tasks. This research addresses the growing interest in understanding the mechanisms of LLMs and in evaluating their alignment with human cognition. We introduce an innovative alignment assessment strategy in the realm of LLMs that diverges from traditional approaches, utilizing the odd-one-out triplet-based task to investigate the alignment of LLMs’ representations with human object concept mental organization. Our methodology, which incorporates image captioning and zero/few-shot learning accuracy scoring, is designed to evaluate language models’ ability to predict similarities and differences in object concepts. A comprehensive experimental evaluation was conducted, involving four captioning strategies, twenty-four LLMs across eight model families, and three scoring procedures, utilizing a significantly large dataset for enhanced understanding of LLM comprehensibility. Finally, our study explores the impact of object description comprehensiveness on model-human representation alignment and analyzes a subset of randomly selected triplets to assess how LLMs are able to represent different levels of human judgment patterns.