Evaluating LLM Alignment With Human Trust Models

Trust plays a pivotal role in enabling effective cooperation, reducing uncertainty, and guiding decision-making in both human interactions and multi-agent systems. Although it is significant, there is limited understanding of how large language models (LLMs) internally conceptualize and reason about trust. This work presents a white-box analysis of trust representation in EleutherAI/gpt-j-6B, using contrastive prompting to generate embedding vectors within the activation space of the LLM for diadic trust and related interpersonal relationship attributes. We first identified trust-related concepts from five established trust models. We then determined a threshold for significant conceptual alignment by computing pairwise cosine similarities across 60 general emotional concepts. Then we measured the cosine similarities between the LLM’s internal representation of trust and the derived trust-related concepts. Our results show that EleutherAI/gpt-j-6B effectively separates opposing concepts while grouping related ones, and that its internal trust representations aligned most closely with the Castelfranchi socio-cognitive model, followed by the McAllister Model. These findings indicate that LLMs encode socio-cognitive constructs in their activation space in ways that support meaningful comparative analysis, enabling quantitative analysis of trust dynamics and supporting future research in multi-agent systems and trustworthy AI.