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 human trust models. We then determined a threshold for significant conc...
| Authors | Anushka Debnath, Stephen Cranefield, Bastin Tony Roy Savarimuthu et al. |
|---|---|
| Primary category | cs.MA |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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