What Did They Mean? How LLMs Resolve Ambiguous Social Situations across Perspectives and Roles

TL;DR

This paper investigates how large language models interpret ambiguous social situations, revealing their tendency to resolve uncertainty into coherent narratives, which poses challenges for designing socially aware AI systems.

Contribution

It provides a systematic analysis of LLM responses to ambiguous social scenarios across multiple domains, highlighting their propensity for interpretive closure and the influence of perspective.

Findings

Only 12.5% of responses genuinely preserved uncertainty.

87.5% of responses produced interpretive closure through various pathways.

Narrator perspective influences the interpretation pathway.

Abstract

People increasingly turn to large language models (LLMs) to interpret ambiguous social situations: a delayed text reply, an unusually cold supervisor, a teacher's mixed signals, or a boundary-crossing friend. Yet in many such cases, no stable interpretation can be verified from the available evidence alone. We study how LLMs respond to these situations across four domains: early-stage romantic relationships, teacher--student dynamics, workplace hierarchies, and ambiguous friendships. Across 72 responses from GPT, Claude, and Gemini, only 9 (12.5\%) genuinely preserved uncertainty. The remaining 87.5% produced interpretive closure through recurring pathways including narrative alignment, narrative reversal, normative advice under uncertainty, and hedged language that still supported a single conclusion. We further find that narrator perspective shapes the path to closure: first-person accounts more often elicited alignment, while third-person accounts invited more detached interpretation, even when the underlying situation remained comparable. Together, these findings show that LLMs do not simply assist interpersonal sensemaking; they tend to resolve ambiguity into coherent and actionable narratives. These results suggest that the central risk is not only that LLMs may misinterpret social situations, but that they may make unresolved situations feel prematurely settled. We frame this tendency as a design challenge for uncertainty-preserving social AI.