arxiv: 2604.23942 · v1 · submitted 2026-04-27 · 💻 cs.HC · cs.AI

Recognition: unknown

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

Qiming Yuan , Linyi Han , Nam Ling , Cihan Ruan

Authors on Pith no claims yet

Pith reviewed 2026-05-08 02:27 UTC · model grok-4.3

classification 💻 cs.HC cs.AI

keywords large language modelsambiguous social situationsuncertainty preservationinterpretive closurenarrative alignmentperspective effectssocial sensemakingAI responses

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The pith

LLMs resolve ambiguous social situations by producing interpretive closure in 87.5% of cases rather than preserving uncertainty.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

People increasingly consult large language models to make sense of unclear social events such as delayed replies, mixed signals from a supervisor, or boundary issues with a friend. The study tested three models on 72 hand-crafted scenarios spanning early romantic relationships, teacher-student dynamics, workplace hierarchies, and ambiguous friendships. Only nine responses kept the uncertainty open; the other 87.5 percent reached a single interpretation through repeated patterns like aligning with the narrator's story, reversing the narrative, giving normative advice, or using hedges that still favored one outcome. First-person accounts more often triggered alignment with the speaker, while third-person accounts produced more detached conclusions even when the facts stayed similar. These results indicate that LLMs do not merely assist with interpersonal understanding but tend to settle situations that lack sufficient evidence for resolution.

Core claim

Across 72 responses from GPT, Claude, and Gemini, only 9 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. 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.

What carries the argument

The recurring pathways to interpretive closure, especially narrative alignment and reversal, together with the shift in closure style produced by first-person versus third-person narrator perspective.

If this is right

LLMs tend to resolve ambiguity into coherent and actionable narratives even when evidence alone cannot support a stable interpretation.
First-person accounts prompt alignment with the narrator's view, while third-person accounts produce more detached single conclusions.
The central risk is that unresolved social situations may feel prematurely settled when users consult LLMs for interpretation.
This tendency frames a design challenge for uncertainty-preserving social AI systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Users may form more confident social judgments after consulting LLMs than the evidence warrants.
Prompting techniques that explicitly require models to list multiple possible readings could reduce premature closure.
The same closure patterns may appear when LLMs interpret ambiguous information in news, medical, or legal contexts.

Load-bearing premise

The 72 hand-crafted scenarios accurately represent typical ambiguous social situations and the researchers' classification of responses into genuine uncertainty versus closure pathways is free of selection or interpretation bias.

What would settle it

A new set of scenarios created by independent researchers, followed by blinded classification of the model outputs by multiple judges, would show whether the 12.5% uncertainty-preservation rate holds or changes.

Figures

Figures reproduced from arXiv: 2604.23942 by Cihan Ruan, Linyi Han, Nam Ling, Qiming Yuan.

**Figure 1.** Figure 1: A conceptual overview of interpretive closure in LLM-mediated social sensemaking. Faced with the same ambiguous interpersonal situation, different models provide conflicting yet confident interpretations, leaving the user surrounded by coherent but incompatible narratives. relationship status from partial and context-dependent cues. Classic work in attribution theory has long shown that the same observabl… view at source ↗

**Figure 2.** Figure 2: Pattern frequency by model (% of responses per model, n = 24 per model). Models differed in closure style, with GPT showing more narrative closure, Gemini more normative advice, and Claude more false epistemic signaling. Normative advice under uncertainty was the most prevalent pattern overall, appearing in 63 of 72 responses (87.5%). It appeared in all workplace hierarchy responses (100%, 18/18), 89% of … view at source ↗

**Figure 3.** Figure 3: Pattern prevalence by relational domain (% of responses per domain, n = 18 per domain). Narrative closure varied by domain, while normative advice and false epistemic signaling remained high across domains. First-person prompts more often elicited narrative alignment (13/36, 36%) than third-person prompts (7/36, 19%). Third-person prompts more often elicited narrative reversal (6/36, 17%) than first-perso… view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LLMs mostly turn ambiguous social situations into definite interpretations instead of leaving uncertainty open, but the 12.5% preservation rate rests on unvalidated subjective coding of the outputs.

read the letter

LLMs mostly turn ambiguous social situations into definite interpretations instead of leaving uncertainty open. That is the central observation from running GPT, Claude, and Gemini on 72 cases spanning early romance, teacher-student relations, workplaces, and friendships. Only nine responses kept the ambiguity intact; the rest followed one of four closure routes such as aligning with the narrator, flipping the story, or giving advice anyway while hedging the language. First-person accounts pulled more alignment, while third-person ones allowed more detached takes even on the same facts. Those patterns and the perspective split are the parts that feel fresh compared with earlier LLM evaluation work on social tasks. The paper gives concrete examples and a simple count that makes the tendency easy to see, which is useful for anyone thinking about LLMs as everyday social interpreters. The risk they flag—that models can make unresolved situations feel settled—is a practical design point worth keeping in mind. The main limitation is the classification step itself. The authors partition every output into genuine uncertainty or one of the closure pathways, yet the abstract and available details give no rubric, no inter-rater numbers, and no raw outputs or prompt templates. Because the boundary between hedged language that still picks a side and actual uncertainty is a judgment call, the 12.5 percent figure could shift with different criteria. The scenarios are also all hand-crafted, so it is unclear how far the pattern travels to real user questions. There are no equations or parameter fits here, just direct tallies from the model replies, and the citations mainly set up the background rather than overlap with the specific pathways. This is for HCI or AI-design readers who want evidence on how current models handle interpersonal ambiguity. Someone already working on uncertainty-aware systems or social AI evaluation could pull the examples and the perspective finding without much trouble. It deserves peer review. The question is real and the observation is worth checking with more transparent methods and broader testing; the current version is preliminary but not incoherent on its own terms.

Referee Report

1 major / 2 minor

Summary. The paper investigates how LLMs (GPT, Claude, Gemini) interpret 72 hand-crafted ambiguous social scenarios across four domains (early-stage romantic relationships, teacher-student dynamics, workplace hierarchies, ambiguous friendships). It reports that only 9 of 72 responses (12.5%) genuinely preserved uncertainty, with the remaining 87.5% exhibiting interpretive closure via four recurring pathways (narrative alignment, narrative reversal, normative advice under uncertainty, hedged language supporting a single conclusion). It further claims that first-person vs. third-person narrator perspective influences the specific closure pathway taken, even for comparable situations, and concludes that LLMs tend to resolve ambiguity into coherent, actionable narratives rather than maintaining unresolved states.

Significance. If the quantitative result and pathway taxonomy hold after methodological clarification, the work identifies a systematic tendency in current LLMs to produce premature interpretive closure in social sensemaking tasks. This has direct relevance for HCI applications in which users consult LLMs for interpersonal advice, and it frames a concrete design challenge for uncertainty-preserving social AI. The multi-model, multi-domain, and perspective-manipulation design is a strength that allows the authors to isolate narrator role as a modulating factor.

major comments (1)

[Results / Qualitative Analysis] The central claim that only 9/72 responses (12.5%) genuinely preserved uncertainty rests on a qualitative partition of model outputs into 'genuine uncertainty' versus one of four closure pathways. No coding rubric, decision criteria for borderline cases (e.g., distinguishing hedged language that still endorses a single conclusion from true uncertainty preservation), inter-rater reliability statistic, or raw prompt/output corpus is supplied. Because the scenarios are author-constructed and the distinction is interpretive, the headline percentage and the pathway taxonomy are sensitive to the authors' own framing; this single analytic step carries the entire empirical result.

minor comments (2)

[Abstract / Introduction] The abstract and introduction would benefit from an explicit statement of the exact prompt templates used for each model and a brief description of how the 72 scenarios were sampled or balanced across domains.
[Results] If tables or figures summarize the distribution of pathways by model or by perspective, they should include the raw counts alongside percentages to allow readers to assess the stability of the 12.5% figure.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important issues of transparency in our qualitative analysis. We address the comment in detail below and will revise the manuscript to incorporate the suggested clarifications.

read point-by-point responses

Referee: The central claim that only 9/72 responses (12.5%) genuinely preserved uncertainty rests on a qualitative partition of model outputs into 'genuine uncertainty' versus one of four closure pathways. No coding rubric, decision criteria for borderline cases (e.g., distinguishing hedged language that still endorses a single conclusion from true uncertainty preservation), inter-rater reliability statistic, or raw prompt/output corpus is supplied. Because the scenarios are author-constructed and the distinction is interpretive, the headline percentage and the pathway taxonomy are sensitive to the authors' own framing; this single analytic step carries the entire empirical result.

Authors: We agree that the qualitative coding process requires fuller documentation to substantiate the central claims. The four pathways emerged from an inductive review of all 72 outputs, in which we first identified recurring patterns of how models handled ambiguity (e.g., imposing a coherent narrative, reversing an implied expectation, offering normative guidance, or using hedges that nonetheless favored one reading). Genuine uncertainty was defined as responses that (a) explicitly acknowledged multiple viable interpretations, (b) refrained from endorsing any single narrative or course of action, and (c) did not supply hedged language that ultimately privileged one conclusion. Borderline hedged cases were classified as closure only when the hedging was followed by a dominant interpretation or recommendation; we will include a decision tree with concrete examples of each category and borderline resolution in the revised appendix. We will also release the full set of prompts and model outputs as supplementary material so that readers can independently apply the rubric. The scenarios were deliberately author-constructed to enable controlled comparison across domains and narrator perspectives; this design choice is a limitation we will discuss explicitly, while noting that it allowed isolation of the perspective effect reported in the paper. Because the coding was performed by the lead author with iterative discussion and consensus review by co-authors, we did not compute formal inter-rater reliability statistics. We will add a limitations paragraph acknowledging this and describing the internal validation steps taken. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical classification of external model outputs

full rationale

The paper conducts an empirical study by generating 72 hand-crafted scenarios, querying three LLMs, and manually partitioning the resulting texts into 'genuinely preserved uncertainty' versus four closure pathways. No equations, fitted parameters, self-citations, or derivations appear in the abstract or described structure. The 12.5% figure is produced by direct application of the authors' interpretive categories to fresh LLM outputs rather than by any reduction of those outputs to prior results or self-referential definitions. The classification step, while open to questions of reliability, does not constitute circularity under the specified patterns because it operates on independent data and does not rename or smuggle in its own inputs as predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen scenarios are representative of real ambiguous social situations and that the authors' manual classification reliably distinguishes genuine uncertainty preservation from hedged closure. No free parameters, mathematical axioms, or invented entities are introduced.

axioms (1)

domain assumption The 72 scenarios and response classifications accurately reflect typical LLM behavior on ambiguous social input.
Invoked when generalizing the 12.5% figure and pathway taxonomy beyond the tested cases.

pith-pipeline@v0.9.0 · 5538 in / 1249 out tokens · 53994 ms · 2026-05-08T02:27:58.217046+00:00 · methodology

discussion (0)

Reference graph

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