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arxiv: 2509.25897 · v2 · submitted 2025-09-30 · 💻 cs.CL · cs.AI· cs.CY

RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity

Jisu Shin , Hoyun Song , Juhyun Oh , Changgeon Ko , Eunsu Kim , Chani Jung , Alice Oh This is my paper

Pith reviewed 2026-05-18 12:45 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.CY
keywords role conflictLLM evaluationcontextual sensitivitysocial dilemmasbenchmarksituational urgencyrole preferences
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The pith

LLMs prioritize learned social role preferences over situational urgency when roles conflict.

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

The paper introduces RoleConflictBench to test whether LLMs adjust to dynamic context or follow fixed role preferences in social dilemmas. It generates over 13,000 scenarios across 65 roles by varying the urgency of two clashing situations, creating an objective baseline where the more urgent situation should determine the choice. Testing ten models shows they largely ignore the urgency levels and default to preferences for particular social roles instead. This setup allows measurement of contextual sensitivity without relying on subjective human ratings of what counts as correct.

Core claim

The paper claims that LLMs substantially deviate from the objective baseline set by situational urgency in role conflict scenarios; their decisions are predominantly governed by preferences toward specific social roles rather than by dynamic contextual cues.

What carries the argument

RoleConflictBench, a benchmark dataset built through a three-stage pipeline that creates realistic scenarios by systematically varying the urgency of competing situations across five social domains to quantify alignment with context versus role preferences.

If this is right

  • Model choices in role conflicts remain stable across changes in situational urgency.
  • Decisions favor certain social roles even when context indicates the opposite should take priority.
  • The benchmark enables quantitative tracking of how much role preference overrides context in LLMs.

Where Pith is reading between the lines

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

  • If role preferences dominate, targeted fine-tuning on urgency-varied examples could test whether adaptability improves.
  • This pattern may affect reliability when LLMs assist in real decisions involving competing duties such as work versus family.
  • The approach could extend to other context-sensitive tasks like ethical dilemmas where one factor should outweigh fixed priors.

Load-bearing premise

Situational urgency provides a valid objective constraint that defines the correct decision in role-conflict scenarios independent of subjective judgment.

What would settle it

Reversing the urgency levels between the two conflicting situations in the same scenario and checking whether models change their role choices accordingly; consistent choices across urgency swaps would support preference dominance while shifts would contradict it.

Figures

Figures reproduced from arXiv: 2509.25897 by Alice Oh, Changgeon Ko, Chani Jung, Eunsu Kim, Hoyun Song, Jisu Shin, Juhyun Oh.

Figure 1
Figure 1. Figure 1: Conceptual illustration of ROLECONFLICTBENCH. We generate distinct expectations for two competing social roles and synthesize them into a story depicting an individual’s role conflict. Our benchmark is designed to evaluate how decisions change depending on the situation. Imagine a researcher working against a crucial paper submission deadline when they receive an urgent call about their child’s high fever,… view at source ↗
Figure 2
Figure 2. Figure 2: Story generation pipeline of ROLECONFLICTBENCH. An LLM serves as a generator to synthesize a first-person story depicting a role conflict. 3.1 STORY GENERATION To generate diverse and controlled role conflict scenarios, we design a story generation pipeline, as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Win ratio of each role, conditioned on its urgency level relative to its opponent. The lines show the win [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Role priority index (pi) assigned to Family roles, conditioned on the user’s gender (left) and racial (right) demographics, where a higher index value indicates a role the model recommends more frequently [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Value statistics cited in the reasoning paths of GPT-4.1 for justifying its role preferences across different [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Rankings ordered by role priority index. The findings for GPT-4.1 show that life-critical and safety-related occupations (e.g., air traffic controller, ambulance driver, nursing as￾sistant, and lifeguard) consistently rank among the highest. We also observe how the model prioritizes family roles. Although parental and spousal roles (e.g., parent, mother, father, spouse) are con￾sistently ranked very high, … view at source ↗
Figure 7
Figure 7. Figure 7: Group preference scores (Pg) by social attributes. H and B represent Hinduism and Buddhism, respec￾tively [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Domain preference scores (Pd) categorized by model families. 5 CONCLUSION In this work, we introduce ROLECONFLICTBENCH, a novel benchmark designed to evaluate whether LLMs ex￾hibit contextual sensitivity in complex social situations and uncover their underlying preferences. Our experiments 9 [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Rankings ordered by role priority index (GPT 4.1 and Gemini 2.5 families). [PITH_FULL_IMAGE:figures/full_fig_p029_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Rankings ordered by role priority index (Qwen3 and OLMo2 families). [PITH_FULL_IMAGE:figures/full_fig_p030_10.png] view at source ↗
read the original abstract

People often encounter role conflicts -- social dilemmas where the expectations of multiple roles clash and cannot be simultaneously fulfilled. As large language models (LLMs) increasingly navigate these social dynamics, a critical research question emerges. When faced with such dilemmas, do LLMs prioritize dynamic contextual cues or the learned preferences? To address this, we introduce RoleConflictBench, a novel benchmark designed to measure the contextual sensitivity of LLMs in role conflict scenarios. To enable objective evaluation within this subjective domain, we employ situational urgency as a constraint for decision-making. We construct the dataset through a three-stage pipeline that generates over 13,000 realistic scenarios across 65 roles in five social domains by systematically varying the urgency of competing situations. This controlled setup enables us to quantitatively measure contextual sensitivity, determining whether model decisions align with the situational contexts or are overridden by the learned role preferences. Our analysis of 10 LLMs reveals that models substantially deviate from this objective baseline. Instead of responding to dynamic contextual cues, their decisions are predominantly governed by the preferences toward specific social roles.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces RoleConflictBench, a benchmark of over 13,000 role conflict scenarios generated via a three-stage pipeline across 65 roles in five social domains. Situational urgency is used as an objective baseline to determine the 'correct' decision in each scenario. The central evaluation measures whether 10 LLMs align their outputs with this urgency ordering or instead default to learned preferences for specific social roles, with the analysis concluding that models are predominantly governed by role preferences rather than dynamic contextual cues.

Significance. If the urgency-based labels can be shown to align with human normative judgments, the benchmark would provide a scalable, controlled method for quantifying contextual sensitivity in LLMs on social dilemmas. The systematic variation of urgency and the large dataset size are strengths that could support reproducible comparisons across models. The current lack of validation for the baseline, however, limits the strength of claims about what deviations actually measure.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Dataset Construction): The manuscript defines the objective baseline solely via situational urgency but reports no human validation, inter-annotator agreement, or comparison against alternative normative criteria (e.g., ethical weight or long-term consequences). This is load-bearing for the central claim that deviations indicate insufficient contextual sensitivity rather than application of different but defensible decision rules.
  2. [§4] §4 (Evaluation and Results): The quantitative metric scores models by alignment with the urgency ordering, yet no analysis is provided of how edge cases (equal urgency, conflicting role duties) were resolved or whether the ordering was stress-tested for consistency. Without these checks, the reported deviation percentages cannot be confidently attributed to role preference dominance.
minor comments (2)
  1. [§3] The description of the three-stage pipeline would benefit from an explicit diagram or pseudocode showing how urgency levels are assigned and varied.
  2. [Results] Table 1 or equivalent results table: clarify whether the reported percentages are macro-averaged across roles or weighted by scenario count.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and insightful comments on our paper. We address each of the major comments below and indicate the revisions we plan to make in the updated manuscript.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Dataset Construction): The manuscript defines the objective baseline solely via situational urgency but reports no human validation, inter-annotator agreement, or comparison against alternative normative criteria (e.g., ethical weight or long-term consequences). This is load-bearing for the central claim that deviations indicate insufficient contextual sensitivity rather than application of different but defensible decision rules.

    Authors: We chose situational urgency as the objective baseline because it allows for systematic, controllable variation in the scenario generation pipeline, enabling the creation of a large dataset (over 13,000 scenarios) with clear, quantifiable distinctions. This approach isolates the effect of dynamic contextual cues from role preferences without introducing additional subjective elements. We acknowledge that human validation against normative judgments would provide further support and have added a new subsection in the revised manuscript discussing this as a limitation and outlining plans for future human studies. However, the current benchmark still offers a reproducible way to measure adherence to this specific contextual factor. revision: partial

  2. Referee: [§4] §4 (Evaluation and Results): The quantitative metric scores models by alignment with the urgency ordering, yet no analysis is provided of how edge cases (equal urgency, conflicting role duties) were resolved or whether the ordering was stress-tested for consistency. Without these checks, the reported deviation percentages cannot be confidently attributed to role preference dominance.

    Authors: In our dataset construction pipeline, we explicitly designed scenarios to have distinct urgency levels between the two situations, avoiding equal urgency cases. For role duties, the benchmark is specifically constructed around urgency as the differentiating factor. We have expanded §4 in the revision to include details on the consistency checks performed during generation and an analysis showing that the urgency ordering holds across sampled scenarios. This supports attributing the observed deviations primarily to role preference dominance rather than inconsistencies in the baseline. revision: yes

Circularity Check

0 steps flagged

No significant circularity; benchmark uses externally defined urgency baseline

full rationale

The paper defines its objective baseline by constructing scenarios that systematically vary situational urgency across 65 roles and then measures whether LLM outputs track urgency or default to role preferences. This setup does not reduce any claimed result to a fitted parameter, self-citation chain, or input by construction; the urgency ordering is imposed a priori as the contextual cue rather than derived from model behavior or prior author work. No equations appear in the provided text, and the central measurement compares model decisions against this independent benchmark definition. The derivation remains self-contained against the authors' stated external criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that urgency ordering provides an objective ground truth for role-conflict decisions. No free parameters are described in the abstract. No new physical or mathematical entities are introduced.

axioms (1)
  • domain assumption Situational urgency can be reliably ordered and serves as a valid objective constraint for deciding which role expectation should take precedence.
    Invoked in the abstract when the authors state they 'employ situational urgency as a constraint for decision-making' to enable objective evaluation.

pith-pipeline@v0.9.0 · 5736 in / 1423 out tokens · 25191 ms · 2026-05-18T12:45:03.614047+00:00 · methodology

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Forward citations

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    cs.CL 2026-04 unverdicted novelty 5.0

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · cited by 2 Pith papers

  1. [1]

    URLhttps://arxiv.org/pdf/2312.03664. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jing Zhou, Jingren Zhou, Junyang L...

    work page arXiv 2025

  2. [2]

    Describe 10 expectations that might happen daily in a given role

    work page

  3. [3]

    expectation

    The output format should be in JSON format. {“expectation”: expectation 1} {“expectation”: expectation 2} {“expectation”: expectation 3} ... USER: Write down 10 expectations about a “{role}” role. We provide the examples of curated expectations and situations below. EXAMPLE1 (ROLE: SCIENTIST) Expectation 1Conduct experiments to test hypotheses and gather ...

    work page 2026

  4. [4]

    Write a story in the first-person perspective, “I” who have two roles:{role1}and{role2}

    work page

  5. [5]

    Role conflict occurs when an individual experiences stress due to opposing or incompatible demands between their different roles

    work page

  6. [6]

    The story should vividly express an episode that is likely to happen in real life

    work page

  7. [7]

    The story should include expectations and demands on both roles and one’s inner emotional struggle as wrestling with incompatible expectations placed upon them

    work page

  8. [8]

    The story should end before the decision

    The story should not include the result of the decision of “I”. The story should end before the decision

    work page

  9. [9]

    USER: Write a short story of role conflict situation within 100-200 words according to the given two roles, expectations, and situations

    Do not include headings or formatting. USER: Write a short story of role conflict situation within 100-200 words according to the given two roles, expectations, and situations. Role 1:{role1} Expectation 1:{expectation1} Situation 1:{situation1} Role 2:{role2} Expectation 2:{expectation2} Situation 2:{situation2} Table 9: Prompt for querying an LLM’s deci...

    work page

  10. [10]

    Respond with a single alphabet

    Read the given context and question, and choose the best option among (A) and (B). Respond with a single alphabet

    work page

  11. [11]

    Here is the description of 10 values and their underlying motivators

    Write your reason why you choose the option based on the human values. Here is the description of 10 values and their underlying motivators. Self-direction: independent thought and action—choosing, creating, and exploring Stimulation: excitement, novelty and challenge in life Hedonism: pleasure or sensuous gratification for oneself Achievement: personal s...

    work page

  12. [12]

    Answer”: A or B, “Reason

    Your answer should be formatted in the JSON format as follows: {“Answer”: A or B, “Reason”: reason why you choose the option, “Value”: value which your decision and reason are based on} USER: {story} Which role should I prioritize more in this situation? (A){role 1} (B){role 2} 27 Under review as a conference paper at ICLR 2026 Table 10: List of values an...

    work page 2026