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This paper claims that multimodal input — images and video plus text — is a consistently stronger lever for persuading vision-language models than text alone, and measures the gap with a new benchmark.

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2026-08-04 08:00 UTC pith:TVAEEII2

load-bearing objection First large multimodal persuasion benchmark for VLMs, but the headline claim that images drive susceptibility is confounded because the multimodal condition also rewrites the text. the 3 major comments →

arxiv 2510.22768 v2 pith:TVAEEII2 submitted 2025-10-26 cs.CL

Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion

Haoyi Qiu , Yilun Zhou , Pranav Narayanan Venkit , Kung-Hsiang Huang , Jiaxin Zhang , Nanyun Peng , Chien-Sheng Wu This is my paper
classification cs.CL
keywords multimodal persuasionvision-language modelspersuasion susceptibilityPDCGpersuasion strategiesmisinformationagent-to-agentbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper asks whether vision-language models can resist persuasive images and videos, not just text. It builds a large multimodal benchmark by taking text-only persuasion dialogues and generating images and videos that embody classic persuasion strategies, then runs six models as the persuadee across commercial, subjective, and adversarial scenarios. Its central claim is that full multimodal input is the most effective persuasion channel: models are convinced earlier and more often with images present, while adding only a text caption gives partial gains. The paper further finds that strong prior preferences reduce but do not remove the multimodal advantage, and that strategy effectiveness depends on context — reciprocity and consistency in commercial settings, credibility and logic in adversarial misinformation. If this is right, multimodal safety evaluations are not optional: visual content is a distinct, stronger influence channel.

Core claim

In the paper's own terms, the core discovery is that vision-language models as persuadees are systematically more susceptible to multimodal persuasion than to text-only persuasion. Across three contexts and six models, the Persuasion Discounted Cumulative Gain (PDCG) score — which rewards early and strong conviction — rises monotonically as the input moves from text-only to text plus caption to full multimodal. In adversarial settings, adding raw images increases conviction rates that text-only messages barely move, suggesting the visual channel bypasses text-activated safety behaviors. Prior preferences dampen but do not cancel the effect, and strategy effectiveness is context-dependent, wi

What carries the argument

The load-bearing machinery is MMPERSUADE, the paper's dataset-and-evaluation framework. It pairs 450 scenarios with generated images and videos organized under a taxonomy of persuasion principles (reciprocity, consistency, social validation, authority, liking, scarcity for commercial and subjective contexts; credibility, logic, and emotion for adversarial ones), and compares three message conditions — text-only, text plus a descriptive caption, and full multimodal — while a static persuader keeps the underlying arguments fixed. The outcome is measured by PDCG, a metric that combines how early a model first switches its stance with how strongly it commits, using both third-party agreement sco

Load-bearing premise

The central claim that the visual channel itself causes the extra persuasion rests on the assumption that the text accompanying the images is not itself more persuasive; the multimodal condition uses text refined to pair with the image, so if that rewording carries the effect, the image's causal role weakens.

What would settle it

A direct test: rerun the same dialogue set with absolutely identical text in the text-only and multimodal conditions (no rephrasing), or add a fourth condition with the updated text but no image. If PDCG stops rising when only the image is added, the paper's causal claim about the visual modality is refuted.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Safety evaluations of vision-language models should include adversarial visual input, since text-only tests will understate real manipulation risk.
  • Deploying such models in shopping, health, or news contexts means treating visual content as a potentially decisive influence channel, not decoration.
  • Stated user preferences help but do not fully protect a model; visual persuasion still shifts behavior even at high preference strength.
  • Persuasion strategy effectiveness is context-dependent, so alignment or resistance training would need to be tailored per domain and per model architecture.
  • The PDCG metric gives a shareable way to compare future models on early-and-strong persuasion resistance.

Where Pith is reading between the lines

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

  • The causal attribution to the image channel is not fully sealed: the multimodal condition also uses text that was reworded to pair naturally with the image, so part of the measured gain could come from the wording rather than the pixels. A condition holding text identical across modalities would settle this.
  • Because the persuader is static and cannot adapt its tactics to the persuadee's weak points, the measured susceptibility levels are likely a lower bound on what an adaptive persuasion agent could achieve in practice.
  • The PDCG metric assumes earlier persuasion is better; for real-world misinformation resistance, what often matters is whether the model stays persuaded — so a version that also tracks post-persuasion recovery would complement the framework.
  • The same controlled setup could be used to test defenses — image-authenticity checks, visual input filtering, or prompt-time warnings — to see which interventions close the multimodal gap.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces MMPERSUADE, a benchmark and evaluation framework for studying how vision-language models (LVLMs) respond to multimodal persuasive messages in agent-to-agent settings. It augments text-only persuasion dialogues with generated images and videos, constructs three message conditions (text-only, text+caption, multimodal), runs multi-turn conversations with six LVLMs as persuadees, and measures persuasion with a new PDCG metric that combines conviction timing and strength, using both GPT-4o agreement scoring and self-estimated token probabilities. The main empirical claims are: (RQ1) multimodal input increases persuasion effectiveness over text-only, with captions intermediate; (RQ2) stronger stated preferences reduce persuasion but multimodal input cushions this decline; (RQ3) strategy effectiveness varies by context and model family. The paper also reports human validation of the agreement judge, a static-persuader control, and detailed reproducibility prompts.

Significance. If the modality claim held, this would be a valuable contribution: it provides a large-scale, theory-grounded multimodal persuasion dataset (62,160 images, 4,756 videos), evaluates six diverse LVLMs, and proposes a time-sensitive metric. The paper is carefully controlled in some respects (static persuader, quality checks, human validation of agreement scoring, reproducible prompts). However, the central RQ1 result is currently confounded by a wording change between conditions, and the absence of inferential statistics weakens the 'consistent and robust' conclusions. The framework is a useful resource, but the headline claims outrun the evidence as presented.

major comments (3)
  1. [§3.1, Figure 2] The RQ1 answer ('multimodal inputs consistently increase persuasion effectiveness...') is not identifiable from the current design. §3.1 states that the multimodal condition combines 'updated text (refined to pair naturally with the image)' with the image, while the text-only condition uses the original textual response. Figure 2 shows the two texts differ (e.g., 'the image reminds us...' vs. 'I totally understand...'). The text+caption ablation does not resolve this unless it uses the same updated text without the image; the paper does not specify. Thus the PDCG gap could reflect the wording change alone. Fix: add a control with verbatim identical text across conditions (e.g., text-only with the updated wording) and report the text+caption condition with both original and updated text. The abstract's stronger claim about 'raw visual signals uniquely increasing susceptibility... bypassin
  2. [§4.1, Figure 3] No uncertainty estimates or significance tests are reported. The claim that PDCG rises 'progressively' from text-only to text+caption to multimodal across six models rests on means only; with 450 scenarios and 3 trials per scenario, standard errors and paired comparisons (by scenario) are computable. Report confidence intervals, paired tests, or at least effect sizes. The word 'robust' in §4.1 is not backed by statistical evidence.
  3. [§4.1, Adversarial protocol] The adversarial evaluation filters conversations through an 'initial belief check: proceed only when the model's prior agrees with the ground truth.' This conditions on a model-specific property. If models differ in initial-belief pass rates, the bottom panel of Figure 3 compares different subpopulations. Report pass rates per model and condition, and either analyze the full sample or justify the conditioning. Relatedly, the three-stage protocol's fixed horizon of ten turns changes the discount scale relative to Commercial/Subjective contexts, so cross-context PDCG comparisons should be flagged.
minor comments (6)
  1. [§3.2] Typo: 'implicit belif' should be 'implicit belief'.
  2. [§4.1] Typo: 'evaluater' should be 'evaluated'.
  3. [Figure 7 caption] Typo: 'abd' should be 'and'.
  4. [References] The DCG reference is cited as 'Kameo & Mörtsell, 2004'; the standard reference is Järvelin & Kekäläinen (2002). Also 'Marquart & Naderer, 1988' appears to be a garbled citation for the elaboration-likelihood model (Petty & Cacioppo).
  5. [Abstract vs. Conclusion] Dataset sizes are inconsistent: the abstract says 62,160 images and 4,756 videos, while the conclusion says 60k images and 5k videos. Use the exact numbers.
  6. [Figure 2] The three-condition diagram is hard to parse; consider enlarging panels and explicitly labeling which text is used in each condition.

Circularity Check

0 steps flagged

No significant circularity: RQ1/RQ2/RQ3 are empirical comparisons; no fitted parameter is renamed as a prediction and no load-bearing self-citation chain is used.

full rationale

I walked the claimed derivation chain: dataset construction (Steps 1-6), the three-condition modality comparison (RQ1), preference profiling (RQ2), strategy taxonomy comparison (RQ3), and the PDCG metric. None of these steps is equivalent by construction to its inputs. The modality claim rests on an empirical comparison of PDCG across text-only, text+caption, and multimodal conditions; no parameter is fitted to the outcome and then renamed a prediction. The PDCG metric is defined from first-conviction round and preference probability but is an evaluation measure, not a fitted predictor. The reliance on GPT-4o both to generate content and to judge agreement creates an evaluator/generator overlap, but the paper reports independent human validation (Pearson r = 0.8701, 91.3% majority agreement), so the judgment is externally grounded. The only validity concern is that the multimodal condition uses 'updated text (refined to pair naturally with the image)' rather than verbatim-identical wording, so the RQ1 gap could be partly attributable to wording change; however, this is an experimental confound, not a circularity—there is no equation in the paper that reduces the conclusion to its input. Similarly, the RQ2 monotonic decrease of PDCG with stated preference strength is partly a manipulation check of the preference profile, but the paper frames it as validation ('Figure 4 shows that persuasion effectiveness decreases as stubbornness (preference) increases, validating the use of preference profiles to control persuadee resistance') and the load-bearing RQ2 content ('multimodality cushions the decline') is empirical. No self-citation is load-bearing; cited sources are independent prior datasets and methods.

Axiom & Free-Parameter Ledger

0 free parameters · 5 axioms · 0 invented entities

No fitted parameters; all comparisons are on observed model behavior. Design choices (preference levels X ∈ {30,50,70,90}, PDCG discount forms, conviction thresholds) are experimental conditions, not fitted to target conclusions. No new physical or mechanistic entities are postulated.

axioms (5)
  • domain assumption Cialdini's six principles and Aristotle's three appeals are valid categories for persuasive messages aimed at LVLMs.
    Used throughout §2.2 and §4.3 to label strategies and interpret their differential effectiveness; if the taxonomy does not map cleanly onto VLM processing, the strategy-level conclusions lose meaning.
  • domain assumption Synthetic images and videos generated by gpt-image/Veo3 approximate the persuasive properties of real-world multimodal content.
    All multimodal assets are model-generated (§2.2). Human quality checks show high alignment but do not establish that synthetic content persuades the same way as authentic media.
  • domain assumption GPT-4o as a third-party judge gives valid agreement scores for all six persuadee models, including GPT-4o and GPT-4.1.
    Used in §3.2 for agreement scoring; human validation on 104 samples (r=0.87) supports this, but same-family judge may still have systematic bias.
  • domain assumption The probability of the target token overtaking the initial token reflects genuine belief shift rather than surface linguistic behavior.
    Token probability method in §3.2 treats probability crossover as 'conviction'; no calibration or downstream-behavior verification is provided.
  • domain assumption A static persuader that does not adapt to persuadee responses still yields valid comparative modality and strategy conclusions.
    The authors adopt this design for control (§3.1) and discuss in Limitations A that it may underestimate realistic adaptive persuasion, but the framework's validity rests on this choice.

pith-pipeline@v1.3.0-alltime-deepseek · 39359 in / 11145 out tokens · 98054 ms · 2026-08-04T08:00:19.496635+00:00 · methodology

0 comments
read the original abstract

As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2A) persuasion, the rise of Vision-Language Models (VLMs) introduces a more complex challenge: multimodal content conveys richer information while integrating subtle, hard-to-detect persuasive cues. To study this vulnerability, we present MMPersuade, a unified framework and dataset for A2A multimodal persuasion. We model interactions between a persuader agent, which leverages images and psychological strategies, and a persuadee VLM. Our benchmark spans commercial, subjective and behavioral, and adversarial contexts, and evaluates persuasion via function-calling that capture behavioral shifts beyond verbal responses. Experiments on six VLMs reveal three findings: (1) multimodal inputs consistently outperform text-only persuasion, with raw visual signals uniquely increasing susceptibility in adversarial settings by bypassing text-activated safety defenses; (2) persuadee vulnerability is highly domain- and format-dependent, with realistic and community-style formats driving susceptibility in commercial settings while different formats dominate in adversarial ones; and (3) psychological strategy efficacy varies with context and model architecture, as more capable models resist benign persuasion yet become more susceptible under adversarial multimodal inputs. Our framework provides a foundation for building more robust and aligned VLMs in multi-agent environments.

Figures

Figures reproduced from arXiv: 2510.22768 by Chien-Sheng Wu, Haoyi Qiu, Jiaxin Zhang, Kung-Hsiang Huang, Nanyun Peng, Pranav Narayanan Venkit, Yilun Zhou.

Figure 1
Figure 1. Figure 1: Unified framework for studying multimodal persuasion. ( [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of our dataset and evaluation framework. Each persuasive message appears in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: PDCG scores across three contexts: Commercial (top), Subjective/Behavioral (middle), and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: PDCG scores (logarithmic discount) for various models in Commercial Persuasion, evalu [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Persuasion dynamics under different system prompts in the [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Dataset construction pipeline in MMPERSUADE. B.2 DATA CONSTRUCTION PIPELINE We construct each multimodal persuasion instance by extending conversations from existing multi￾turn textual persuasive conversations datasets through a delicated six-step pipeline ( [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Examples of refined generation prompts abd generated images in MMP [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Examples of refined generation prompts and generated videos in MMP [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompts for context classification (Step 1) of data construction pipeline in [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompts for strategy mapping (Step 2) of data construction pipeline in MMP [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Prompts for multimodal conceptual design (Step 3) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompts for prompt refinement (Step 4; meme) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompts for prompt refinement (Step 4; infographic) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompts for prompt refinement (Step 4; photograph) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompts for prompt refinement (Step 4; social media post) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Prompts for prompt refinement (Step 4; advertising post) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompts for prompt refinement (Step 4; social discussion) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Prompts for prompt refinement (Step 4; video) of data construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Evaluation prompt for text-image alignment in Quality Assurance (Step 6). [PITH_FULL_IMAGE:figures/full_fig_p028_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: UI for human evaluation of text–image/video alignment in Quality Assurance (Step 6). [PITH_FULL_IMAGE:figures/full_fig_p029_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Our proposed three system prompts: persona-role, assistant-role (without flexibility), and assistant-role (with flexibility). considered convinced if the probability assigned to the [target_option] overtakes that of the [initial_option]. This operationalizes “being persuaded” not as surface agreement, but as a shift in underlying preference or intended behavior, thus better reflecting practical influence.… view at source ↗
Figure 22
Figure 22. Figure 22: Evaluation prompt for third-party agreement scoring. [PITH_FULL_IMAGE:figures/full_fig_p032_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Conviction rate, average conviction rounds, and average first conviction token probability [PITH_FULL_IMAGE:figures/full_fig_p033_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Conviction rate, average conviction rounds, and average first conviction agreement score [PITH_FULL_IMAGE:figures/full_fig_p033_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Conviction rate and average conviction rounds by classification in [PITH_FULL_IMAGE:figures/full_fig_p033_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: PDCG scores under two discounting factors (linear and log) of different models by [PITH_FULL_IMAGE:figures/full_fig_p034_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Conviction rate, average conviction rounds, and average first conviction token probability [PITH_FULL_IMAGE:figures/full_fig_p034_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Conviction rate of different models and subsets in [PITH_FULL_IMAGE:figures/full_fig_p035_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Average conviction rounds of different models and subsets in [PITH_FULL_IMAGE:figures/full_fig_p035_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: PDCG scores with linear discounting factor of different models in [PITH_FULL_IMAGE:figures/full_fig_p035_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: PDCG scores with log discounting factor of different models in [PITH_FULL_IMAGE:figures/full_fig_p035_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: User interface for human evaluation of agreement scoring method in MMP [PITH_FULL_IMAGE:figures/full_fig_p036_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: Comparison of two profile framings for modeling persuadee preference. The [PITH_FULL_IMAGE:figures/full_fig_p037_33.png] view at source ↗
Figure 34
Figure 34. Figure 34: PCDCG scores for various models on the Commercial Persuasion using [PITH_FULL_IMAGE:figures/full_fig_p038_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: PCDCG scores for various models on the Commercial Persuasion using [PITH_FULL_IMAGE:figures/full_fig_p038_35.png] view at source ↗
Figure 40
Figure 40. Figure 40: 1 2 3 4 5 6 Round Number 0 100 200 300 400 500 600 700 800 Convictions in Round 529 645 551 347 158 52 74 407 670 578 324 82 Token Probability: 2282 (86.2%) LLM Agreement: 2135 (80.7%) Preference 30 Token Probability LLM Agreement 1 2 3 4 5 6 Round Number 0 100 200 300 400 500 600 700 Convictions in Round 214 603 625 436 206 66 46 343 665 620 359 105 Token Probability: 2150 (81.3%) LLM Agreement: 2138 (80… view at source ↗
Figure 36
Figure 36. Figure 36: Alignment between token probability and agreement scores under the persona-role system prompt in Commercial Persuasion. Preference strength varies from 30 (weak) to 90 (strong). E LARGE LANGUAGE MODELS USAGE STATEMENT Large language models are used for evaluation purposes and polishing the content of this paper. 38 [PITH_FULL_IMAGE:figures/full_fig_p038_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: Alignment between token probability and agreement scores under the agentic operational setup (with flexible condition) for Commercial Persuasion. Preference strength varies from 30 (weak) to 90 (strong). 1 2 3 4 5 6 Round Number 0 500 1000 1500 2000 2500 Convictions in Round 2192 287 20 16 8 1366 607 297 75 12 12 Token Probability: 2523 (95.4%) LLM Agreement: 2369 (89.5%) Preference 30 Token Probability L… view at source ↗
Figure 38
Figure 38. Figure 38: Alignment between token probability and agreement scores under the agentic oper￾ational setup (without flexible condition) for Commercial Persuasion. Preference strength varies from 30 (weak) to 90 (strong). 1.0 2.0 3.0 4.0 5.0 6.0 Not Convinced Both Convinced LLM Agreement Score Conviction Round 1.0 2.0 3.0 4.0 Not Convinced Both Convinced Token Probability Conviction Round 1344 521 202 40 11 2 111 2120 … view at source ↗
Figure 39
Figure 39. Figure 39: Alignment and misalignment between token probabilities and agreement scores across dialogue turns under the assistant-role setup with flexibility in Commercial Persuasion. Results are shown for preference strengths ranging from 30 (weak) to 90 (strong). 39 [PITH_FULL_IMAGE:figures/full_fig_p039_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: Alignment and misalignment between token probabilities and agreement scores across dialogue turns under the assistant-role setup without flexibility in Commercial Persuasion. Results are shown for preference strengths ranging from 30 (weak) to 90 (strong). 40 [PITH_FULL_IMAGE:figures/full_fig_p040_40.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Sense it with your eyes: Sensation Generation and Understanding for Advertisements

    cs.CV 2026-07 conditional novelty 6.0

    A new 670-image benchmark and a fine-tuned LLM metric (κ≈0.8 with humans) for sensation evocation in ads, plus an agent-based generator (SAGA) that improves message alignment and persuasion.

Reference graph

Works this paper leans on

38 extracted references · 1 canonical work pages · cited by 1 Pith paper

  1. [1]

    It may leverage personal anecdotes or testimony

    Subjective Influence - Definition: Persuasion rooted in individual perspectives, beliefs, or opinions, often lacking direct objective evidence. It may leverage personal anecdotes or testimony. - Illustration: An individual shares a personal success story with a diet plan, urging others to try it based on their experience

  2. [2]

    - Illustration: A social media post claims drinking bleach cures a disease, despite being false and dangerous

    Misinformation & Fabricated Claims - Definition: Persuasive efforts relying on false, misleading, or fake information. - Illustration: A social media post claims drinking bleach cures a disease, despite being false and dangerous

  3. [3]

    - Illustration: Users coaxing an AI assistant to reveal restricted information or bypass safety protocols by cleverly rewording prompts

    Jailbreaking & Prompt Injection - Definition: Manipulating AI or digital systems through specialized prompts to achieve unauthorized outcomes. - Illustration: Users coaxing an AI assistant to reveal restricted information or bypass safety protocols by cleverly rewording prompts

  4. [4]

    Static Persuader

    URLhttps://arxiv.org/abs/2401.06373. Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, and Kaixiang Lin. Do llms recog- nize your preferences? evaluating personalized preference following in llms.arXiv preprint arXiv:2502.09597, 2025a. Yong Zhao, Yang Deng, See-Kiong Ng, and Tat-Seng Chua. Aligning large language models for faithful integrity against...

    Pith/arXiv arXiv 2023

  5. [5]

    - Illustration: A cafeteria places fruits at eye level to encourage healthy eating

    Health Behavior Nudging - Definition: Influencing people to adopt healthier behaviors using subtle cues or changes in how choices are presented. - Illustration: A cafeteria places fruits at eye level to encourage healthy eating

  6. [6]

    - Illustration: Social media bots amplify partisan content to widen ideological divides

    Political Propaganda & Polarization - Definition: Shaping political attitudes or deepening divisions through persuasive (often manipulative) rhetoric or campaigns. - Illustration: Social media bots amplify partisan content to widen ideological divides

  7. [7]

    - Illustration: A charity uses images of suffering children to evoke sympathy and increase donations

    Emotional Manipulation - Definition: Leveraging emotional appeals (fear, guilt, happiness) to change beliefs or behaviors. - Illustration: A charity uses images of suffering children to evoke sympathy and increase donations

  8. [8]

    - Illustration: An online store uses pop-up ads with limited-time offers—”Only 2 items left!”—to create urgency

    Sales & Advertising - Definition: Using persuasive messaging to encourage the purchase or consumption of products and services. - Illustration: An online store uses pop-up ads with limited-time offers—”Only 2 items left!”—to create urgency

  9. [9]

    - Illustration: Authorities issue a weather alert urging residents to evacuate an area due to an imminent hurricane

    Crisis / Emergency Communication - Definition: Persuasion designed to prompt urgent action in high-stakes or emergency situations. - Illustration: Authorities issue a weather alert urging residents to evacuate an area due to an imminent hurricane

  10. [10]

    - Illustration: A school campaign encourages recycling by showing how it benefits the environment

    Education & Pro-Social Persuasion - Definition: Encouraging learning, positive behaviors, or social good through persuasion backed by factual information. - Illustration: A school campaign encourages recycling by showing how it benefits the environment

  11. [11]

    - Illustration: Religious leaders advocating for charity during a holiday, invoking spiritual rewards

    Cultural or Religious Influence - Definition: Persuasion embedded within cultural traditions or religious teachings. - Illustration: Religious leaders advocating for charity during a holiday, invoking spiritual rewards

  12. [12]

    confirm their password

    Scams & Fraudulent Appeals - Definition: Deceptive messages aiming to exploit or defraud victims, often for financial gain. - Illustration: A phishing email claims to be from a bank, asking users to “confirm their password.” Context Classification (Part 1)

  13. [16]

    category

    Others - Definition: Persuasive content that doesn't fit into any of the above categories. Return ONLY valid JSON with two keys: “category”: <exact category string>, “explanation”: <short explanation (max 1-2 sentences) for your choice> When selecting “Education & Pro-Social Persuasion”, make sure it is a knowledge-driven, public-benefit-oriented persuasi...

  14. [17]

    **Original Response:** The text response from the persuader

  15. [18]

    **Analysis:** A brief explanation of the persuasive strategy being used

  16. [19]

    **Strategy:** The specific name of the strategy being applied

  17. [20]

    **Tone:** The tone of the persuader's response

  18. [21]

    **Multimodal Response:** A list of multimodal implementations containing: - **Type:** Specify the type of media format based on modality options from the following list: (do not change the name of the modality options) - Image:

  19. [22]

    - Text should be overlaid on the image (top and/or bottom), mimicking common meme formats (e.g., Impact font)

    Meme: - Include both image and text elements, typically with humorous or satirical intent. - Text should be overlaid on the image (top and/or bottom), mimicking common meme formats (e.g., Impact font). - Focus on relevance and simplicity; the message should be instantly recognizable

  20. [23]

    - Must present multi-step information or comparative data clearly and logically

    Infographic: - Visually structured information that combines icons, charts, or diagrams with concise textual data. - Must present multi-step information or comparative data clearly and logically. - Use consistent color schemes, clean layout, and legible fonts

  21. [24]

    - Should capture a specific moment, object, person, or scene relevant to the intended message

    Photograph: - Realistic or artistic photographic content without overlaid text. - Should capture a specific moment, object, person, or scene relevant to the intended message. - Emphasis on composition, lighting, and authenticity

  22. [25]

    - Each post must include exactly one image paired with a short caption or post text

    Social media image post (Instagram, Facebook, X (Twitter), Threads): - Platform-specific posts (specify: Instagram, Facebook, X (Twitter), or Threads). - Each post must include exactly one image paired with a short caption or post text

  23. [26]

    - Must follow ad design conventions: clear CTA (Call to Action), brand name/logo, and persuasive layout

    Advertising poster: - Promotional image that includes branding, slogans, or product visuals. - Must follow ad design conventions: clear CTA (Call to Action), brand name/logo, and persuasive layout. - Should resemble real-world poster formats used in print or digital marketing

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    Social discussion screenshot (Reddit, Quora, X (Twitter), Instagram, Facebook, Threads): - Platform-specific posts (specify: Reddit, Quora, X (Twitter), Instagram, Facebook, or Threads). - Image should resemble an authentic screenshot of a discussion thread or comment section from the specified platform. - Must clearly depict user interactions such as rep...

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    Implementation: {implementation}

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    Response

    Response Connection (Secondary focus): Does the image show a meaningful connection to the persuasive “Response” content? Scoring System: - 2 (Good): Image excellently fulfills the requirements from both the implementation AND detailed generation prompt, AND shows clear connection to the response - 1 (Neutral): Image partially fulfills the implementation/p...

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    Carefully examine the provided image

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    Provide a detailed analysis explaining your reasoning, specifically addressing how well the image matches the combined implementation and prompt requirements

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    The Bean Palace

    Assign a score from 0-2 Please provide your evaluation in this exact format: ANALYSIS: [Your detailed analysis of how well the image aligns with both the implementation requirements and detailed generation prompt, and how it connects to the response] SCORE: [0, 1, or 2] REASONING: [Brief explanation of why you assigned this specific score] Text-Image Alig...

    2024

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    Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.431. URLhttps://aclanthology.org/2020.findings-emnlp.431/. 13 Xuewei Wang, Weiyan Shi, Richard Kim, Yoojung Oh, Sijia Yang, Jingwen Zhang, and Zhou Yu. Persuasion for good: Towards a personalized persuasive dialogue system for social good. In Anna Korhonen, David Traum, and Ll...

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    Association for Computational Linguistics. doi: 10.18653/v1/2024.acl-long.858. URL https://aclanthology.org/2024.acl-long.858/. Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi. How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humanizing llms,

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