SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations

Aditya Kumar Guru; Danush Khanna; Kripabandhu Ghosh; Pratinav Seth; Sandeep Chaurasia; Siddharth Shukla; Sidhaarth Sredharan Murali; Tanuj Tyagi

REVIEW 5 major objections 3 minor 27 references

SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations

T0 review · 5 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that an introspection-based two-stage prompt, SELF-PERCEPT, improves LLM detection of mental manipulation in multi-person conversations beyond standard prompting.

desk verdict A genuinely new multi-party manipulation dataset and a plausible prompting recipe, but unstable gold labels and a missing baseline undercut the reported gains. read the letter →

arxiv 2505.20679 v1 pith:D6EWYGTO submitted 2025-05-27 cs.CL cs.HCcs.LG

Danush Khanna , Pratinav Seth , Sidhaarth Sredharan Murali , Aditya Kumar Guru , Siddharth Shukla , Tanuj Tyagi , Sandeep Chaurasia , Kripabandhu Ghosh This is my paper · ORCID

classification cs.CLcs.HCcs.LG

keywords mentalmanipulationdetectionmulti-personconversationmulti-turndialogueSELF-PERCEPTpromptingself-perceptiontheorylargelanguagemodelsMultiManipdatasetmulti-labelclassification

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that a two-stage prompting framework called SELF-PERCEPT can make large language models detect mental manipulation in multi-person conversations more reliably than standard prompting. To test this, the authors build MultiManip, 220 three-person, multi-turn dialogues from reality television, balanced between manipulative and non-manipulative cases and labeled with 11 manipulation techniques. They report that SELF-PERCEPT, which makes the model first list observed behaviors and then infer attitudes, achieves the highest accuracy and F1 on both GPT-4o and Llama-3.1-8B, outperforming zero-shot, few-shot, and chain-of-thought prompting. If the paper is right, a prompt-only introspection step can sharpen detection of subtle social-psychological abuse without any fine-tuning.

What carries the argument

The core mechanism is SELF-PERCEPT, a two-stage prompting template. Stage 1, 'self-percept,' instructs the model to list observed behaviors, verbal and non-verbal cues, inconsistencies, and persuasive techniques for each participant. Stage 2, 'self-inference,' feeds that observation list back to the model and asks whether manipulation is present and which of 11 predefined techniques apply. The paper treats this as a textual analogue of Self-Perception Theory, where attitudes are inferred from behavior, and argues that the intermediate behavioral inventory is what gives the framework its edge over chain-of-thought reasoning. A second load-bearing object is MultiManip, the balanced 220-dialogue benchmark built from reality-television transcripts.

What would settle it

Re-annotate the 220 MultiManip dialogues with expert annotators and rerun all four prompting methods; if SELF-PERCEPT's F1 advantage over chain-of-thought shrinks or reverses under expert labels, the central claim fails. A cheaper check is an ablation that feeds the raw dialogue directly into the Stage-2 prompt: if performance does not drop, the behavioral-observation stage is not what is driving the gain.

Watch

Extended reading notes

Core claim

The central claim is that reading a conversation twice works: asking the model to catalog each participant's verbal and non-verbal cues before asking it to judge manipulation yields better multi-label detection than direct reasoning. On the new MultiManip dataset, SELF-PERCEPT gives GPT-4o an accuracy of 0.42 and macro-F1 of 0.37, versus 0.34 F1 for chain-of-thought, and gives Llama-3.1-8B an accuracy of 0.30 and F1 of 0.34, versus 0.10 for chain-of-thought. On the existing MentalManip test set, the same prompt reaches 0.45 accuracy and 0.47 F1 with GPT-4o and 0.23 accuracy and 0.19 F1 with Llama-3.1-8B. The paper presents these results as evidence that a behavior-observation stage reduces false positives by contextualizing intent.

Load-bearing premise

The load-bearing premise is that MultiManip's gold labels measure real manipulation rather than the extraction model's notion of it: labels were pre-selected by Llama-3.1-70B, five annotators reached only moderate agreement (Fleiss' kappa 0.429), and ties were resolved by majority vote.

Editorial extensions

If this is right

SELF-PERCEPT reports the best accuracy and macro-F1 among four prompting methods on MultiManip for both GPT-4o and Llama-3.1-8B.
The gain is mostly in precision, meaning fewer ordinary conversations get flagged as manipulative.
The same prompt transfers to the dyadic MentalManip test set, so it is not tied to three-person reality-show formats.
Because no fine-tuning is involved, any instruction-following language model can adopt the framework directly.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the mechanism is explicit behavioral cue extraction, the same two-stage template could transfer to other social-attribution tasks such as deception, gaslighting, or coercion detection.
With only 220 samples and moderate annotator agreement, the reported F1 gaps may be close to the label-noise ceiling; a larger expert-annotated benchmark is needed to confirm the ordering.
The structured Stage-1 outputs could serve as weak supervision for fine-tuning smaller models, reducing the dataset-size limitation the paper notes.
The method's success with Llama-3.1-8B suggests that the improvement is instructional rather than a quirk of a single model family.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First, the punchline: the paper introduces a genuinely new dataset and a plausible prompting recipe, but the gold labels are not stable enough to carry the reported F1 gains. Most fine-grained manipulation classes have a median annotator agreement of 2 out of 5, and the paper's own tie-breaking rule lets a label into the gold set with only those two votes. With GPT-4o's gain over CoT sitting at +3 F1 points, noise of that size can flip the ranking.

What is new and worth credit: MultiManip is the first multi-person, multi-turn manipulation benchmark drawn from reality-show transcripts, with 11 manipulation types and a balanced split. The authors release code and data, and they are transparent about the dataset's small size and moderate Fleiss' kappa. SELF-PERCEPT is a modest but sensible extension of chain-of-thought prompting: Stage 1 extracts observable verbal and non-verbal behavior, Stage 2 infers manipulation from those observations. It is cheap, easy to reproduce, and the idea is clearly motivated.

The soft spots are substantive. The annotation instability is the load-bearing one: Table 6 reports median agreement of 2/5 for 8 of the 11 fine-grained classes, and Appendix A.4 states that tied labels are all retained. So a label can become "gold" with support from only two annotators, which is not a majority in a 5-person panel. This is exactly the kind of noise that can shift macro-F1 by a few points. There are also no confidence intervals, no multiple seeds, and no comparison with intent-aware prompting (Ma et al., 2024), which is the closest prior method and is cited in the paper. The pre-filtering of candidate dialogues by Llama-3.1-70B also narrows the space of conversations the benchmark actually tests. The authors acknowledge the size limitation, but the label-reliability issue is deeper than sample size.

Who is this for? Researchers working on manipulation detection or on structured prompting for LLMs will find the dataset and prompt templates useful, but they should treat the evaluation numbers as provisional. The paper deserves a real peer review, not a desk reject: the dataset is novel, the code is released, and the prompt idea is worth testing on stronger labels. My recommendation is major revision with at least one clean re-annotation pass (or a consensus-only subset), added variance estimation, and the missing baseline.

Referee Report

5 major / 3 minor

Summary. The paper introduces MultiManip, a new dataset of 220 multi-turn, multi-person dialogues extracted from Survivor transcripts, balanced between manipulative and non-manipulative conversations and annotated with 11 manipulation techniques. The authors propose SELF-PERCEPT, a two-stage prompting framework inspired by Self-Perception Theory, in which a model first observes and lists verbal/non-verbal behaviors and then infers whether manipulation is present and which techniques are used. They evaluate GPT-4o and Llama-3.1-8B with zero-shot, few-shot, chain-of-thought, and SELF-PERCEPT prompting on MultiManip and on the existing MentalManip test set, reporting that SELF-PERCEPT achieves the highest accuracy and macro-F1 in most settings.

Significance. The paper addresses an understudied and socially important task: detecting subtle mental manipulation in multi-party, multi-turn conversations. The MultiManip dataset is a useful resource in that it moves beyond dyadic fictional dialogues, and the authors make code and data publicly available. The SELF-PERCEPT idea of separating behavioral observation from inference is intuitively plausible and worth testing. However, the central empirical claim that SELF-PERCEPT outperforms existing prompting methods is not yet established: the evaluation rests on a small, noisy gold set, lacks statistical safeguards, and omits the closest prior baseline. If the robustness concerns are addressed, the dataset and prompting framework could make a meaningful contribution.

major comments (5)

[§2.2, Table 6, §A.4] The gold labels in MultiManip are not shown to be stable enough to support the reported method rankings. Section 2.2 reports Fleiss' kappa = 0.429, and Table 6 shows median agreement of 2 out of 5 annotators for 8 of the 11 manipulation classes, with mean agreement scores below 2.0 for Feigning Innocence and Accusation. Section A.4 states that when label frequencies tie, all tied labels are retained, so a label can enter the gold set with support from only 2 of 5 annotators. Because the main F1 differences on MultiManip are small (0.37 vs 0.34 for GPT-4o, for example), the ranking may change under annotation noise. The paper should provide an uncertainty analysis, such as a bootstrap over annotators, confidence intervals, or a sensitivity analysis excluding low-agreement items, to show the central comparison is robust.
[§3.3, Tables 1-2] The evaluation omits the closest prior method. The paper cites Ma et al. (2024), 'Detecting conversational mental manipulation with intent-aware prompting,' in the introduction, but Tables 1 and 2 do not include intent-aware prompting as a baseline. Since that method targets the same task and was evaluated on MentalManip, the claim that SELF-PERCEPT 'outperforms all other methods' is not supported without this direct comparison. The authors should add this baseline, and any other recent prompting baselines for manipulation detection, to Tables 1 and 2.
[Appendix D, Tables 1-2] The experiments are single-run and lack any statistical quantification. Appendix D reports temperature 0.7 for all inference, but no seeds or repeated runs are described, and Tables 1 and 2 report point estimates without confidence intervals. With only 220 MultiManip samples and F1 gaps as small as 0.03 between GPT-4o SELF-PERCEPT and CoT, these differences could easily arise from decoding stochasticity. The paper should report results over multiple seeds, with confidence intervals and a paired significance test, before claiming consistent improvement.
[§3.2, Appendix C, §B.1] The two-stage decomposition of SELF-PERCEPT may be confounded with the baseline because Stage 1 already asks for the answer. The Stage 1 prompt in Appendix C instructs the model to 'Identify any persuasive techniques or manipulations used in the conversation,' and the worked example in §B.1 shows the Stage 1 output containing a 'Persuasive Techniques' bullet that already identifies manipulation content. Stage 2 then asks for a Yes/No decision and a label. As a result, the comparison against CoT is not a clean test of the proposed introspection mechanism. The authors should clarify what information is available to Stage 2 and add an ablation that removes manipulation identification from Stage 1.
[Table 1, §3.3] The relationship between the reported Precision, Recall, and F1 values is not defined, and some values are mutually inconsistent at face value. For example, GPT-4o CoT lists P=0.21, R=0.32, F1=0.34 (harmonic mean 0.25), and Llama-3.1-8B CoT lists P=0.23, R=0.26, F1=0.10 (harmonic mean 0.24). The caption states 'Macro F1 Scores' but does not state whether P and R are macro-averaged. The paper should report the exact averaging scheme and, if F1 is macro-averaged, explain why the narrative in §3.3 interprets the F1 gain as a precision/recall trade-off.

minor comments (3)

[Abstract/§1] The phrase 'an balanced representation' and 'an balanced distribution' should be corrected to 'a balanced representation' and 'a balanced distribution.'
[§A.1] The Fleiss' kappa formula presented is for single-label categorical assignments, but annotators could assign multiple labels per dialogue; the paper should specify how kappa was computed in this multi-label setting.
[Figure 3, §B.2] The SHAP plots lack axis labels and a description of the underlying model, tokenizer, and attribution method; the interpretability analysis is not reproducible as written.

Circularity Check

1 steps flagged · score 2.0 of 10

Empirical comparison is self-contained; only a minor self-referential SHAP illustration was found.

other [Section 3.3 (SHAP analysis; Appendix B.3)]
"The SPT Stage 1 model correctly identifies manipulation, as evidenced by the strong negative SHAP values for words like "anxious," "situation," and "teamwork", which capture psychological pressure, power imbalance, and persuasive intent (C.f Appendix B.3)."

SHAP values are computed from the classification decision of the very model being evaluated, so they describe which input words the model's internal weights favored, not whether the model's judgment matches ground truth. The paper cites these internal attributions as evidence that "The SPT Stage 1 model correctly identifies manipulation," making the correctness claim rest on the model's own output rather than on the human labels used elsewhere. This is a self-referential validation, though it is an explanatory aside and does not carry the main benchmark comparison.

full rationale

The central claim is an empirical benchmark result, not a derivation: SELF-PERCEPT's F1 and accuracy numbers in Tables 1 and 2 are computed by prompting frozen LLMs and comparing outputs to human-annotated MultiManip labels and the external MentalManip test set. No parameter is fitted to the test data, and the comparison against zero-shot, few-shot, and CoT uses the same models and data, so the main ranking is not forced by construction. The only circular-seeming passage is the SHAP analysis in Section 3.3 and Appendix B.3, where the model's own SHAP attributions are quoted as evidence that SPT Stage 1 correctly identifies manipulation; SHAP values show which inputs a model weighted, not that the weighting is correct, so that illustrative claim is self-referential. It is not load-bearing for the headline result. The dataset-construction concerns raised by the reader, including Llama-3.1-70B preselection, Fleiss kappa 0.429, and tie-breaking that can admit 2-of-5 labels into the gold set, are validity threats to the benchmark, but they are not circularity, because the gold labels are external to the prompting methods being compared. Overall, no load-bearing reduction of the central claim to its own inputs was found.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of the MultiManip labels (axioms 1 and 2), on the LLM pre-filter that created the dataset (axiom 3), and on the psychological analogy underlying the prompt method (axiom 4). No numerical parameters are fitted in the paper, so the free-parameter ledger is empty.

assumptions (4)

domain assumption The 11 manipulation techniques taken from Barnhill (2014) are applicable and sufficient for labeling conversational manipulation.
Section 2 states the taxonomy is 'adapted from psychological research (Barnhill, 2014) for conversational contexts' and is used for both annotation and prompting.
domain assumption Majority-vote aggregation of five annotators with Fleiss kappa 0.429 yields valid ground-truth labels.
Section 2.2 describes the annotation process and reports moderate agreement; the paper treats the resulting labels as ground truth despite the disagreement.
ad hoc to paper Dialogues pre-selected by Llama-3.1-70B are representative of real-world manipulative and non-manipulative conversations.
Section 2.1 explains that Llama-3.1-70B was used to filter raw transcripts for manipulative content; the benchmark inherits any bias in that filter.
ad hoc to paper Self-Perception Theory, a model of human attitude inference, transfers to LLM text inference.
Section 3.1 motivates SELF-PERCEPT with Self-Perception Theory but provides no evidence that LLMs infer attitudes the way humans do.

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Cite this review

Pith. "Pith review of SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations." pith.science (2026). https://pith.science/paper/D6EWYGTO

@misc{pith2026250520679,
  author       = {Pith},
  title        = {Pith review of: SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D6EWYGTO}},
  note         = {Machine review of arXiv:2505.20679}
}

read the original abstract

Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation's nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. For manipulative interactions, it includes 11 distinct manipulations depicting real-life scenarios. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at https://github.com/danushkhanna/self-percept .

Figures

Figures reproduced from arXiv: 2505.20679 by the authors.

**Figure 2.** Taxonomy of various Mental Manipulation Techniques. Description of Techniques in [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Top SHAP Contributions from SPT Stage 1 and CoT [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

OpenAI Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haim ing Bao, Mo Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenn...

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doi:10.1177/1178222618792860 2018

[7] [7]

Cristian Danescu-Niculescu-Mizil and Lillian Lee. 2011. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011

work page 2011

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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Rozi \`e re, Bet...

arXiv 2024