{"id":"4c85ae8d-9c37-4e10-b828-e30875c42cc6","arxiv_id":"2506.00061","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A new 58-technique taxonomy and 746-dialogue dataset for social influence detection, on which the best LLM achieves 0.45 category F1.","lead":"The paper introduces SITT, a taxonomy of 58 social influence techniques in 9 categories, and a 746-dialogue dataset annotated by experts. It benchmarks five LLMs and finds that even the best model reaches only a moderate F1 score of 0.45 for categories, showing that current models struggle with nuanced social influence.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"GPT-4o pre-assignment truncates the gold label space, so the reported F1 scores cannot establish the claimed LLM detection ceiling.","rationale":"The reader's weakest assumption—gold label reliability—is on the right track, but the more precise and load-bearing problem is that the gold labels are not merely noisy; they are structurally truncated by the GPT-4o pre-assignment step. Section 4.2 and Appendix B.4 show that annotators received dialogues with categories pre-assigned by GPT-4o and were only asked to label techniques within their assigned category pairs. The dataset therefore cannot contain a technique that GPT-4o did not predict for that dialogue, and the 'other categories' marks are category-level only, with no second annotation round to add techniques (Section 4.2). This means the benchmark's technique-level F1 scores measure agreement with a label set whose positive space is bounded by GPT-4o's predictions. A model that correctly detects a technique from a category GPT-4o missed is penalized as a false positive, and recall for all models is artificially capped. The claim that 'LLMs have not yet developed strong capabilities' could still be true, but the reported numbers do not establish it; a full label set might reveal substantially higher recall. The proposed check—re-annotating a sample without pre-assignment—would settle this directly. I therefore keep the reader's CONDITIONAL verdict: the taxonomy and dataset remain useful, but the empirical benchmark needs this validity check before its headline conclusion can be accepted.","tokens_in":45036,"tokens_out":7960,"duration_ms":74579,"concrete_test":"Take a random sample of 100 dialogues from the SITT dataset; have three fresh experts annotate each dialogue for all nine categories and all 58 techniques using the published guidelines, with no GPT-4o pre-assignment. Compute the proportion of positive labels in the new gold set that are absent from the published gold set. If more than 10% of category/technique positives are missing, the reported F1 scores are systematically biased and the zero-shot ceiling claim is unsupported by the current benchmark.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing weakness is the label-construction pipeline, not annotator noise. Section 4.2 states that annotation samples were distributed 'based on initial model predictions' — the GPT-4o pre-assignments from Appendix B.4 — and that each annotator labels techniques only within their two assigned categories. As a result, any category or technique that GPT-4o did not pre-assign for a dialogue cannot receive a technique-level gold label; the 'other categories' marks are category-only, and per Section 4.2 the second annotation round that would assign techniques for those categories 'was not yet performed.' The gold technique labels are therefore restricted to GPT-4o's pre-assigned categories. This makes the benchmark circular for the technique-level claim: a model that correctly detects a technique from a category GPT-4o missed is scored as a false positive, and the model's recall is capped by GPT-4o's own recall. The headline F1=0.31 for techniques (Claude, Polish) and the list of 'never correctly identified' techniques are thus not clean measurements of LLM capability; they partly measure agreement with a GPT-4o-initialized label set. The 159 GPT-4o-generated dialogues (Appendix B.3) compound the issue because their gold labels are the techniques GPT-4o was told to instantiate. The absence of inter-annotator agreement and calibration is real but secondary: even perfect expert agreement cannot recover labels that were never presented for annotation. This concern directly threatens the paper's central claim that LLMs have not yet developed strong detection capabilities.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces SITT, a two-tier taxonomy of 58 social influence techniques grouped into nine categories, and constructs a 746-dialogue Polish benchmark (SITT dataset) annotated by 11 experts, with English translations produced by GPT-4o. The authors evaluate five LLMs in a hierarchical multi-label classification setting, reporting micro-F1 scores for categories and techniques. The headline result is that Claude 3.5 Sonnet achieves the best Polish performance with 0.45 micro-F1 for categories and 0.31 for techniques, while several categories and techniques are rarely or never correctly identified. The paper concludes that current LLMs have limited zero-shot capability for detecting subtle social influence and argues for domain-specific fine-tuning and larger datasets.","tokens_in":45292,"tokens_out":6175,"duration_ms":63373,"significance":"If the benchmark were methodologically clean, the paper would be a useful contribution: it provides a detailed taxonomy with definitions and examples, a public dataset in an under-resourced language setting, full prompts and annotation guidelines, and a falsifiable low-ceiling result for zero-shot LLM detection of social influence. The appendices are unusually transparent about data construction and expert verification. However, the central empirical claim about technique-level detection is currently not supported because the gold technique labels are restricted by GPT-4o pre-assignment, and the evaluation also lacks inter-annotator agreement and a human baseline. The resource and taxonomy are still valuable, but the benchmark conclusions require rework before publication.","major_comments":[{"comment":"Technique-level gold labels are restricted to categories that GPT-4o pre-assigned. Section 4.2 says annotation samples were distributed \"based on initial model predictions\" and each expert labels techniques only in two assigned categories; Section 4.4 explicitly notes that the second round of annotations \"was not yet performed.\" Consequently, any technique in a category GPT-4o did not pre-assign cannot receive a gold technique label, so a model that detects it is counted as a false positive and recall is capped by GPT-4o's recall. This directly affects the headline technique F1 scores in Table 3 and the \"never correctly identified\" technique lists in Section 5.2 and Appendix D. The technique-level benchmark should be completed with full annotation, or the claims should be reframed as agreement with a GPT-4o-initialized label set.","section":"4.2, 4.4, 5.2, Table 3"},{"comment":"The same model, GPT-4o, generated 159 of the 746 dialogues with labels equal to the techniques it was told to instantiate, pre-assigned the categories used to route expert annotation, translated the Polish data into English, and was then one of the evaluated models. Section 5.2 even reports that GPT-4o's Polish and English results are identical. This creates potential leakage and makes the GPT-4o rows of Table 3 difficult to interpret as an independent evaluation. Report a robustness check that excludes GPT-4o-generated dialogues and uses a different translator model, or justify why these choices cannot affect the conclusions.","section":"B.3, B.4, B.5, 5.2"},{"comment":"No inter-annotator agreement is reported for the 2,177 assignments, and the verification step in Section 4.3 covers only a minimum of 10% of each annotator's work, with consistency ranging from 73% to 100%. Since the gold labels are the yardstick for every F1 score in Table 3, the absence of agreement statistics and the absence of any human baseline make it impossible to separate model error from annotation noise. Report per-category agreement (e.g., Cohen's kappa or Fleiss' kappa on the overlapping judgments) and a human-expert baseline on a sample.","section":"4.2, 4.3"},{"comment":"The hierarchical design in Section 5.1 lets a model name techniques only from the categories it predicted in the first step; the authors themselves note in the Limitations paragraph of Section 6 that category-level misclassification prevents technique identification. The reported technique-level F1 therefore conflates category detection with technique detection, which is especially relevant for the claims about techniques that were \"never correctly identified\". Report technique metrics conditioned on correct category prediction, or state in the headline conclusions that the technique numbers include error propagation from the category step.","section":"5.1, 6"}],"minor_comments":[{"comment":"Techniques 35 and 36 are both numbered 36 in the table and figures for \"Take advantage of a good mood\" and \"Take advantage of a bad mood\", conflicting with the numbering in Appendix A; renumber consistently throughout.","section":"Table 7, Figures 7 and 9"},{"comment":"Please correct typos and inconsistencies: \"Reciprority\" in Table 1, \"Content\" instead of \"Context\" in Table 6, \"Discusion\" in the Section 6 heading, and \"assigend\" in Section 4.2.","section":"Table 1, Table 6, Section 6, Section 4.2"},{"comment":"The reported F1 scores have no confidence intervals or significance tests; given the small set of five models and the extreme class imbalance shown in Figure 2, add bootstrap confidence intervals or at least a note on the instability of the estimates.","section":"Table 3, Figures 5-9"},{"comment":"The identical Polish and English results for GPT-4o are stated but not explained; clarify whether this is expected because the same model produced the translation, and discuss what this implies for the English-language comparisons in Table 3.","section":"5.2"},{"comment":"The 43 dialogues with no assigned technique are not characterized; state whether these are genuine negative examples or artifacts of the incomplete second annotation round, since this affects the interpretation of recall at the technique level.","section":"4.4"}],"recommendation":"major_revision","confidential_remarks":"The taxonomy and dataset are potentially valuable, and the category-level evaluation may still be informative after the technique-level issues are addressed. My recommendation is major revision because the technique-level benchmark is currently not trustworthy; the fixes are concrete (complete the second annotation round or narrow the claims, add agreement statistics and a human baseline, and run a robustness check without GPT-4o-generated instances). I would not reject the paper outright because the central issue is fixable within the manuscript's scope."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Worth knowing: this paper delivers a genuinely useful resource, a nine-category, 58-technique taxonomy of social influence and a 746-dialogue Polish corpus with expert labels. The taxonomy is a re-grouping of known techniques from Cialdini and Dolinski & Grzyb, but the textual-detection angle and the corpus are new. The paper is unusually transparent: full prompts, annotation guidelines, per-category and per-technique results, and an honest limitations section.\n\nThe soft spot is load-bearing. Section 4.2 says annotation samples were distributed based on initial model predictions, i.e., GPT-4o pre-assignments from Appendix B.4. Each annotator labeled techniques only within their two assigned categories, and the second round that would assign techniques for the other categories was not performed. So any technique from a category GPT-4o did not pre-assign for a dialogue could never receive a gold label. The other categories marks are category-only. This makes the technique-level F1 scores (e.g., Claude's 0.31) a measurement of agreement with a GPT-4o-initialized label set, not a clean measurement of LLM detection ability. A model that correctly catches a technique from a category GPT-4o missed is scored as a false positive, and recall is capped by GPT-4o's own recall. The never-correctly-identified techniques are suspect for the same reason. The 159 GPT-4o-generated dialogues compound this, because their gold labels are the techniques GPT-4o was told to instantiate.\n\nThe lack of inter-annotator agreement and a human baseline is real but secondary. Even perfect expert agreement cannot recover labels that were never presented for annotation.\n\nThe central claim, that LLMs have limited zero-shot capability on these techniques, is plausible and likely directionally right, but this evidence does not establish it. Read the paper as a resource contribution with a pilot evaluation, not as a benchmark result.\n\nRecommendation: send to peer review, but require the label-construction issue to be addressed, either a re-annotation with category sampling independent of model predictions, or an explicit scoping of the claim as detection relative to GPT-4o-proposed categories. Add agreement metrics and a human baseline. The resource is worth the effort.","headline":"Useful new taxonomy and dataset, but the technique-level benchmark is circular because GPT-4o pre-assignment capped the gold label space.","tokens_in":45915,"tokens_out":2751,"would_cite":true,"duration_ms":27264,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Top model hits 0.45 F1 on persuasion categories, 0.31 on techniques.","keywords":["social influence detection","manipulation detection","persuasion taxonomy","SITT","multi-label classification","LLM benchmark","Polish dialogue dataset","zero-shot classification"],"falsifier":"Re-annotate a random sample of the 746 dialogues with a fresh, calibrated panel and compute agreement such as Cohen's kappa per category and technique; if the techniques that scored zero in the LLM benchmark also show low annotator agreement, the zero scores are partly label noise rather than pure model failure.","tokens_in":44829,"feed_emoji":"🗣️","tokens_out":8434,"duration_ms":83698,"temperature":0.7,"pith_summary":"The paper builds a two-tier taxonomy of social influence, with nine categories and 58 concrete techniques, and uses it to create a 746-dialogue benchmark annotated by 11 experts in Polish and translated into English. It then asks whether five LLMs can detect the categories and techniques zero-shot in a hierarchical multi-label setup. The central claim is that they cannot yet: the best model reaches micro-F1 0.45 for categories and 0.31 for techniques on Polish, while the Context category averages only 0.07 F1 and dozens of techniques are never detected at all. If correct, this is a realistic baseline for any system meant to flag manipulation or persuasion in natural conversation, and it argues that domain-specific fine-tuning and larger, better-balanced datasets are necessary next steps.","feed_headline":"Top model hits 0.45 F1 on persuasion categories, 0.31 on techniques","feed_subtitle":"A 746-dialogue expert benchmark shows zero-shot LLMs miss most subtle social influence, with context tricks near zero.","key_machinery":"The load-bearing object is the Social Influence Technique Taxonomy (SITT): 58 empirically grounded techniques grouped into nine categories (Image, Context, Information, Social norms, Reciprocity, Emotions, Liking, Authority, Consistency), each with a definition and a dialogue-level example. The evaluation runs on the SITT dataset, 746 dialogues annotated in Polish by 11 experts and machine-translated into English, with categories and techniques as multi-label targets. Detection is hierarchical: a first prompt asks the LLM to assign categories from definitions and examples, and a second prompt asks it to choose techniques only from those categories, with temperature and top-p set to 0.0. That two-stage design means technique scores depend entirely on category scores, which is why category-level misses propagate into technique-level misses.","core_discovery":"On the paper's own terms, the discovery is a capability measurement. Under the SITT taxonomy and the hierarchical protocol, where category assignment comes first and technique assignment is restricted to predicted categories, no tested LLM shows strong detection of social influence. Claude 3.5 Sonnet performs best on Polish with micro-F1 0.45 for categories and 0.31 for techniques, while on English the best category score is Mixtral-8x22B at 0.40. The weakest spot is the Context category, whose mean F1 is 0.07, and 11 techniques in Polish and 14 in English receive no correct prediction at all. The paper reads this as evidence that current LLMs lack sensitivity to nuanced linguistic and contextual cues, that they are conservative in ways that favor precision over recall, and that the bottleneck is not uniform across categories.","pith_inferences":["Because GPT-4o generated 159 of the 746 dialogues from the taxonomy itself and pre-assigned categories, part of the benchmark may really be measuring agreement between models rather than human-visible social influence; a cleaner evaluation would use independently sourced dialogues.","The near-zero Context and Consistency scores may reflect a task-design issue as much as a model limitation, since those techniques often require world knowledge the prompt does not supply; conditioning the second stage on oracle categories would isolate the cause.","The per-technique failure lists are a ready-made curriculum: retraining on just the never-detected techniques and measuring recovery would directly test whether the ceiling is data-limited.","The same taxonomy could be turned into a generation-and-detection loop, using the definitions to synthesize positive and negative examples and testing whether fine-tuning on hard negatives closes the gap."],"forward_implications":["Any zero-shot LLM-based detector built today will miss most manipulative content, especially techniques that depend on situational or personal context.","The high-precision, low-recall behavior means such detectors will rarely produce false accusations, but will silently pass over most manipulation.","Category-level errors propagate: because the second stage only sees techniques from predicted categories, a missed category makes every technique inside it unreachable.","Technique-level performance is so low that fine-tuning, larger datasets, and better class balance are prerequisites for practical use.","Language matters: Polish scores are generally higher than English, consistent with the data being originally Polish and then translated."],"supporting_citations":[{"why":"Supplies the main systematization of social influence techniques that SITT reorganizes into nine categories.","marker":"(Dolinski and Grzyb, 2022)"},{"why":"Adds the influence principles behind four SITT techniques, including authority, reciprocity, and consistency.","marker":"(Cialdini, 2021)"},{"why":"Provides the framing and loss-aversion mechanisms behind the Context and Information categories.","marker":"(Kahneman, 2011)"},{"why":"MentalManip is the source of 488 of the 746 dialogues, each already identified as containing an influence technique.","marker":"(Wang et al., 2024)"},{"why":"CToMPersu contributes 99 persuasive dialogues from its evaluation set to the corpus.","marker":"(Zhang and Zhou, 2025)"},{"why":"GPT-4o generated 159 taxonomy-based dialogues, translated the corpus to English, and pre-assigned category labels for expert verification.","marker":"(OpenAI et al., 2024)"},{"why":"Identifies Claude 3.5 Sonnet, the model that achieves the best reported F1 on Polish categories and techniques.","marker":"(Anthropic, 2024)"}],"fun_headline_variants":["LLMs miss most social influence tactics, best F1 0.45","Expert-annotated 746 dialogues reveal LLMs' weak persuasion detection","Context tricks stump LLMs: zero F1 for 11 tactics in Polish","New taxonomy benchmarks LLMs, best category F1 just 0.45","Claude 3.5 top scorer but still fails at social cues, F1 0.45"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The benchmark conclusions collapse if the gold labels are not reliable: each dialogue-category pair was judged by only two or three of the eleven annotators, there was no calibration session and no reported inter-annotator agreement, only about 10 percent of annotations were verified, and GPT-4o both generated part of the corpus and pre-assigned the categories the experts then confirmed.","fun_headline_variants_meta":{"raw":{"variants":["LLMs miss most social influence tactics, best F1 0.45","Expert-annotated 746 dialogues reveal LLMs' weak persuasion detection","Context tricks stump LLMs: zero F1 for 11 tactics in Polish","New taxonomy benchmarks LLMs, best category F1 just 0.45","Claude 3.5 top scorer but still fails at social cues, F1 0.45"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000772,"raw_usage":{"total_tokens":3415,"prompt_tokens":938,"completion_tokens":2477,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":554,"completion_tokens_details":{"reasoning_tokens":2371}},"tokens_in":554,"tokens_out":2477,"duration_ms":17948,"temperature":1.0,"reasoning_tokens":2371,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T12:46:58.261839+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-annotate a random sample of the 746 dialogues with a fresh, calibrated panel and compute agreement such as Cohen's kappa per category and technique; if the techniques that scored zero in the LLM benchmark also show low annotator agreement, the zero scores are partly label noise rather than pure model failure.","supporting_citations":[],"review_version":1}