{"id":"71693642-a253-4ceb-8be1-68866ef6d082","arxiv_id":"2505.24199","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"An intuitionistic fuzzy set annotation scheme is claimed to raise inter-annotator agreement from 0.67 to 0.79, cut annotation time by 15.7%, and improve RLHF win rate by 12.3%, but without released artifacts or significance testing.","lead":"Intuitionistic fuzzy sets, which let annotators rate support, opposition, and uncertainty separately, are proposed as a way to collect human preference data for training large language models. The reported gains in annotation agreement, speed, and downstream win rates are large, but the paper provides no code, data, or statistical tests to back them.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 12.3% win-rate and consistency gains are not attributable to IFS because the three annotation groups of five annotators are uncontrolled and no error bars or artifacts are provided; the claimed improvement is therefore unverified.","rationale":"The paper's central claim is empirical: IFS annotation yields better consistency, lower fatigue, and a 12.3% downstream win-rate gain. For that claim to be true, the comparison must isolate the annotation interface from all other differences between conditions. Section V-A2 describes random division of 15 professional annotators into groups of five, with no baseline measurement of annotator skill, no counterbalancing of task order, and no statistical test. With n=5 per cell, random assignment is far too weak to ensure exchangeability, especially on a skill-sensitive task; a single strong annotator can shift a group mean substantially. The subsequent tables report only point estimates. Thus the observed differences are equally compatible with a group-composition confound as with a genuine IFS effect. The missing artifact (no code, data, or training configuration) prevents any verification of the key numbers. This alone justifies withholding acceptance. Additional in-text signals reinforce the same conclusion: Eq. (13) defines Quality Score with (1 − π_avg) as a component, so the reported −0.73 correlation between average hesitation and 'annotation quality' is definitionally circular; and Eq. (7) is mathematically wrong as stated because for a single annotation it returns (1−μ,ν) rather than (μ,ν), so the 'IFWA' operator is not an IFS aggregation. These do not replace the experimental concern but show the manuscript has not been carefully checked. A within-subject crossover replication is the decisive check: if the IFS advantage persists under paired comparison with the same annotators, the confound is refuted; if not, the central claim fails. Therefore the reader's REJECT verdict remains appropriate; no adjustment is needed.","tokens_in":8250,"tokens_out":10363,"duration_ms":132971,"concrete_test":"Conduct a within-subject replication: have the same 15 annotators annotate three matched 500-pair batches (one per method) in counterbalanced order, then compute paired per-annotator agreement, time, and downstream preference-model win rates with 95% bootstrap intervals. If the IFS condition no longer shows a significant advantage over binary on all three metrics, the original between-group design was too confounded to support the headline claim.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim rests on a between-group comparison in which only five professional annotators were randomly assigned to each annotation interface (Section V-A2). The paper reports no per-annotator baselines, no matching of expertise, no task-order counterbalancing, and no significance tests for Tables I–V; the code, data, and RLHF training configuration are not released. With n=5 per condition, random division does not guarantee exchangeability, so the reported gains (agreement 0.79 vs 0.67, 15.7% time reduction, 12.3% win-rate gain) could be produced by group-level skill differences or interface familiarity rather than by IFS. This is the load-bearing weakness. Two internal signals reinforce it: Eq. (13) defines Quality Score with (1−π_avg) as an explicit term, making the reported −0.73 hesitation-quality correlation definitionally circular, and Eq. (7) returns (1−μ,ν) for a single weighted annotation instead of (μ,ν), so the stated IFWA aggregation is not a valid IFS operator.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes an intuitionistic fuzzy set (IFS) framework for side-by-side preference annotation for LLM training data. Annotators express preference via membership (support), non-membership (opposition), and hesitation (uncertainty) on three sliders; the framework includes aggregation operators for multiple annotators, quality metrics, and an empirical comparison against binary and Likert-scale annotation on three datasets. The central claim is that IFS annotation improves inter-annotator agreement, reduces fatigue and annotation time, and yields better downstream RLHF/DPO data, including a 12.3% relative win-rate improvement over the binary baseline.","tokens_in":8477,"tokens_out":6907,"duration_ms":83869,"significance":"If the empirical claims were reliable, the work would offer a practical and well-motivated way to record annotator uncertainty in preference collection, with a concrete interface and aggregation methods. The paper correctly identifies a real limitation of binary and Likert-scale annotation and proposes a plausible remedy. However, the contribution as submitted is not established: the experimental core uses only five annotators per condition with no controls or significance testing, the main aggregation equation is mathematically invalid, and the uncertainty-quality correlation is circular by construction. The paper provides no code, data, or machine-checked proofs, despite promising open-source release, so the reported results cannot be verified or reproduced.","major_comments":[{"comment":"The empirical comparison is a between-subject design with five professional annotators per condition, and the paper reports no per-annotator baselines, no matching of expertise, no task-order counterbalancing, and no confidence intervals or significance tests. All point estimates in Tables I-V, including agreement 0.79 vs 0.67, average time 38.1 vs 45.2 seconds, and win rate 0.699 vs 0.623, are therefore compatible with group-level skill differences or interface-familiarity effects rather than with the annotation method. The abstract's claim that the IFS method 'significantly improves' annotation quality is unsupported; the authors need annotator-level data, a within-subject or matched design, statistical inference, and an artifact release to substantiate the claim.","section":"Section V-A2, Tables I-V"},{"comment":"The IFWA operator is written as (∏_{i=1}^n (1−μ_i)^{w_i}, 1−∏_{i=1}^n (1−ν_i)^{w_i}). For a single annotation (n=1, w_i=1) this returns (1−μ, ν) instead of (μ, ν), so the operator does not reduce to the identity on an IFS value. Moreover, the output can violate the IFS constraint μ+ν≤1; for example, μ=0.2 and ν=0.8 yields (0.8, 0.8), whose sum is 1.6. The standard intuitionistic fuzzy weighted averaging operator is (1−∏(1−μ_i)^{w_i}, ∏ν_i^{w_i}). This error undermines the aggregation section and the IFWA row of Table VI, and it must be corrected and the affected results recomputed.","section":"Section IV-C1, Eq. (7)"},{"comment":"The reported 'strong negative correlation (-0.73) between average hesitation degree and annotation quality' is definitional, not empirical. Equation (13) defines Quality Score as α·(1−π_avg)+β·Clarity+γ·Consistency, while Eq. (9) defines Confidence as 1−mean(π); the remaining two terms are also deterministic functions of the same μ and ν observations. Since quality is constructed to decrease with π, the negative correlation is forced by the definition. To make the claim meaningful, the authors must define annotation quality independently of hesitation or drop the claim that the correlation empirically supports using uncertainty as a quality indicator.","section":"Section V-D2, Eq. (13)"},{"comment":"Inter-annotator agreement is not reported on a common scale across the three methods. The IFS agreement is defined by Eq. (11) as 1 minus the mean normalized intuitionistic fuzzy distance, but the paper gives no definition of the corresponding binary and Likert agreement measures. If binary and Likert agreement are computed as percentages or kappa statistics while IFS agreement uses a different distance-based formula, then the direct comparison in Table I (0.79 vs 0.67) is not meaningful. The authors should report all three conditions under a single, clearly defined agreement metric.","section":"Section V-B1, Eq. (11), Table I"},{"comment":"The downstream preference-model and RLHF experiments are described without the information needed for reproducibility or interpretation: the base model and architecture, optimizer, hyperparameter values, dataset version and split, and evaluation protocol are all omitted. Without these details, the 12.3% win-rate improvement (Table V) cannot be attributed to the annotation method, and the result cannot be independently checked. This is a load-bearing gap because the downstream improvement is a central claimed benefit of the framework.","section":"Section V-C, Tables IV-V"}],"minor_comments":[{"comment":"The text says each group annotated 'the same subset of 1,000 examples from each dataset, with 20% overlap for inter-group comparison'; if the subset was identical for all groups, the overlap is 100%, so the intended design should be clarified.","section":"Section V-A2"},{"comment":"The free parameters in Eqs. (5)-(6), (8), and (13) are never assigned numerical values or a fitting procedure; the criterion weights w_i and dynamic coefficients α, β, γ must be specified or estimated from data for the aggregation and quality metrics to be reproducible.","section":"Section IV-B, IV-C, IV-D"},{"comment":"The 'consistency' measured by self-agreement after one week is reported with standard deviations, but the metric itself is undefined; state how self-agreement was computed and how many re-annotated examples were used.","section":"Section V-B2, Table II"},{"comment":"Several related-work descriptions are imprecise: reference [4] is about user feedback for NMT rather than inter-annotator reliability as stated, and reference [5] concerns V-usable information, not directly human-preference consistency; the citations should be checked and corrected.","section":"Section II"},{"comment":"The conclusion promises an open-source release of annotation tools and aggregation algorithms, but no repository, URL, or supplementary artifact is included anywhere in the manuscript.","section":"Section VII"}],"recommendation":"reject","confidential_remarks":"The paper is not ready for publication in a serious journal. The central empirical claims are built on a five-annotator-per-condition comparison without statistical controls, the IFWA aggregation equation is mathematically wrong as written, and the headline uncertainty-quality correlation is circular. These are not local presentation issues; they require a substantially new empirical study and a corrected theoretical section. The absence of any code, data, or training configuration despite the promised release further lowers confidence in the reported numbers. I would not encourage resubmission in the current form."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the IFS-annotation idea is genuinely worth a look, but the preprint's headline numbers are not supported by anything I can verify, and two equations are internally off.\n\nWhat's new: the three-slider interface for separate support/opposition/hesitation is a sensible design for capturing uncertainty in side-by-side preference annotation, and the paper applies standard IFS machinery (Atanassov, Xu, Yager) to a practical RLHF problem. The writing is clear and the limitations section is honestly written.\n\nWhere it falls down. First, the empirical core is a between-group comparison with five annotators per condition, random division, no per-annotator baselines, no task-order counterbalancing, and no significance tests or error bars. With n=5, the reported 17.9% agreement gain, 15.7% time reduction, and 12.3% downstream win-rate gain are all plausibly explained by group-level skill differences or interface familiarity. No code, data, or training configuration is provided, despite the conclusion promising an open-source release. Second, Eq. (7) is not a valid IFWA operator: with a single annotation (w=1) it returns (1−μ,ν) instead of (μ,ν), and the resulting pair can violate μ+ν≤1. That is a published equation and it is simply wrong as written. Third, Eq. (13) defines Quality Score with (1−π_avg) as an explicit term, while Eq. (9) defines Confidence as 1−mean(π). So the reported −0.73 hesitation-quality correlation is partly built into the definition. The uncertainty-quality finding is therefore circular and proves little about the data.\n\nWhat's defensible: the motivation is real, the interface idea is plausible, and the citation pattern is appropriate. But as it stands, the evidence does not support the abstract's claims of significance. The formal errors are fixable, and the empirical gaps are addressable by releasing the artifacts and rerunning with matched annotators or more subjects and proper statistics.\n\nVerdict: this is not ready for peer review as a substantive empirical paper. If the authors fix the math, release the annotation interface and data, and add error bars and a proper control design, I'd be happy to see a revised version. For now, I'd pass on citing it and probably not bring it to reading group. The underlying idea is worth keeping in mind.","headline":"The IFS-annotation interface is a reasonable idea, but the headline improvements are unverified and two equations are internally off.","tokens_in":9026,"tokens_out":3952,"would_cite":false,"duration_ms":43359,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Replacing binary side-by-side preference labels with three-slider intuitionistic fuzzy set annotations—support, opposition, and hesitation—produces higher-quality preference data and improves downstream LLM alignment, with a reported…","keywords":["intuitionistic fuzzy sets","preference annotation","side-by-side evaluation","RLHF","DPO","annotator uncertainty","data quality","LLM alignment"],"falsifier":"A crossover study in which the same annotators label the same items with binary, Likert, and IFS, with method order balanced, would settle the claim: if the win-rate and agreement advantages disappear or shrink to noise under within-annotator comparison, the reported 12.3% improvement is not attributable to the IFS representation. A simpler check is per-group baselines: measure each annotator's agreement on a common binary pre-test; if the IFS group starts with higher agreement, the result is confounded.","tokens_in":8000,"feed_emoji":"🎚️","tokens_out":6450,"duration_ms":60218,"temperature":0.7,"pith_summary":"The paper claims that traditional binary and Likert-scale side-by-side preference annotation forces annotators to make artificial choices, losing information about uncertainty and hesitation. It proposes replacing these with an intuitionistic fuzzy set (IFS) representation, where each judgment has a support (membership), an opposition (non-membership), and a hesitation degree, collected through three separate sliders. Across three datasets with fifteen professional annotators, the paper reports that IFS annotation raises inter-annotator agreement from 0.67 to 0.79, reduces average annotation time from 45.2 to 38.1 seconds per example, and yields preference data that trains RLHF models to a 0.699 win rate versus 0.623 for binary. If these results hold, the practical route to better alignment data is not more forced comparisons but a principled way to record how sure the annotator is.","feed_headline":"Three-slider labels lift LLM win rate 12.3%","feed_subtitle":"Letting annotators set support, opposition, and uncertainty separately produces faster, more consistent preference data.","key_machinery":"The load-bearing object is the intuitionistic fuzzy set, defined by a membership degree $\\mu(x)\\in[0,1]$, a non-membership degree $\\nu(x)\\in[0,1]$, and a hesitation degree $\\pi(x)=1-\\mu(x)-\\nu(x)$, with $\\mu+\\nu\\le 1$. In the annotation protocol, each response in a side-by-side pair is scored with two sliders (support and opposition), and hesitation is computed automatically, so the interface enforces the IFS constraint in real time. Aggregation is carried out by the intuitionistic fuzzy weighted averaging operator and by a proposed dynamic weighting mechanism that adjusts annotator weights from consistency, expertise, and agreement; quality is assessed by IFS-specific metrics for confidence, clarity, and a distance-based inter-annotator agreement. The argument works by showing that these three components carry independent information: hesitation correlates with task difficulty, and uncertainty can serve as a quality signal.","core_discovery":"The central discovery is that explicitly modeling the three-way structure of a preference judgment—how much an annotator prefers response A, how much they oppose it, and how much they hesitate—captures signal that binary or Likert scales discard. The paper formalizes a preference over two responses as an intuitionistic fuzzy set with membership $\\mu$, non-membership $\\nu$, and hesitation $\\pi=1-\\mu-\\nu$, and designs an interface where annotators move support and opposition sliders while hesitation is displayed automatically. It then defines aggregation operators, including a consensus-based weighted combination and a dynamic weighting scheme based on annotator consistency, expertise, and agreement. Empirically, the paper reports that these IFS annotations are more self-consistent over time, show higher inter-annotator agreement, degrade less with fatigue, and produce preference models whose RLHF-trained outputs win more often against a baseline. The claimed effect sizes are large: a 12.3% relative improvement in win rate over binary annotations and a 15.7% reduction in annotation time.","pith_inferences":["A within-annotator crossover design—same people labeling the same items with binary, Likert, and IFS—would separate the representation's effect from group-level differences in annotator skill; the paper's between-group experiment does not.","The hesitation slider could be repurposed as an active-learning signal: examples with large average $\\pi$ are candidates for expert review or additional annotation, though the paper only lists this as future work.","The reported win-rate gain is relative to a particular baseline and training setup; whether IFS data improves absolute alignment or safety beyond what any high-quality binary dataset would achieve is not isolated here.","If hesitation correlates with task difficulty as strongly as reported (-0.73), IFS annotations could serve as a built-in difficulty estimator for benchmarking and test-set stratification."],"forward_implications":["IFS-based annotation can be dropped into existing RLHF and DPO pipelines: the preference model is trained on the aggregated $\\mu$ and $\\nu$ values, and the resulting rewards improve downstream win rate by 12.3% over binary-derived data.","Annotation projects that adopt the three-slider protocol can expect roughly 16% shorter per-example times and about half the hourly quality degradation (0.03 vs 0.08 per hour) reported for binary annotation.","Uncertainty information becomes a management tool: high average hesitation identifies examples that need review or expert annotation, and the negative correlation between hesitation and quality (-0.73) supports flagging low-confidence judgments.","The aggregation and dynamic-weighting machinery extends to multi-criteria evaluation, allowing fluency, accuracy, and safety to be weighted and combined within the same IFS representation."],"supporting_citations":[{"why":"Establishes the RLHF training paradigm that this annotation method feeds into.","marker":"[1]"},{"why":"Defines direct preference optimization, the downstream training objective that consumes the preference data.","marker":"[2]"},{"why":"Defines intuitionistic fuzzy sets with membership, non-membership, and hesitation degrees, the mathematical basis of the annotation representation.","marker":"[3]"},{"why":"Shows that annotator disagreement can carry signal rather than noise, motivating the preservation of uncertainty in preference labels.","marker":"[9]"},{"why":"Explores multi-way comparisons and tie options in preference collection, the alternative approach this paper compares against.","marker":"[10]"},{"why":"Supplies the intuitionistic fuzzy aggregation operators that the paper's weighted averaging builds on.","marker":"[16]"}],"fun_headline_variants":["Tri-slider feedback lifts LLM win rate 12.3%","Uncertainty-aware annotation beats binary labels in RLHF","Modeling hesitation in human feedback yields better model wins","Three-way sliders produce faster, more consistent LLM data"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"All reported differences are attributed to the annotation method rather than to the particular annotators, because the paper compares three separate groups of five people instead of having the same people use all three methods.","fun_headline_variants_meta":{"raw":{"variants":["Tri-slider feedback lifts LLM win rate 12.3%","Uncertainty-aware annotation beats binary labels in RLHF","Modeling hesitation in human feedback yields better model wins","Three-way sliders produce faster, more consistent LLM data"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000611,"raw_usage":{"total_tokens":2870,"prompt_tokens":997,"completion_tokens":1873,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":613,"completion_tokens_details":{"reasoning_tokens":1812}},"tokens_in":613,"tokens_out":1873,"duration_ms":16235,"temperature":1.0,"reasoning_tokens":1812,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T12:30:50.001229+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A crossover study in which the same annotators label the same items with binary, Likert, and IFS, with method order balanced, would settle the claim: if the win-rate and agreement advantages disappear or shrink to noise under within-annotator comparison, the reported 12.3% improvement is not attributable to the IFS representation. A simpler check is per-group baselines: measure each annotator's agreement on a common binary pre-test; if the IFS group starts with higher agreement, the result is confounded.","supporting_citations":[{"cited_title":"Ouyang et al., ”Training language models to follow ins tructions with human feedback,” in Proc","cited_arxiv_id":null,"evidence_quote":"Establishes the RLHF training paradigm that this annotation method feeds into."},{"cited_title":"Rafailov et al., ”Direct preference optimization: Y o ur language model is secretly a reward model,” arXiv preprint arXiv:2305.182 90, 2023","cited_arxiv_id":null,"evidence_quote":"Defines direct preference optimization, the downstream training objective that consumes the preference data."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines intuitionistic fuzzy sets with membership, non-membership, and hesitation degrees, the mathematical basis of the annotation representation."},{"cited_title":"Plank et al., ”Learning part-of-speech taggers with i nter-annotator agreement loss,” in Proc","cited_arxiv_id":null,"evidence_quote":"Shows that annotator disagreement can carry signal rather than noise, motivating the preservation of uncertainty in preference labels."},{"cited_title":"Dubois et al., ”AlpacaFarm: A simulation framework f or methods that learn from human feedback,” in Proc","cited_arxiv_id":null,"evidence_quote":"Explores multi-way comparisons and tie options in preference collection, the alternative approach this paper compares against."},{"cited_title":"Xu, ”Intuitionistic fuzzy aggregation operators,” IEEE Transactions on Fuzzy Systems, vol","cited_arxiv_id":null,"evidence_quote":"Supplies the intuitionistic fuzzy aggregation operators that the paper's weighted averaging builds on."}],"review_version":1}