{"id":"c0d39760-4aca-4ccb-aea5-77d21529073f","arxiv_id":"2411.11937","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"An audit of three RLHF datasets using a new seven-value taxonomy and a classifier shows that information-utility values dominate and prosocial values are underrepresented.","lead":"This paper introduces Value Imprint, a framework that labels the human values embedded in RLHF preference datasets and uses a fine-tuned language model to classify whole datasets. A case study of three widely used RLHF datasets finds that information-seeking and knowledge values dominate while prosocial values like justice and well-being are scarce.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Cross-dataset value percentages rest on an unvalidated classifier transfer; the reported 84% human evaluation cannot be audited because selection and per-dataset agreement are unspecified.","rationale":"The reader's weakest_assumption identifies exactly the load-bearing concern: the classifier trained on Anthropic hh-rlhf is assumed to transfer to WebGPT and Alpaca, and the cross-dataset percentages in Section 4.2 depend on that transfer. I agree that this is the central vulnerability. The within-Anthropic ground-truth annotation (Table 1) is reasonably supported: the annotation team reports Krippendorff's alpha of 0.85 on 200 shared items, and the taxonomy is described in detail with citations. That portion of the paper supports the viability of the method on the training domain. However, the abstract's headline claim is explicitly about all three datasets, not just Anthropic, so the transfer step is not a peripheral detail. The only reported transfer check, the 84% human agreement on 500 items, is under-specified: without knowing whether those items include WebGPT and Alpaca, and without per-dataset agreement rates, the figure cannot rule out that the model mostly reproduces Anthropic-style patterns and assigns plausible-looking labels elsewhere. The concrete test I propose would settle this by directly measuring per-dataset classification quality on held-out human labels. Because the reader already conditioned the verdict on this issue, my recommendation is to keep the CONDITIONAL verdict; no change to the reader's assessment is needed.","tokens_in":23030,"tokens_out":3200,"duration_ms":31087,"concrete_test":"Sample 500 instances from each of WebGPT and Alpaca (plus a held-out Anthropic sample), have the same annotator team label them with the Section 3.2 taxonomy while reporting inter-annotator agreement, and compare the model's predictions to these labels per dataset. Report per-dataset accuracy, macro-F1, and confusion matrices, and also report the sampling procedure and label distribution for the human-evaluated 500 used in the paper. If per-dataset accuracy on WebGPT or Alpaca is substantially below the Anthropic test-set performance, or if the 84% agreement does not reproduce when the sample is stratified by dataset, then the Section 4.2 cross-dataset percentages are artifacts of the training-domain prior and the central claim fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central comparative claim in the abstract and Section 4.2—that Wisdom/Knowledge is dominant and Justice/Rights nearly absent across all three RLHF datasets—is produced by a RoBERTa classifier trained exclusively on 6,501 annotated Anthropic hh-rlhf examples and then applied to WebGPT and Alpaca without any reported domain adaptation or held-out validation on those datasets. The paper's only external evidence is a human evaluation of 500 classification results with a single 84% agreement figure; the paper does not report how those 500 were sampled, whether they cover all three datasets, the per-dataset agreement, or the label distribution of the sample. Given the format differences (hh-rlhf uses Human:/Assistant: dialogs; WebGPT uses question/answer_0; Alpaca uses instruction/output), the classifier may be responding to surface dialog structure and training-label priors rather than to the value content. The extreme percentage for Wisdom/Knowledge in WebGPT (78.17%) relative to the Anthropic training distribution (~30%) is consistent with distribution shift or label-prior effects. Additionally, Appendix D concedes that preferences embody multiple values and that interpretation is subjective, yet the model assigns a single dominant-value label; this makes cross-dataset transfer especially brittle. Therefore the headline finding across all three datasets is not independently verified.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces Value Imprint, a two-phase framework for auditing the human values embedded in RLHF datasets. The authors first construct a seven-category human-values taxonomy from a literature review in philosophy, axiology, and STS, then use it to annotate 6,501 preferences from Anthropic's hh-rlhf dataset, reporting a Krippendorff alpha of 0.85. They then train a RoBERTa classifier on these labels and apply it to three datasets: Anthropic/hh-rlhf, OpenAI WebGPT Comparisons, and Alpaca GPT-4-LLM. The headline finding is that information-utility values (Wisdom/Knowledge and Information Seeking) dominate all three datasets, while prosocial and democratic values (Well-being, Justice, Human/Animal Rights) are the least represented. The authors contribute their taxonomy, ground-truth annotations, and classification outputs via a GitHub repository.","tokens_in":23279,"tokens_out":5196,"duration_ms":51308,"significance":"The paper addresses a genuine and under-studied problem: how to make the value content of RLHF datasets auditable. The annotation phase is a strength: the inter-annotator agreement of 0.85 is encouraging, and releasing the taxonomy and datasets is a useful contribution to the community. The proposed framework, if validated, would give researchers a practical tool for interrogating the value orientations of preference datasets. The empirical finding that RLHF corpora skew toward information-utility values and away from civic and prosocial values is potentially important for alignment research. However, the current evidence does not yet support the full strength of the three-dataset claim, because the classifier trained on Anthropic data is applied to WebGPT and Alpaca without a demonstrated validation of that transfer. The central framework is defensible, but the central comparative claim needs additional verification.","major_comments":[{"comment":"The headline cross-dataset percentages are not independently verified. The RoBERTa classifier is trained exclusively on 6,501 annotated preferences from Anthropic hh-rlhf (Section 3.3) and then applied to OpenAI WebGPT Comparisons and Alpaca GPT-4-LLM without any held-out validation on those datasets or any reported domain adaptation. The only external check is a 500-item human evaluation reported as a single 84% agreement figure in Section 3.4.2, but the paper does not report how those 500 items were selected, whether they cover all three datasets, the per-dataset agreement, or the label distribution of the sample. Given the format differences (Human:/Assistant: dialogs versus question/answer_0 versus instruction/output) and the large shift for Wisdom/Knowledge (78.17% in WebGPT versus about 30% in the Anthropic training distribution), the classifier may be reproducing training-distribution priors and surface dialog structure rather than measuring the target datasets. The authors should provide per-dataset human evaluation, confusion matrices, or a classifier trained and evaluated on held-out data from each target dataset, or they should explicitly restrict the headline claim to the Anthropic dataset.","section":"Sections 3.4.2 and 4.2"},{"comment":"Alpaca GPT-4-LLM is not an RLHF preference dataset in the same sense as hh-rlhf or WebGPT Comparisons. It contains GPT-4-generated instruction-output pairs, not human pairwise preference judgments. Treating it as one of 'all three RLHF datasets' conflates distinct data-generating processes and weakens the comparative claim. The authors should either re-scope the claim to 'instruction-tuning and RLHF datasets' or provide a clear justification for why the Alpaca corpus is included in an audit of RLHF preferences, and adjust the abstract and conclusions accordingly.","section":"Section 3.1 and Section 4.2"},{"comment":"The evaluation of the classifier is underspecified. Section 3.4.2 reports an 'accuracy score range of 80%' and Section 4.1.2 reports F1 scores for selected classes (e.g., Empathy & Helpfulness 0.629, Well-being & Peace 0.649), but the paper does not report the test-set accuracy with confidence intervals, the macro- or weighted-average F1, a confusion matrix, or per-class support. Because the aggregate percentages in Section 4.2 are computed from classifier outputs, the uncertainty in those aggregates should be quantified. The 500-item human evaluation also needs a detailed protocol, including selection procedure, number of annotators, agreement metric, and per-dataset results, before '84%' can be interpreted as evidence of transferability.","section":"Sections 3.4.2 and 4.1.2"}],"minor_comments":[{"comment":"The description of the ground-truth annotation should state explicitly how the 6,501 preferences were sampled from Anthropic hh-rlhf (e.g., random, stratified, or other), and whether each annotated unit is a single response, a prompt-response pair, or a chosen/rejected pair. This information affects the interpretation of the label distribution and the classifier training.","section":"Section 3.3"},{"comment":"The WebGPT analysis uses only the answer_0 column and drops the comparison structure that defines the dataset. The authors should justify this choice and clarify whether answer_0 is the preferred answer, the first answer, or something else, since this affects what 'human values embedded in WebGPT' means.","section":"Section 3.1"},{"comment":"The heatmap in Figure 3 lacks axis labels, a color scale, and a legend, so the reader cannot read the quantitative comparisons that the text reports. The figure should be self-contained or be replaced by a table.","section":"Figure 3"},{"comment":"The sentence 'It contains 169,352 per row. resulting in a combined 338,704 if treated independently' is incomplete and inconsistent with the train/test counts reported in Section 3.1. Please correct the wording and the arithmetic.","section":"Appendix E"},{"comment":"A few reference entries have inconsistent journal names (e.g., 'Nous' versus 'Noûs' in entries [69], [134], [138], and [145]). Please unify and verify the bibliographic details.","section":"References"},{"comment":"The limitation in Appendix D that preferences often embody multiple values and that the model assigns a single dominant-value label is important, but it is not carried into the abstract or conclusions. The statements there present the value distributions as definitive facts rather than as dominant-value interpretations, which overstates the precision of the audit.","section":"Appendix D"}],"recommendation":"major_revision","confidential_remarks":"The paper fits the NeurIPS Datasets and Benchmarks track and makes a useful methodological contribution. The main risk is that the abstract and Section 4.2 claim a validated three-dataset result, whereas the evidence supports a well-annotated single-dataset classifier with an unvalidated transfer. I would ask the authors to provide per-dataset validation or to re-scope the claim, and to report the full evaluation details before acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Worth knowing: the annotation work for the Anthropic hh-rlhf ground truth is careful and the within-dataset findings are plausible. The cross-dataset comparison, however, is weaker than the abstract suggests, and the 500-item human check is not documented well enough to verify the transfer.\n\nWhat is genuinely new: this is the first attempt I know of to audit these three specific RLHF datasets with a human-values taxonomy, and the contributed ground truth and classification datasets are a real resource. The taxonomy construction is grounded in a large literature review, and the inter-annotator agreement of 0.85 on 200 shared examples is solid. The qualitative examples in Section 4.1 help make the categories concrete, and the paper is honest enough to include Appendix D, where the authors concede that value interpretation is subjective, the taxonomy is Western-focused, and a single dominant label is a simplification. That kind of stated limitation counts for something.\n\nThe soft spot is the load-bearing claim that Wisdom/Knowledge and Information Seeking dominate all three datasets. The classifier is trained exclusively on 6,501 Anthropic hh-rlhf preferences, then applied to WebGPT and Alpaca with different formats, domains, and label distributions. The reported 84% human agreement on 500 classified examples cannot be audited: no sampling procedure, no per-dataset breakdown, no label distribution of the evaluated sample. Given that the classifier outputs a single dominant value label while Appendix D concedes that preferences embody multiple values, the transfer is especially brittle. The extreme WebGPT Wisdom/Knowledge figure (78.17%) relative to the Anthropic training distribution (~30%) could be a real domain difference, but it could also be a label-prior or surface-format artifact. As published, the cross-dataset percentages are plausible but unverified.\n\nWithin the Anthropic dataset, the central finding holds up: information-utility values dominate and prosocial/rights values are rare. That alone is a useful empirical contribution. The paper needs either held-out validation on WebGPT and Alpaca, or a reframed conclusion that limits the comparative claim to the classifier's output rather than to the datasets' true value content.\n\nThis paper is for RLHF dataset curators and alignment-transparency researchers. It deserves a serious referee, but the revision should address the transfer validation head-on before the cross-dataset claims are accepted.","headline":"A credible first map of value distributions in RLHF data, but the headline cross-dataset percentages rest on an unvalidated classifier transfer.","tokens_in":23788,"tokens_out":1657,"would_cite":false,"duration_ms":19754,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"RLHF preference datasets are value-skewed, an audit claims: information-utility values dominate while prosocial and democratic values trail far behind.","keywords":["human values","RLHF datasets","value audit","value taxonomy","preference learning","AI alignment","dataset transparency","RoBERTa classification"],"falsifier":"Have independent annotators label a random sample of a few hundred preferences from OpenAI WebGPT and Alpaca GPT-4-LLM using the paper's taxonomy, and compare the resulting value shares with the model's predictions; if the human-labeled Justice share is not near 0.04 percent for WebGPT, the central imbalance claim would fail as a measurement of those datasets.","tokens_in":22859,"feed_emoji":"⚖️","tokens_out":4791,"duration_ms":42707,"temperature":0.7,"pith_summary":"Value Imprint is a two-phase technique for auditing which human values are actually embedded in the preference data used to fine-tune large language models with reinforcement learning from human feedback. The authors build a seven-category taxonomy of human values, hand-label 6,501 preference pairs from the Anthropic hh-rlhf dataset, and train a RoBERTa classifier to label the full Anthropic, OpenAI WebGPT, and Alpaca GPT-4 preference collections. Their central finding is that information-utility values, Wisdom/Knowledge and Information Seeking, dominate all three datasets, while prosocial and democratic values, especially Justice and Human/Animal Rights, are the least represented. The paper argues the framework is viable as an auditing tool, reporting roughly 80 percent classification accuracy and 84 percent agreement with human judgment on 500 classified examples. If correct, the work gives researchers a concrete way to see the value orientation of RLHF data before models are trained on it.","feed_headline":"Audit: RLHF data prizes knowledge over justice","feed_subtitle":"A classification framework finds Wisdom/Knowledge dominates three preference datasets while Justice and Well-being trail far behind.","key_machinery":"The load-bearing mechanism is the two-phase Value Imprint pipeline. Phase one is a hierarchical human-values taxonomy: seven high-level categories (Information Seeking, Wisdom/Knowledge, Duty & Accountability, Civility & Tolerance, Empathy & Helpfulness, Well-being & Peace, Justice/Human & Animal Rights) derived from an integrated review of philosophy, axiology, and STS literature, with sub-values linked by hypernym-hyponym relations. Phase two is a transformer-based classifier: five researchers annotated 6,501 preference pairs using the taxonomy as a codebook, reaching an inter-annotator agreement of 0.85 (Krippendorff's alpha), and those labels trained a RoBERTa sequence classifier with weighted cross-entropy and class weights. That classifier is what produces the value distribution counts that form the paper's evidence, so the taxonomy's categories and the annotation quality are what carry the argument.","core_discovery":"The paper's central claim is that RLHF preference datasets are not value-neutral: they operationalize a skewed distribution of human values, and the skew is systematic across three widely used datasets. Using a taxonomy of seven value families, the authors found that in the 6,501 ground-truth preferences from Anthropic hh-rlhf, Information Seeking (36.96%) and Wisdom/Knowledge (30.75%) were the dominant values, whereas Civility & Tolerance, Empathy & Helpfulness, Well-being & Peace, and Justice/Human & Animal Rights were each below 8 percent, with Justice at 3.12 percent. The classifier extended this pattern: Wisdom/Knowledge was the most common predicted value in all three datasets (78.17% of OpenAI WebGPT, 66.56% of Alpaca GPT-4-LLM, 33.84% of Anthropic chosen and 33.71% of Anthropic rejected preferences), while Justice & Human/Animal Rights was the least represented (0.04%, 0.17%, 1.76%, and 1.76%, respectively). The paper also reports that some 'chosen' responses in the Anthropic data contain unethical content, which it reads as evidence of the need for auditing before reward-model training.","pith_inferences":["The headline cross-dataset percentages assume the classifier transfers from Anthropic-style pairs to WebGPT and Alpaca formats; a more direct test would be to relabel samples from those two datasets and compare the resulting distributions.","If the value imbalances are real, they may partially explain reward hacking and sycophancy failures: a reward model trained on mostly information-seeking preferences has little incentive to develop justice or well-being reasoning.","A natural extension is to use the same pipeline with a culturally adapted taxonomy on non-Western preference data, which the authors explicitly note their Western-oriented taxonomy is not built for.","The framework could be combined with post-training interventions: use audits to construct balanced or value-targeted preference sets, then measure whether classifier-visible value distributions shift downstream model behavior."],"forward_implications":["Any researcher can apply the released ground-truth labels and classified datasets to audit an RLHF corpus before training a reward model.","Models trained on these datasets are likely to be better calibrated for information-retrieval requests than for scenarios that require justice reasoning, empathy, or well-being support.","The presence of unethical chosen responses in the Anthropic data implies that preference datasets can encode harmful affordances even when annotators selected them as preferable, and audits can surface those cases.","Domain-specific value thresholds, such as requiring a medical LLM to reason about medical ethics, become measurable targets rather than vague aspirations.","The reported 80 percent accuracy and 84 percent human-agreement figures, if reproducible, make value auditing a practical complement to existing dataset documentation practices."],"supporting_citations":[{"why":"Supplies the Anthropic hh-rlhf dataset, which provides the 6,501 annotated preference pairs used as ground truth and is the first of the three audited datasets.","marker":"[2]"},{"why":"Supplies the OpenAI WebGPT Comparisons dataset, one of the three corpora whose value distribution is measured.","marker":"[41]"},{"why":"Supplies the Alpaca GPT-4-LLM dataset, the third audited corpus, with instruction-output preferences.","marker":"[42]"}],"fun_headline_variants":["RLHF data prizes knowledge, sidelines justice","Knowledge dominates RLHF data, justice nearly absent","Value Imprint: RLHF datasets skew toward knowledge, away from justice","Auditing RLHF: knowledge dominates, justice lags"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The model trained on 6,501 Anthropic preference pairs assigns accurate value labels to the OpenAI WebGPT and Alpaca GPT-4 datasets even though those datasets were collected in different formats with different domains and likely different label distributions.","fun_headline_variants_meta":{"raw":{"variants":["RLHF data prizes knowledge, sidelines justice","Knowledge dominates RLHF data, justice nearly absent","Value Imprint: RLHF datasets skew toward knowledge, away from justice","Auditing RLHF: knowledge dominates, justice lags"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001122,"raw_usage":{"total_tokens":4730,"prompt_tokens":1067,"completion_tokens":3663,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":683,"completion_tokens_details":{"reasoning_tokens":3598}},"tokens_in":683,"tokens_out":3663,"duration_ms":23021,"temperature":1.0,"reasoning_tokens":3598,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T18:14:00.279040+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have independent annotators label a random sample of a few hundred preferences from OpenAI WebGPT and Alpaca GPT-4-LLM using the paper's taxonomy, and compare the resulting value shares with the model's predictions; if the human-labeled Justice share is not near 0.04 percent for WebGPT, the central imbalance claim would fail as a measurement of those datasets.","supporting_citations":[],"review_version":1}