{"id":"cb8f20f6-0e6f-4937-b445-6f1fe2abd661","arxiv_id":"2505.06356","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A pipeline combining LlavaGuard, Command R+, and Toxic-BERT flags and removes 7,531 image-text pairs from the 558,000-pair LLaVA pretraining dataset.","lead":"This paper filters the LLaVA image-text pretraining dataset with three toxicity detectors and releases a version with 7,531 flagged pairs removed. It maps the flagged content by category, but it does not validate the detections or measure the effect on model safety.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 7,531 count rests on unvalidated detector outputs; no human verification or external benchmark confirms these pairs are toxic, and the paper's own Future Work defers accuracy checking.","rationale":"The reader identified the same gap: detector outputs are treated as ground truth without verification. I agree this is the most load-bearing concern. The paper has merit as a reproducible pipeline description and releases a dataset, but the core quantitative claim—7,531 toxic pairs—rests on three model outputs whose precision and recall on LLaVA pretraining data are unknown. LlavaGuard's taxonomy is designed for general VLM safety, not necessarily for web-scraped image-text pairs; Command R+ is a general-purpose LLM prompted with an undisclosed optimized prompt; Toxic-BERT was fine-tuned on Jigsaw forum comments, a very different text domain. Each stage can introduce systematic error, and no error analysis is provided. The paper's Section 5 explicitly defers user evaluation and comparison with other pipelines, which confirms the authors recognize the missing check. Because the artifact itself is potentially useful and the pipeline is transparently described, the appropriate verdict remains conditional: accept only after adding human validation and preferably a filtered-versus-unfiltered model comparison. I did not find a more fundamental internal inconsistency; the arithmetic of 7,111 + 892 − overlap = 7,531 is not contradicted by any reported data, though the overlap is unstated. The concrete test above would settle whether the count is accurate.","tokens_in":6432,"tokens_out":3468,"duration_ms":31625,"concrete_test":"Conduct a stratified human annotation study: randomly sample 100 pairs from the 7,111 LlavaGuard/Command R+ set (excluding Toxic-BERT overlap), 100 from the 892 Toxic-BERT set (excluding overlap), and 100 from the estimated 472 overlap. Have three annotators independently label each image-text pair as toxic or not using the nine LLaVA Guard categories, then adjudicate disagreements. Report the precision of the 7,531 removed set; if precision is below a pre-specified threshold (e.g., 90%), the claim that these pairs are toxic is unsupported. For recall, additionally sample 1,000 pairs from the retained set and measure how many are judged toxic; a non-trivial miss rate would show the 'mitigated' label is incomplete.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central quantitative claim—7,531 removed toxic pairs—is produced entirely by an unvalidated filter chain. In Section 3.2, LlavaGuard flags 7,600 images; a prompt optimized with Cohere's prompt tuner is then used to instruct Command R+ to identify 'truly unsafe' image IDs, reducing the set to 7,111. Separately, Toxic-BERT flags 892 captions above a hand-set 0.8 confidence threshold. The union, 7,531, is removed and the dataset is called 'toxicity-mitigated.' No human annotation, external toxicity benchmark, or downstream safety evaluation checks any of these numbers. The paper itself states in Section 5 that verifying the accuracy of the dataset is future work. Therefore, the central claim is a procedural assertion ('we ran these models and removed their union') rather than a demonstrated safety improvement. If the detectors are miscalibrated on this distribution—LlavaGuard was trained for general VLM safety, Toxic-BERT on Jigsaw forum comments, and the optimized prompt is undisclosed—the count could substantially misstate true toxicity. The missing validation is load-bearing because every downstream use of the released dataset depends on those 7,531 pairs actually being toxic.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper analyzes the LLaVA image-text pretraining dataset (558,000 pairs) for toxic content using a multimodal pipeline: LlavaGuard 7B flags potentially unsafe images (7,600), Command R+ with a prompt tuned via Cohere's prompt tuner refines this set to 7,111, and Toxic-BERT flags 892 captions above a hand-set 0.8 confidence threshold. The authors take the union, 7,531 unique image-text pairs, remove them, and release a 'toxicity-mitigated' version of the dataset. The paper also reports the distribution of LlavaGuard toxicity categories and Toxic-BERT toxicity types, and outlines future plans for safety evaluation.","tokens_in":6640,"tokens_out":2517,"duration_ms":25542,"significance":"If the claims are validated, the paper would provide a useful open resource: a filtered version of the widely used LLaVA pretraining dataset, plus an analysis of where toxicity appears across visual and textual modalities. The pipeline is clearly described and the release is a concrete community contribution. However, the significance is conditional on the accuracy of the detectors; the paper provides no evidence that the 7,531 flagged pairs are actually toxic by any external standard. The manuscript itself acknowledges in Section 5 that verifying the accuracy of the dataset is future work, which is a serious gap for the central claim.","major_comments":[{"comment":"The central quantitative claim—that 7,531 image-text pairs are 'toxic' and their removal produces a 'toxicity-mitigated' dataset—rests entirely on the outputs of three models, with no human verification, no external toxicity benchmark, and no precision/recall or false-positive analysis for any of the detectors on this specific data distribution. LlavaGuard was trained for general VLM safety and Toxic-BERT on Jigsaw forum comments, so their behavior on web-scraped LLaVA pretraining pairs is unknown. Because the paper's own Section 5 states that verifying the accuracy of the dataset is future work, the 7,531 count is a procedural assertion ('we ran these models and removed their union') rather than a demonstrated safety property.","section":"Section 3.2, Figure 3"},{"comment":"The refinement step in which Command R+ identifies 'truly unsafe' image IDs from LlavaGuard's 7,600 flags uses an optimized prompt built with Cohere's prompt tuner, but the prompt itself is never disclosed and no independent ground truth is used to validate Command R+'s binary judgments. The reduction from 7,600 to 7,111 images could reflect either removal of LlavaGuard false positives or introduction of Command R+ false negatives; without the prompt and a validation set, the step is neither reproducible nor scientifically assessable. This is load-bearing because the final count of 7,111 depends entirely on this unvalidated model judgment.","section":"Section 3.2, Command R+ refinement"},{"comment":"The paper claims that removing 7,531 pairs yields a 'toxicity-mitigated version for LLaVA pretraining,' but provides no downstream evaluation showing that filtering 1.35% of the dataset preserves model capabilities or actually reduces toxic generations. No comparisons on standard benchmarks (e.g., MM-SafetyBench, VHELM, ELITE, which the paper cites as future work) are reported. Without such evaluation, the practical benefit of the released dataset is unquantified, and the removal could in principle degrade the diversity or quality of the pretraining data without any measurable safety gain.","section":"Sections 4 and 6"}],"minor_comments":[{"comment":"There are numerous typographical artifacts: 'LLaV A' appears with an extra space in the title and abstract, 'T oxicity' and 'W e' appear with stray spaces in Figures 1 and 2 and the abstract, and the author footnote 'Work does not belong to position referred in 2' is unclear.","section":"Throughout"},{"comment":"The caption of Figure 2 reads 'n > 0.8: 892' etc., but 'n' is not defined; it presumably denotes the Toxic-BERT confidence score. Please clarify the notation and also state the total number of captions processed.","section":"Section 3.2 and Figure 2"},{"comment":"The paper reports the union of 7,111 image-based flags and 892 text-based flags as 7,531 unique images, but it does not report the overlap between the two sets. Without the contingency table, readers cannot assess how many pairs were flagged by both modalities or whether the multimodal combination actually contributes beyond either detector alone.","section":"Section 3.2"},{"comment":"Some references are incomplete or inconsistently formatted: the Cohere Command R reference lacks a year and a stable URL, and the Detoxify entry is a GitHub link rather than a citable paper. Also, reference [20] in Section 5 is cited as 'Howard et al.' but the reference is by Ratzlaff et al.","section":"References"},{"comment":"The statement that models 'provided a reliable foundation for systematically identifying, understanding, and mitigating toxicity' is not supported by any quantitative reliability measures in this paper, and should be softened or backed by validation results.","section":"Section 3.1"}],"recommendation":"major_revision","confidential_remarks":"The manuscript's main contribution is a dataset artifact, and the load-bearing validation gap is explicitly acknowledged in the Future Work section. I would advise the editor that this gap is fixable within the manuscript's scope by adding a human-annotation study (even on a sample) and a downstream training comparison, but the paper should not be accepted in its current form because the central count is not established. The undisclosed Command R+ prompt also needs to be released for reproducibility."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the useful thing here is the released artifact—a filtered LLaVA pretraining set with 7,531 pairs removed and a category breakdown. The weak point is that the word 'toxic' is doing more work than the evidence supports: every label comes from off-the-shelf detectors that are never validated on this dataset, and the authors themselves defer accuracy checking to future work.\n\nWhat's actually new: nobody had systematically screened the 558K LLaVA pretraining pairs with this combination of detectors and published the resulting filtered set. That's a real, if modest, resource. The pipeline description is clear, the category distribution (sexual content 33%, weapons/substance 22%, nudity 19.5%) is informative, and the paper is honest about its pipeline. The related work on dataset filtering is adequate.\n\nThe soft spot is load-bearing. LlavaGuard was built for general VLM safety, Toxic-BERT for Jigsaw forum comments. Neither is benchmarked on LLaVA's distribution. The Command R+ step uses an 'optimized prompt' from Cohere's prompt tuner, but the prompt is not included, so the 7,111 count is not independently auditable. No human agreement, no precision/recall, no error analysis. The paper calls the result 'toxicity-mitigated,' but without a downstream evaluation—does a model trained on the filtered set behave differently on safety benchmarks, and does it lose capability?—that claim is more procedural than demonstrated. Since only 1.35% of pairs are removed, the safety impact could easily be negligible or even slightly negative if the removal mostly discards diversity. Minor inconsistencies: the abstract says 'image-text pairs' while the method section often says 'images,' and the union of 7,111 image flags plus 892 caption flags is reported as 7,531 'toxic images' without clarifying the modality overlap.\n\nThis is not a bad paper. It is a straightforward data-curation effort with a clear description and an honest statement of limitations. But the central quantitative claim is not yet supported, and the recommended fixes—human-verified sample, released prompt and IDs, a filtered-vs-unfiltered model comparison—are all within reach. I'd send it to peer review, because the artifact could be a useful safety resource, but I'd expect major revision before acceptance. As is, I'd mention it to a colleague working on multimodal safety, but I wouldn't build on the 7,531 number myself.","headline":"Useful dataset artifact, but the 7,531 'toxic' claim rests on unvalidated detectors and no downstream test; worth reviewing but needs validation.","tokens_in":7187,"tokens_out":2990,"would_cite":false,"duration_ms":27554,"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":"A three-detector pipeline removes 7,531 toxic image-text pairs from LLaVA's pretraining set.","keywords":["toxicity detection","multimodal safety","LLaVA","image-text pretraining","dataset filtering","LlavaGuard","Toxic-BERT","responsible AI"],"falsifier":"Take a random sample of, say, 200 of the 7,531 removed pairs and 200 retained pairs, mask the source, and have independent annotators label each pair as toxic or not; if the removed pairs are not clearly more toxic than the retained pairs, or if a large share of removed pairs are benign, then the 7,531 count and the 'mitigated' label are not established.","tokens_in":6209,"feed_emoji":"🛡️","tokens_out":6950,"duration_ms":61526,"temperature":0.7,"pith_summary":"Pretraining datasets assembled from the web carry harmful images and captions, and models trained on them can absorb that content. This paper claims that LLaVA's widely used 558,000-pair pretraining set contains measurable toxicity, and that a multimodal filtering pipeline can find and remove it. Combining an image-safety model (LlavaGuard) with a second-pass reranker (Command R+) and a text-toxicity model (Toxic-BERT), the paper flags 7,531 unique toxic image-text pairs, roughly 1.35 percent of the dataset, and removes them to produce an openly released, toxicity-mitigated version of the pretraining data. If the detection is accurate, the result offers a practical recipe for cleaning web-scale multimodal data before training.","feed_headline":"Filtering removes 7,531 toxic pairs from LLaVA pretraining data","feed_subtitle":"A three-model pipeline catches harmful images and captions, then releases the cleaned set for training.","key_machinery":"The load-bearing mechanism is a two-modality, three-stage filtering pipeline. LlavaGuard 7B is a vision-language safety classifier that rates each image against nine policy categories (hate/harassment, violence, sexual content, nudity, criminal planning, weapons/substance abuse, self-harm, animal cruelty, and disasters/emergencies); Command R+ is a large language model that re-examines each LlavaGuard-flagged image with an optimized prompt to cut false positives; Toxic-BERT is a text classifier fine-tuned to score captions for toxic language and flag those above 0.8 confidence. The pipeline then unions the surviving image flags with the caption flags and removes every unique flagged pair.","core_discovery":"On the paper's own terms, the central discovery is that toxicity in LLaVA pretraining is detectable and removable with off-the-shelf components: LlavaGuard flags 7,600 images under its nine-category safety taxonomy, Command R+ narrows that to 7,111 after a prompt-tuned re-evaluation, and Toxic-BERT flags 892 captions at over 80 percent confidence; the union is 7,531 unique pairs. The distribution is dominated by sexual content (33.1%), weapons or substance abuse (22.0%), nudity (19.5%), and violence, harm, or cruelty (16.3%). Removing these pairs yields a toxicity-mitigated LLaVA pretraining dataset, which the paper releases open source.","pith_inferences":["Editorial inference: the 7,531 count reflects these specific detectors' criteria, not a verified ground truth, so a human audit would likely shift the number in either direction.","Editorial inference: the arithmetic overlap between the image-only flags (7,111) and caption-only flags (892) is only about 472 pairs, suggesting image toxicity and text toxicity are largely disjoint and that both modalities need separate filters.","Editorial inference: a natural testable extension is to pretrain a LLaVA-style model on the mitigated set and compare safety-benchmark behavior plus standard VQA or captioning accuracy against a model trained on the original set; the paper defers that evaluation to future work."],"forward_implications":["A public toxicity-mitigated LLaVA pretraining set now exists, with 7,531 flagged pairs removed, for anyone to use in pretraining.","The same pipeline can be applied to other image-text pretraining collections without retraining detectors, since all three components are off the shelf.","The category breakdown gives dataset curators specific targets: sexual content and weapons or substance abuse account for more than half of the flagged images.","Because only about 1.35 percent of the 558,000 pairs are removed, the filtered set preserves almost all of the original data's volume and the paper's stated richness and diversity."],"supporting_citations":[{"why":"Supplies the LlavaGuard 7B image-safety classifier and the nine-category taxonomy that produced the 7,600 initial image flags.","marker":"[9]"},{"why":"Provides Command R+, the reranking model whose optimized-prompt output narrows the image flags to 7,111.","marker":"[6]"},{"why":"Provides Toxic-BERT, the text classifier whose over-80-percent-confidence output flags 892 captions.","marker":"[8]"},{"why":"Defines the LLaVA pretraining dataset and visual-instruction-tuning setup whose 558,000 pairs are filtered.","marker":"[14]"}],"fun_headline_variants":["7,531 toxic pairs pulled from LLaVA pretraining data","LLaVA pretraining scrubbed: 7,531 toxic pairs gone","New LLaVA dataset drops 7,531 toxic image-text pairs","Off-the-shelf filters strip 7,531 toxic pairs from LLaVA","Cleaning LLaVA: 7,531 harmful pairs removed, data released"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole removal count depends on treating the three detectors' labels as accurate measures of toxicity, since no human verification or independent benchmark is used to confirm them.","fun_headline_variants_meta":{"raw":{"variants":["7,531 toxic pairs pulled from LLaVA pretraining data","LLaVA pretraining scrubbed: 7,531 toxic pairs gone","New LLaVA dataset drops 7,531 toxic image-text pairs","Off-the-shelf filters strip 7,531 toxic pairs from LLaVA","Cleaning LLaVA: 7,531 harmful pairs removed, data released"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000322,"raw_usage":{"total_tokens":1762,"prompt_tokens":848,"completion_tokens":914,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":464,"completion_tokens_details":{"reasoning_tokens":814}},"tokens_in":464,"tokens_out":914,"duration_ms":7532,"temperature":1.0,"reasoning_tokens":814,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T22:44:19.081820+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a random sample of, say, 200 of the 7,531 removed pairs and 200 retained pairs, mask the source, and have independent annotators label each pair as toxic or not; if the removed pairs are not clearly more toxic than the retained pairs, or if a large share of removed pairs are benign, then the 7,531 count and the 'mitigated' label are not established.","supporting_citations":[{"cited_title":"Command R","cited_arxiv_id":null,"evidence_quote":"Provides Command R+, the reranking model whose optimized-prompt output narrows the image flags to 7,111."},{"cited_title":"Detoxify","cited_arxiv_id":null,"evidence_quote":"Provides Toxic-BERT, the text classifier whose over-80-percent-confidence output flags 892 captions."},{"cited_title":"Visual Instruction Tuning, 2023","cited_arxiv_id":null,"evidence_quote":"Defines the LLaVA pretraining dataset and visual-instruction-tuning setup whose 558,000 pairs are filtered."}],"review_version":1}