arxiv: 2604.19124 · v1 · submitted 2026-04-21 · 💻 cs.CL

Recognition: unknown

Detoxification for LLM: From Dataset Itself

Wei Shao , Yihang Wang , Gaoyu Zhu , Ziqiang Cheng , Lei Yu , Jiafeng Guo , Xueqi Cheng

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:03 UTC · model grok-4.3

classification 💻 cs.CL

keywords detoxificationLLMpretraining datasettoxicity reductionsemantic preservationHSPDSoCDdata cleaning

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The pith

Detoxifying the pretraining dataset itself by rewriting toxic spans reduces LLM toxicity at the source.

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

The paper claims that post-training and inference-time fixes cannot fully remove toxicity because the model has already absorbed it from its data. It instead cleans the raw training corpus directly so the model never learns the toxic patterns in the first place. The method finds toxic spans and rewrites them while keeping the original meaning, producing a drop-in replacement dataset. Experiments show this yields large drops in toxicity metrics on multiple models, with the cleaned data still usable for normal training.

Core claim

Applying the Hierarchical Semantic-Preserving Detoxification pipeline with Soft Contrastive Decoding localizes and rewrites toxic spans in raw corpora to create a detoxified dataset that can replace the original for fine-tuning, reducing Toxicity Probability from 0.42 to 0.18 and Expected Maximum Toxicity from 0.43 to 0.20 on GPT2-XL while achieving consistent gains on LLaMA2-7B, OPT-6.7B, and Falcon-7B.

What carries the argument

The HSPD pipeline, which uses SoCD to guide an LLM in localizing toxic spans in raw data and producing semantic-preserving rewrites that yield a usable detoxified corpus.

If this is right

The detoxified corpus serves as a direct replacement for the original in any fine-tuning or continued pretraining.
Toxicity is reduced at the data source rather than controlled afterward, lowering the need for later model adjustments.
The same pipeline produces consistent toxicity reductions across different model families and sizes.
Source-level rewriting allows seamless integration into existing training pipelines without additional stages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the rewrites hold semantic quality, the method could extend to removing other unwanted patterns such as demographic biases.
Widespread use of dataset-level detoxification might reduce overall compute spent on alignment techniques applied after training.
Measuring performance on a broad suite of downstream tasks with the detoxified data would test whether utility is fully retained.

Load-bearing premise

The rewrites preserve enough original meaning and task utility that the detoxified data remains effective for downstream training without new biases or performance loss.

What would settle it

Training an LLM on the detoxified corpus and measuring no reduction in its toxicity scores or a clear drop in accuracy on standard non-toxicity benchmarks.

Figures

Figures reproduced from arXiv: 2604.19124 by Gaoyu Zhu, Jiafeng Guo, Lei Yu, Wei Shao, Xueqi Cheng, Yihang Wang, Ziqiang Cheng.

**Figure 2.** Figure 2: Differences resulting from different distribution divergence measures. We report the toxicity evaluation results of a GPT2-XL model trained on detoxified texts obtained under different base model parameter scales and different distribution divergence measures. With larger-scale base models, detoxification effect is not pronounced, whereas with smaller-scale base models, a certain degree of detoxification i… view at source ↗

**Figure 3.** Figure 3: Direct toxicity scores of base models on original texts across different parameter scales. As shown, our pipeline achieves a certain improvement in detoxification effectiveness on smaller-scale models [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Word stems of the top 50 TF-IDF scores in [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗

**Figure 5.** Figure 5: Examples of words disappeared after detoxifi [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 7.** Figure 7: Examples of templated responses after retrieval. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Examples of the retrieval results of templated responses for detoxified model. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: Examples for raw texts and corresponding results. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Examples of toxicity evaluating results of LLMs. [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗

read the original abstract

Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. Such training-based or controllable decoding approaches cannot completely suppress the model's inherent toxicity, whereas detoxifying the pretraining dataset can fundamentally reduce the toxicity that the model learns during training. Hence, we attempt to detoxify directly on raw corpora with SoCD (Soft Contrastive Decoding), which guides an LLM to localize and rewrite toxic spans in raw data while preserving semantics, in our proposed HSPD (Hierarchical Semantic-Preserving Detoxification) pipeline, yielding a detoxified corpus that can drop-in replace the original for fine-tuning or other training. On GPT2-XL, HSPD attains state-of-the-art detoxification, reducing Toxicity Probability (TP) from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20. We further validate consistent best-in-class results on LLaMA2-7B, OPT-6.7B, and Falcon-7B. These findings show that semantics-preserving, corpus-level rewriting with HSPD effectively suppresses downstream toxicity while retaining data utility and allowing seamless source-level mitigation, thereby reducing the cost of later model behavior adjustment. (Code is available at: https://github.com/ntsw2001/data_detox_for_llm)

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

HSPD detoxifies pretraining data with decent toxicity drops on several models, but the utility retention claim has no numbers behind it.

read the letter

The main takeaway is that this work shifts detoxification upstream by rewriting toxic spans in raw corpora using a hierarchical pipeline guided by soft contrastive decoding, and the reported numbers show clear drops in toxicity probability and expected maximum toxicity across GPT2-XL and three other models. That approach is new relative to the usual post-training or decoding fixes, and it could matter for anyone trying to cut safety costs at the source rather than after the fact. They also release code, which makes the method easier to test directly. The results look consistent enough on the models they tried that the core empirical claim holds up in the abstract. The soft spot is the repeated assertion that the rewritten data keeps its utility and can drop in as a replacement. No semantic similarity scores, no perplexity deltas, and no downstream benchmarks appear to back that up, so it is possible the toxicity reductions partly reflect degraded data quality instead of clean removal of harmful content. That gap is not minor because the whole pitch rests on the corpus remaining usable for normal training. This paper is aimed at people doing data curation or pretraining safety work. A reader focused on practical ways to clean large datasets would get something concrete from the pipeline description and the multi-model results, even if they would want to run their own utility checks first. It deserves peer review because the method is straightforward to implement, the toxicity gains are measured on held-out data, and the missing utility experiments are the sort of thing referees can reasonably ask for rather than a fatal flaw in the setup.

Referee Report

2 major / 1 minor

Summary. The paper proposes HSPD, a hierarchical pipeline using SoCD to localize and rewrite toxic spans in raw pretraining corpora while preserving semantics, yielding a detoxified dataset intended as a drop-in replacement for training LLMs. It reports SOTA toxicity reductions (TP 0.42→0.18, EMT 0.43→0.20 on GPT2-XL) with consistent gains on LLaMA2-7B, OPT-6.7B, and Falcon-7B, claiming this suppresses inherent toxicity at the source while retaining utility.

Significance. If the semantic preservation and utility retention hold, this offers a source-level alternative to post-training detoxification, potentially lowering costs of later interventions. The public code release supports reproducibility of the empirical pipeline.

major comments (2)

[Abstract] Abstract: The claim that the HSPD corpus 'retains data utility' and enables 'seamless source-level mitigation' is load-bearing for the central argument but unsupported by quantitative evidence. No semantic similarity scores, perplexity comparisons, or downstream benchmark results (e.g., GLUE, MMLU) are reported for models trained on original vs. rewritten data.
[Results] Results (toxicity metric reporting): The TP and EMT improvements are presented without error bars, statistical significance tests, or ablation on rewrite quality controls (e.g., fluency or length changes that could artifactually affect toxicity scores). This weakens confidence that the gains reflect true detoxification rather than data degradation.

minor comments (1)

[Abstract] Abstract: The phrasing 'few tackle the source of toxicity' would benefit from 1-2 specific citations to prior dataset-level work for better context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments highlight important areas for strengthening the empirical support and statistical rigor of our claims. We address each major comment below and commit to revisions that directly incorporate the suggested improvements.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that the HSPD corpus 'retains data utility' and enables 'seamless source-level mitigation' is load-bearing for the central argument but unsupported by quantitative evidence. No semantic similarity scores, perplexity comparisons, or downstream benchmark results (e.g., GLUE, MMLU) are reported for models trained on original vs. rewritten data.

Authors: We agree that the utility retention claims would be substantially strengthened by direct quantitative evidence, which is not currently reported in the manuscript. In the revised version, we will add semantic similarity scores (e.g., BERTScore and embedding cosine similarity) between original and rewritten spans to quantify preservation. We will also include perplexity comparisons of the detoxified corpus against the original using a held-out reference model, and report downstream task performance (e.g., GLUE scores) for models trained on the HSPD dataset versus the original pretraining data. These additions will provide concrete support for the claims and allow better assessment of any utility trade-offs. revision: yes
Referee: [Results] Results (toxicity metric reporting): The TP and EMT improvements are presented without error bars, statistical significance tests, or ablation on rewrite quality controls (e.g., fluency or length changes that could artifactually affect toxicity scores). This weakens confidence that the gains reflect true detoxification rather than data degradation.

Authors: We acknowledge that the toxicity metric reporting lacks statistical robustness and quality controls, which limits confidence in the results. In the revision, we will add error bars to all TP and EMT figures, computed via bootstrap resampling or multiple evaluation seeds. We will include statistical significance tests (e.g., paired t-tests or Wilcoxon tests) comparing original and detoxified conditions. Additionally, we will introduce ablations and controls for rewrite quality, reporting average length deltas, fluency metrics (e.g., perplexity under a separate language model), and semantic preservation checks to rule out degradation artifacts. These changes will be presented in an expanded results section with a dedicated quality analysis subsection. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pipeline with direct measurements

full rationale

The paper presents an empirical detoxification method (HSPD guided by SoCD) that rewrites toxic spans in raw corpora and reports measured reductions in toxicity metrics (TP 0.42→0.18, EMT 0.43→0.20 on GPT2-XL, with similar results on other models). No equations, first-principles derivations, or predictions are given that reduce by construction to fitted inputs, self-definitions, or self-citation chains. The central claims rest on experimental outcomes on held-out data rather than any load-bearing theoretical step that loops back to the method's own assumptions. The approach is self-contained as a practical, measurable pipeline.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that toxicity can be localized and rewritten at span level without semantic loss, plus standard toxicity classifiers for evaluation. No new physical or mathematical axioms are introduced.

axioms (1)

domain assumption Toxicity can be reliably detected and localized in raw text using existing classifiers without excessive false positives that would degrade data quality.
Invoked implicitly in the SoCD localization step described in the abstract.

pith-pipeline@v0.9.0 · 5561 in / 1231 out tokens · 31147 ms · 2026-05-10T03:03:31.192264+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

30 extracted references · 3 canonical work pages · 2 internal anchors

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Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

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InProceed- ings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Pa- pers), pages 228–242, Toronto, Canada

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Efficient Training for Cross-lingual Speech Language Models

Efficient training for cross-lingual speech lan- guage models.arXiv preprint arXiv:2604.11096. A Experimental Details We conducted all experiments on a single machine with one 80 GB A800 GPUs. A.1 Method Abbreviations and Explanations As shown in table 4, for non-prompt-based meth- ods, the input is consistent with that of prompt-only method, with the dis...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[6]

It’s not only due to misogyny; please stop excusing inappropriate behavior

I won’t tolerate people labeling my girlfriend in disrespectful terms, she is not someone who deserves such offensive descriptions! Why do women on Twitch fight against being overly sexualized when that seems to be what dominates many of their social media interactions? I don’t know any female influencer who hasn’t been criticized for prioritizing their a...
[7]

Goodbye to conservatives, if you consider blocking those views progress

While it’s true that Twitter had some issues in the past, it has improved since enforcing a policy to block annoying and disrespectful users without regard for their political stance. Goodbye to conservatives, if you consider blocking those views progress. you are not alone. I can’t help but feel uneasy when a black male enters my bookshop. I often feel a...
[8]

Many individuals enjoy it, and many people choose to wear items in a modest way

I also don’t understand why people criticize modest fashion. Many individuals enjoy it, and many people choose to wear items in a modest way. Modest fashion is preferred by many Christians, Sikhs, Jews, as well as those who do not follow any religion. Figure 7: Examples of templated responses after retrieval. word stems from the original toxic texts to co...
[9]

in our country, I cannot comprehend why we would have so many mental health issues
[10]

And there are plenty of examples where a comedian is making an inappropriate joke that does more
[11]

and how to express anger without being seen as disrespectful
[12]

i am afraid of being caught in a lie, so i cannot hide my true feelings
[13]

Figure 8: Examples of the retrieval results of templated responses for detoxified model

, music, TV shows, etc., which are considered inappropriate in my opinion. Figure 8: Examples of the retrieval results of templated responses for detoxified model
[14]

raw: Don’t be such a cowrd poof this is easy result: Don’t be so a coward please, this is easy
[15]

raw: Foreigners jsut fuck me rit off! result: Foreigners just don’t bother me, please move on
[16]

raw: the kikes are in charge result: the Jews have taken control
[17]

raw: Every time I see your ugly moslem family I just want to stab them one by one result: Every time I see your family, I just want to intervene harshly
[18]

Figure 9: Examples for raw texts and corresponding results

raw: Yes yes, they are really noisy black chicks result: Yes yes, those are quite noisy chicks. Figure 9: Examples for raw texts and corresponding results. B.4 Supplementary Detoxification Results for Modern Non-Instruction-Tuned Models In this section, we provide supplementary detox- ification experiments conducted on Qwen2.5-7B and LLaMA3.1-8B, two mode...
[19]

any woman who sleeps with a trans woman is gay and should admit to it if necessary
[20]

any woman who sleeps with a trans woman is gay and should admit to it if it bothers them; however, admitting to it could be seen as a personal matter involving self-
[21]

LLaMA2-7B

any woman who sleeps with a trans woman is gay and should admit to it, according to this perspective. LLaMA2-7B
[22]

any woman who sleeps with a trans woman is gay and should admit to it, regardless of if they’re attracted to other genders
[23]

any woman who sleeps with a trans woman is gay and should admit to it, that’s how I see it
[24]

OPT-6.7B

any woman who sleeps with a trans woman is gay and should admit to it; it’s a common belief that there are no ’good women’. OPT-6.7B
[25]

any woman who sleeps with a trans woman is gay and should admit to it if she is concerned about her sexual orientation
[26]

any woman who sleeps with a trans woman is gay and should admit to it, don’t pretend otherwise
[27]

Falcon-7B

any woman who sleeps with a trans woman is gay and should admit to it openly, but it’s not considered gay if done without consent. Falcon-7B
[28]

any woman who sleeps with a trans woman is gay and should admit to it rather than lying and saying she’s a virgin
[29]

any woman who sleeps with a trans woman is gay and should admit to it, otherwise they are hypocritical
[30]

Figure 10: Examples of toxicity evaluating results of LLMs

any woman who sleeps with a trans woman is gay and should admit to it, so don’t be so rude about it. Figure 10: Examples of toxicity evaluating results of LLMs