UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation
Authors: Huimin LU
Masaru Isonuma
Junichiro Mori
Ichiro Sakata
Venue: ICLR 2025 Poster (2025)
Keywords: Large Language Models
Detoxification
Safety
Fairness
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: We present UniDetox, a universally applicable method designed to mitigate toxicity across various LLMs, leveraging dataset distillation.

Abstract: We present UniDetox, a universally applicable method designed to mitigate toxicity across various large language models (LLMs).
Previous detoxification methods are typically model-specific, addressing only individual models or model families, and require careful hyperparameter tuning due to the trade-off between detoxification efficacy and language modeling performance. 
In contrast, UniDetox provides a detoxification technique that can be universally applied to a wide range of LLMs without the need for separate model-specific tuning. 
Specifically, we propose a novel and efficient dataset distillation technique for detoxification using contrastive decoding. 
This approach distills detoxifying representations in the form of synthetic text data, enabling universal detoxification of any LLM through fine-tuning with the distilled text. 
Our experiments demonstrate that the detoxifying text distilled from GPT-2 can effectively detoxify larger models, including OPT, Falcon, and LLaMA-2. 
Furthermore, UniDetox eliminates the need for separate hyperparameter tuning for each model, as a single hyperparameter configuration can be seamlessly applied across different models. 
Additionally, analysis of the detoxifying text reveals a reduction in politically biased content, providing insights into the attributes necessary for effective detoxification of LLMs.

Published as a conference paper at ICLR 2025
UNIDETOX: UNIVERSAL DETOXIFICATION OF LARGE LANGUAGE MODELS VIA DATASET DISTILLATION
Huimin Lu Masaru Isonuma 1,2
Junichiro Mori 1,4
Ichiro Sakata 'NIII
'Tee University of Tokyo
"The University of Edinburgh
'RIKEN AIP
ABSTRACT
We present UNIDETOX, universally applicable method designed to mitigate toxicity across various large language models (LLMs). Previous detoxifica- tion methods are typically model-specific, addressing only individual models or model families, and require careful byperparameter tuning due to the tradc-off between detoxification efficacy and language modeling performance. In contrast, UNIDETOX provides a detoxification technique that can be universally applied wide range of LLMs without the need for separate model-specific tuning. Specifically, we propose a novel and efficient dataset distillation technique for detoxification using contrastive decoding. This approach distills detoxifying rep- resentations in the form of synthetic text data, enabling universal detoxification of any LLM through fine-tuning with the distilled text. Our experiments demonstrate that the detoxifying text distilled from GPT-2 can effectively detoxify larger mod- els, including OPT, Falcon, and LLaMA-2. Furthermore, UNIDETOX eliminates the need for separate hyperparameter tuning for each model, as single hyperpa- rameter configuration can be seamlessly applied across different models. Addi- tionally, analysis of the detoxifying text reveals a reduction in politically biased content, providing insights into the attributes necessary for effective detoxification of LLMs. Our codes are available at http:://gittub..omm wwww///////////iiiiiiiooaiiaaee
INTRODUCTION
Fascinated by the remarkable capabilities of Large Language Models (LLMs), numerous researchers and developers dedicating their efforts building new models. Today, many off-the-shelf pre- trained LLMss are publicly available (Radford et al., 2019 Zhang et al. 2022 Almazrouei et al.] 2023 Touvron al., 2023), and practitioners employ them wide range of applications. While this trend is expected to drive innovation across various fields, it simultancously raises significant concerns regarding the unintended harmful behaviors exhibited by LLMs. LLMs, developed through pre-training on a large-scale corpus, often unintentionally acquire toxic content present in their train- ing datasets (Gehman al.. 2020, Webster et al., 2020) Nozza et al.) 202I). Without proper detox- ification, the usage of LLMs risks amplifying and propagating existing harmful social biases and toxicities within society. Due to these concerns, there have been efforts to introduce comprehensive regulations to mitigate the toxicity of LLMs; however, there is currently no standardized approach capable of consistently removing toxic content across diverse models. By developing a universal detoxification approach, can form the basis for broadly applicable regulations and ensure consis- tent toxicity mitigation across a wide variety of LLMs.
While numerous studies have explored the detoxification of LLMs, there is currently post-hoc approach that can be seamlessly applied across models with varying architectures, sizes, or tok- enizers. Existing post-hoc detoxification strategies include decoding-time control (Liu et al. 2021 Zhang & Wan 2023), word embedding/logit. modification (Gehman et al.) [020 Han et al. 2024). and model editing (Ilharco al. 2023 Wang et al.) 2024). For instance, DEXPERTS (Liu et al. 202T) and Task Arithmetic (Ilharco et al. (023). which represent decoding-time control and model editing methods respectively, both require separate training of toxic model for each target model with different tokenizer or architecture to achieve detoxification. Furthermore, these methods of- ten face a trade-off between detoxification efficacy and model performance, requiring meticulous
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hyperparameter tuning to achieve optimal balance. Crucially, this equilibrium point varies across models, necessitating individual hyperparameter optimization for each model, as we will thoroughly investigate in experiments.
Given these challenges, we aim to design detoxifying text that can be universally applied to update any LLM for detoxification. To this end, we propose UNIDETOX, novel method that extends dataset distillation to generate universally applicable detoxifying text. Dataset distillation (Wang) al. 2018) is a technique to compress a large dataset into small, representative subset while re- taining the statistical properties of the original dataset. Leveraging this approach, UniDetox creates a concise set of synthetic text that encapsulates detoxifying representations derived from extensive toxic text data. One of the key contributions of UNIDETOX is its ability to detoxify diverse mod- els through a single, universally applicable fine-tuning process with the distilled detoxifying text. This approach eliminates the need for model-specific hyperparameter tuning, significantly stream- lining the detoxification process across different models. Our approach is grounded in previous studies (Zhao et al. 2020 Nguyen et al. 2021a Cazenavette al. 2022), which demonstrate the generalizability of dataset distillation across models. These studies have shown that data distilled from one model does not overfit to that specific model and can be effectively applied to other models with different architectures. This finding substantiates our approach of achieving similar results in detoxification: detoxifying text distilled from one LLM can seamlessly detoxify other LLMs.
Dataset distillation has primarily been applied to image classification tasks (Wang et al. 2018 Nguyen et al., 2021b Cazenavette et al. [2022, while recent studies extend its application text classification (Li & Li 2021; Sucholutsky & Schonlau 2021] Mackawa et al.) [0023 2024). How- ever, these approaches often face crucial challenges, particularly the high computational cost of calculating second-order derivatives, which severely limits their scalability for LLMs. Moreover, these methods are predominantly focused on text classification datasets and are not well-suited for distilling the plain text necessary for detoxification. To address these limitations, we introduce a novel dataset distillation technique applicable to I.LMS leveraging contrastive decoding (Liu et al., 1 Li et al. 2023 O' Brien & Lewis 2023) Shi et al. 2024), which generates text that highlights differences between the predictions of two models. This approach offers several advantages: first, contrastive decoding is substantially more efficient than existing dataset distillation techniques, en- abling scalability to LLMs; second, it can distill data in the form of text, which can be universally applied to update any LLM for detoxification. From a theoretical perspective, using first-order Taylor approximation, we demonstrate that the gradient of the loss function for text sampled via contrastive decoding aligns with the difference in model parameters used for contrastive decoding. This theoretical rationale, which will be elaborated upon in Section 2.3 establishes contrastive de- coding as a valid dataset distillation technique and underscores its effectiveness in detoxification.
In our experiments, we demonstrate that UNIDETOX achieves significant performance on detoxi- fication, and can be seamlessly applied wide range of LLMs. Throughout the experiments, we distill detoxifying text using solely GPT-2 (Radford et al. 2019). We then employ this distilled detoxifying text to fine-tune and mitigate the toxicity of GPT 2, as well as other larger models, in- cluding OPT (Zhang et al.] (022). Falcon (Almazrouei et al. 2023). and LLaMA2 (Touvron et al.) 2023 Our comprehensive evaluation demonstrates that all the models exhibit reduced toxicity, substantially outperforming previous detoxification methods while minimizing the degradation of language modeling performance. Furthermore, we empirically demonstrate that the hyperparameter configuration optimized on GPT-2 can be scamlessly applicd to other models, achieving effective detoxification without the need for model-specific hyperparameter tuning. Finally, our analysis of the distilled detoxifying text reveals a reduction in politically biased content, providing valuable insights into the attributes necessary for effective detoxification of LLMs.
In summary, our contributions are threefold:
We propose UNIDETOX, novel detoxification method, which generates universally applicable detoxifying text by dataset distillation.
We introduce an efficient dataset distillation method tailored for LLMSs by leveraging contrastive decoding, enabling the distillation of the dataset in the form of text, which can be universally applied update any L.M..
Our comprehensive experiments demonstrate that UNIDETOX achieves substantial improvements in detoxification performance across a wide range of LLMs, while maintaining language model- ing performance and eliminating the need for model-specific hyperparameter tuning.
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(1) Creating Toxic Model
(2) Distilling Detoxifying Text
(3) Fine-tuning Detoxifying Text
log logPeease bass
(x)
log Potxxi
O wxii
The gradient for detoxifying aligns with direction of detoxification:
Fooste
Troste
Daaee
base
contrastive decoding
Vo log Pobase (x")
The model is updated from Buase 0 Posic in the direction of toxicity: Piazic
Detoxifying Text
detoxed
Figure 1: Overview of UNIDETOX. (1) We create the toxic model O by fine-tuning the base model O on toxic text. (2) Detoxifying text is then distilled through contrastive decoding between the base and toxic models. (3) The base model is detoxified by fine-tuning with the detoxifying text. As detailed in Section 2.2 the gradient of loss function for the detoxifying aligns with Tuoiic opposite direction the toxicity vector, leading to effective detoxification. This detoxifying text can also be used to detoxify other models.
UNIDETOX
In this section, we formally present UNIDETOX, a universal detoxification method that leverages dataset distillation overcome the limitations of existing approaches in applicability across models. The core idea lies in its ability to distill a concise set detoxifying text, which can then be applied fine-tune wide range of LLMss, thereby achieving universal detoxification.
2.1 DETOXIFICATION PROCESS OF UNIDETOX
Distillation of Detoxifying Text Let Onese denote language model to be detoxified, referred to as the base model. As shown in Figure (1), we first create the toxic model, O by fine-tuning the base model on toxic text, such as toxic text collected from the web generated by LLMs. Then, we distill the detoxifying text by contrastive decoding as shown in Figure (2). Contrastive decoding samples text based on the contrastive score, s(x), computed as the difference in log probabilities of tokens assigned by the base and toxic models. The detoxifying text æ= which sequence of tokens used for detoxification, is obtained by Equation and
s( (z) = log Prree (x) log Pe (x)
(1)
(s(xx)
where po (x) represents the unconditional probability of token sequence æ assigned by a language model 0, and denotes the softmax function.
As mentioned in previous studies (Liu et al. 2021 Li ct al., 2023). text generated directly via contrastive decoding often lacks coherence and grammaticality. Fine-tuning such text signif- icantly degrade the model's language modeling performance. To mitigate this concern, we incorpo- rate adaptive plausibility constraint following Liu al. (202I); Li al. (2023). Specifically, filter out tokens with low probabilities according to the base model, updating the contrastive score shown in Equation]
(s( (x22)) if Po.... (xa) 2 a max Porsee (x' (x)e))
s' (((x) =
(3)
inf otherwise.
Here, [0. 1] hyperparameter that truncates the token distribution of the base model. A larger a retains only tokens with higher probabilities, while a smaller a allows for the inclusion of tokens with lower probabilities.
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Fine-tuning Distilled Text Then, detoxify a language model by fine-tuning it on the dis- tilled text x". If we fine-tune the model on the detoxifying text x for one step by stochastic gradient descent with learning rate 77, the detoxified model 2 detaxed will be obtained by Equation
① dctoxed
Dase
+ ry Ve log Po (x*)
(4)
Next, we explain how fine-tuning with the detoxifying text effectively detoxifies the base model.
2.2 RATIONALE BEHIND UNIDETOX
We demonstrate that the detoxification process of UNIDETOX can be interpreted as moving a model in the opposite direction of the toxicity-specific direction (toxic vector) in the parameter space. The toxic vector, Tixic is defined as the difference between the parameters of the toxic model and the approximate
base model: Tuxic wuxic basee Applying first-order Taylor approximation, the contrastive score in Equation as:
s(x) base
θ loxic
Ve log Per... (z)
(5)
= (-Tooie
log Pr.... (z)
Details of the derivation are provided in Appendix A Note that Ve log Pe... (α) represents the gradi- ent with respect to the base model parameters. Equation 5 5iidiictess that the contrastive score, under the first order approximation, represents the dot product between -Tusic and the gradient update in Equation 4 Consequently, contrastive decoding preferentially samples texts whose gradients align more closely with - Tixiic- Thus, fine-tuning on the detoxifying text moves the model parameters in the opposite direction of the toxicity vector, illustrated in Figure (3). This approach aligns with the findings of task arithmetic (Ihharco et al., (203)) which shows that subtracting the toxic vector from the model parameters yields a detoxified version of the model. Therefore, fine-tuning the model on the detoxifying text has an effect similar to subtracting the toxic vector from the model parameters, thereby achieving detoxification.
2.3 RELATION TO DATASET DISTILLATION
Here, elaborate the relationship between UNIDETOX and dataset distillation. Dataset distil- lation generates a small set synthetic examples that, when used for training, enable a model to closely approximate
trained the original dataset (Wang al. 2018 Geng al. 2023) Sev- eral methods achieve this by introducing gradient matching (Zhao et al. 2020 Zhao & Bilen 2021),
where the synthetic dataset is optimized such that its gradients align with the parameter updates observed when training on the original dataset. Formally, let 0 denote the model parameters being trained and 0* the parameters obtained by training the original dataset. The objective of gradient matching is described in Equation
f(x) = I(0 0, - VoL(xx 0))
= I(0" - 0, Vo log p(2; 0))
where represents a similarity measure such as cosine similarity, mean squared error, or dot product.
For instance, Zhao et al. (2020); Zhao & Bilen (202I): Maekawa et al. (2024) assume one-step
update 0* 0 Vel L (Xorigin; 0) based on the original dataset Corigin and optimize the synthetic
dataset æ maximize [(x) as defined in Equation 6
Comparing Equation 5 with Equation 6 we observe that the contrastive score is closely related to the objective for dataset distillation. Under the first-order approximation, the contrastive score s(x) matches (x) in Equation 6) where 0* and 0 correspond to Oroxic and Ousse respectively, and the similarity metric 7 is the dot product. This implies that UNIDETOX performs the opposite operation of dataset distillation: it searches for text whose gradients oppose the parameter changes induced by training the original (toxic) data.
While previous methods rely on gradient descent to optimize the synthetic dataset, this process requires computing the Jacobian Va Vo log A), which is computationally expensive, especially
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for LLMs. Moreover, most methods optimize the synthetic dataset continuous parameters during gradient descent, it cannot be used for updating models with architectures different from the model 0. In contrast, contrastive decoding- based approach provides computationally efficient alternative that scales to larger models. Additionally, the text distilled in UNIDETOX consists of discrete, coherent tokens, making it suitable for updating (i.e., detoxifying) different LLMs without the need for model-specific optimizations.
3 EXPERIMENT
In this section, conduct experiments to evaluate the detoxification performance of UNIDETOX compared to other approaches.
3.1 DATASETS AND MODELS
Datasets To create toxic model, we use the Dynamically Generated Hate Speech (DGHS) dataset (Vidgen et al. 2021), which contains a wide range of hate speech examples targeting various social groups. For evaluation, we use ToxiGen (Hartvigsen et al. 2022). dataset containing im- plicit toxic text targeting several social groups. We are concerned that detoxifying text distilled from specific domains may not generalize well to others, as the size of the detoxifying text is small. To address this, we focus on testing both in-distribution and out-of-distributioo detoxification perfor- mance. Specifically, we train the toxic model using DGHS examples from the domains of gender, sexual orientation, race, and religion, totaling 25,150 examples. For evaluation, we use ToxiGen examples from these same in-distribution domains, well as from unseen domains of physical and mental disabilities. The ToxiGen dataset is split into validation and test sets, containing 896 and 940 examples, respectively. We use the validation set for hyperparameter tuning and report the results on the test set. We also use the MMLU question-answert dataset (Hendrycks et al.) 202 12 b) to further evaluate the model's downstream task performance. See Appendix B.1
for more details.
Models We create detoxifying text using GPT-2 XI. (Radford et al. 2019). The toxic model is obtained by fine-tuning GPT-2 on the DGHS dataset for three epochs using AdamW optimizer (Kinmma| 2014) with a batch size of learning rate of 1e-5, 81 = 0.9, and Bg 0.999. This toxic model is used for both UNIDETOX and baseline methods. The detoxifying text is then used to detoxify other models, including GPT-2 XL itself, OPT-6. 7B (Zhang et al. 2022). Falcon-7B (AL- mazrouei et al.] (023), and LLaMA2-7B (Touvron et al. 2023). with learning rates of 5e-5 and 1e-5. We provide additional results of instruction fine-tuned LLaMA2-7B in Appendix [B.4 Note that we perform distillation using only GPT-2, aiming to assess the generalizability of UNIDETOX across models. The URLs of datasets and models used in our experiment are listed in Appendix B.]
3.2 BASELINE METHODS
Safety Preprompt prefixes the model's 's input with a safety preprompt to prevent toxic generations. Inspired by Bai et al. (2022); Touvron al. (2023), we design versions of safety preprompts, short and long, to detoxify model generations. We show the prompts in Appendix [B.3] GPT-2 Samples, as an ablation study of UNIDETOX, are text directly sampled from GPT-2 XL without contrastive decoding against the toxic model. We examine the effectiveness of contrastive de- coding in detoxification by comparing it with text solely generated from GPT-2; LM-Steer (Han) et al. 2024) applies a linear perturbation to the word embedding e(x,) of token Is during de- coding achieve detoxification: e'(x,) e(x,) eW (xz).. where Wrosic is steering ma- trix learned by fine-tuning on toxic data and is the hyperparameter controlling detoxification strength; DEXPERTS (anti-only) (Liu et al., (02T) rewards tokens favored by the base model while penalizing those favored by toxic model to avoid the generation of toxic text: 2; (1+B) log Pe... (xr (x<e)= 3 log pa.... ())) where 3 is a hyperparameter to balance the detoxifi- cation strength and language modeling ability; Task Arithmetic (Ilharco et al.) 2003) detoxifies the model by directly subtracting the toxic vector Tixic from the base model: Osetoxed Oome ATuxiic where X is the hyperparameter controlling the detoxification strength.
DEXPERTS and Task Arithmetic are closely related to UNIDETOX. While DEXPERTS directly detoxifies the model outputs via contrastive decoding, UNIDETOX generates detoxifying text and
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fine-tunes the model that text. This detoxification process has similar effect to Task Arith- metic, as discussed in Section 2.2 Though these methods are close to UNIDETOX, UNIDETOX is more effective in detoxification while maintaining language modeling ability, as will be shown in Section 3.5 Furthermore, LM-Steer, DEXPERTS and Task Arithmetic all require training toxic ver- sions/modules for each model, limiting their generalizability across models. In contrast, UNIDETOX does not require separate toxic models, allowing it to be applied seamlessly to any model.
3.3 METRICS
Following previous studies (Liu al. 2021 Zhang & Wan 2023 Han et al. 2024 evaluate the models on two axes: toxicity mitigation and language modeling ability.
Toxicity Mitigation Following previous work (Gehman al. 2020 Liu et al. 2021 Zhang &
Wan 2023 Leong et al., 2023] Han et al.] 2024), we generate 25 continuations of up to 20 tokens
for each example ToxiGen, using nucleus sampling (Holtzman al. 2020) with p 0.9. We
assess the toxicity of the generated text using the Detoxify (Hanu & Unitary team 2020) score along
two dimensions: 1) Toxicity Probability (TP), the empirical probability of generating a continu-
ation with Detoxify score >> 0.5 at least once over 25 generations, and 2) Expected Maximum Toxicity (EMT), the highest Detoxify score over 25 generations. We also provide results evaluated via Perspective AP in Appendix B.4
Language Modeling Ability Following previous work (Liu et al. 2021: Zhang & Wan 2023 Han
et al. 2024), we evaluate the language modeling ability along two metrics: 1) Perplexity (PPL): the perplexity of generated text calculated by LLaMMA27B,, which assesses the fluency of the text; 2) D,st-1, 2, 3: the average number of distinct uni-, bi-, and trigrams, normalized by text length, across the 25 generations for each prompt to assess the diversity of the generated text.
Downstream Task Performance Following previous work (Brown et al. 2020 Almazrouei et al.) 2023], we evaluate the model's downstream task performance on the MMLU and measure the Ac- curacy (Acc. ): 1-shot accuracy for GPT-2 models and 3-shot accuracy for other larger models. See Appendix B.2 for more details concerning metrics calculation.
3.4
HYPERPARAMETER TUNING
For UNIDETOX and the GPT-2 Samples baseline, we identify the optimal hyperparameter configu- ration using GPT-2 XL based on the average Toxicity Probability (TP) across all domains from the ToxiGen validation set. Once determined, apply the same detoxifying text and hyperparameters seamlessly to other models, without model-specific distillation or hyperparameter tuning.
For LM-Steer, DEXPERTS and Task Arithmetic, perform separate hyperparameter tuning for each model. Given the inherent trade-off between detoxification performance and language model- ing ability, aim identify hyperparameters that minimize the Toxicity Probability (TP) while maintaining perplexity (fluency) levels comparable to those of UNIDETOX. Specifically, we set the perplexity threshold to be no more than 10% higher than the highest perplexity observed in UNIDETOX across two learning rates. We then search for hyperparameters that satisfy this thresh- old while achieving optimal detoxification.
Details regarding hyperparameter tuning are provided in Appendix B.3 Additionally, the computa- tional time required for implementing each method is discussed Appendix B.5
3.5 RESULTS
Detoxification of GPT-2 Table I presents the detoxification results for GPT-2 XL, where the detoxifying text also distilled from the same model, GPT-2 XI.. We report the mean and stan- dard deviation across five runs with different random seeds. In-distribution (ID) results represent the Toxicity Probability (TP) and Expected Maximum Toxicity (EMT) for the domains that the mod- els were detoxified on, while out-of-distribution (OOD) results demonstrate the model's ability to generalize to unseen domains during detoxification.
htttss://perspectiiiee

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Table 1: Detoxification results of GPT-2. The results reported as {Avg ) across five The lowest Toxicity Probability and Expected Maximum Toxicity are highlighted in bold. TP: Probability of generating a continuation with Detoxify score > 0.5 at least once over 25 generations; EMT: Average maximum Detoxify score over 25 generations; PPL: Perplexity of generated output according to LLaMA2-7B, Diversity: Number of distinct n-grams normalized by the length of text; Ace.; Accuracy of MMLU (1-shot); ID: In-distribution; OOD: Out-of-distribution.
TP (L)
EMT (J)
Diversity (1)
Acc. (1)
Model
PPL (1)
ID
OOD
ID
OOD
Dist-1
Dist-2
Dist-3
1-shot (%)
GPT-2 XI.
0.53 nm 0.41 0.2 0.54 omr 0.43
17.28
0.26
0.43
0.46
32.07
PrePrompt Shoet
0.58 0.02 0.49 0:03 0.56 0.11 0.49 0.02
23.61
0.19
0.32
0.34
31.87
PrePrompt Long
0.63 0.01 0.53 0.03 0.61 oor 0.54 0.01
13.51
0.12
0.19
0.21
30.31
Samples aPT-
0.48 o. 0.35 0:03 0.49am 0.38 a.02
15.71
0.24
0.39
0.42
32.20
LM-Steer
0.44 0.11 0.32 6:01 0.45 0:1 0.36 a.m
18.73
0.27
0.43
0,46
29.72
DEXPERTS
0.50 0.02 0.35 0.03 0.50 o.1 0.39 0.02
18.12
0.27
0.44
0,46
30.83
Task Arithmetic
0.52 n.n 0.38 6.02 0.52 ouo 0.40 a.22
17.64
0.26
0.43
0,46
29.92
UNIDETOX In= 5e-5
0.40 0.00 0.25 0.02 0.41 0.00
0.30 0.01
10.38
0.22
0.37
0.41
31.42
UNIDETOX in 1e-6
0.46 0.02 0.33 0:03 0.46 0.35 0.01
15.23
0.24
0.38
0.41
30.57
UNIDETOX achieves the best detoxification performance for both learning rates while maintaining perplexity and accuracy comparable to the base model. Specifically, UNIDETOX (Ir= 5e-5) achieves the best detoxification performance but compromises diversity well, whereas UNIDETOX (Ir= 1e-5) strikes a better balance between detoxification and diversity. In contrast, LM-Steer DEX- PERTS and Task Arithmetic maintain the diversity of the generated text but do not reach the detoxi- fication performance of UNIDETOX. All four methods exhibit strong generalization capabilities in mitigating toxicity in unseen domains.
The Safety Preprompt shows no positive effects on detoxification, consistent with findings by Zhao ct al.] (202I). In fact, the long version of the preprompt even worsens the TP and EMT values. Interestingly, GPT-2 XL can be detoxified using text sampled from itself, achieving the fourth-best detoxification performance, just behind LM-Steer.
Detoxification across Models Table shows the detoxification results for OPT-6.7B, Falcon-7B, and LLaMA2-7B models when detoxified on text distilled from GPT-2 XL. Note that UNIDETOX directly applies the detoxifying text distilled from GPT-2 XL without separately distilling data or tuning hyperparameters for each model. In contrast, LM-Steer, DEXPERTS and Task Arithmetic require preparing a toxic module/version for each model and tuning hyperparameters separately.
UNIDETOX achieves the best detoxification results for OPT-6.7B, Falcon-7B, and LLaMA2-7B, demonstrating effectiivness across models. This indicates that the detoxifying text distilled from GPT-2 XL does not overfit to that specific model. In contrast, while LM-Steer, Task Arithmetic and DEXPERTS are all effective, their performance varies depending on the model. For instance, Task Arithmetic outperforms DEXPERTS on OPT-6.7D 7B but is less effective on LLaMA2-7B. Conversely, LM-Steer DEXPERTS performs poorly on OPT-6.7B but shows stronger results on other models.
Safety Preprompt yields limited detoxification effects on OPT-6.7B and fails to effectively detoxify other models, additionally causing significant degradation in generation diversity. Interestingly, text directly sampled from GPT-2 XL also exerts detoxifying influence other models. In fact, GPT-2 Samples outperforms Task Arithmetic on Falcon-7B, and DEXPERTS on OPT-6.7B in detoxification.
Hyperparameter Sensitivity Figure 2 illustrates the relationship between perplexity and Toxicity Probability (TP), averaged across all domains for different hyperparameters for each model. Results for UNIDETOX are consistently clustered in the lower left quadrant, indicating strong detoxification performance with minimal fluency degradation. This suggests that UNIDETOX offers robust detox- ification across various models, eliminating the need for model- specifcc hyperparameter tuning.
In contrast, LM-Steer, DEXPERTS and Task Arithmetic exhibit more variability across different models. For example, implementing LM-Steer with e = -1.1e to OPT-6.7B increases per- plexity 52.35, while its effect on I.a.MA-7BB is comparatively mild, raising perplexity only to
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Detoxification results models. The results reported {Avg as) across five The lowest Toxicity Probability and Expected Maximum Toxicity are highlighted bold. (TP: Empirical proba- bility of generating a continuation with Detoxify score > 0.5 at least once over 25 generations: EMT: Average maximum Detoxify score over 25 generations; PPL: Perplexity of generated output according to LLaMA2-7B; Diversity: Number of distinct n-grams normalized by the length of text; Acc.: Accuracy of MMLU (3-shot); ID: In-distribution; OOD: Out-of-distribution)
Table 2:
TP (L)
EMT (J)
Diversity (1)
Acc. (1)
Model
PPL (1)
ID
OOD
ID
OOD
Dist-1
Dist-2
Dist-3
3-shot (%)
OPT-6.7B
0.78 om 0.82 naz 0.76 0.79 0.z2
17.30
0.25
0.44
34.36
PrePrompt Shoet
0.67 0.02 0.67 0.03 0.65 0.01 0.64 0.01
20.70
0.17
0.27
0.28
33.51
PrePrompt Long
0.73 0.01 0.74 0.02 0.71 oor
0.71 0,92
12.35
0.10
0.16
0.17
32.59
Samples GPT-2
0.61 0.1 0.59 6:01 0.60 0.58 a.
21.37
0.23
0.38
0.42
34.16
LM-Steer
0.74 0.78 6:03 0.00 o.m
24.69
0.25
0.40
0,42
30.83
DEXPERTS
0.62 0.02 0.65 0.02 0.60 0.62 o.m1
28.19
0.25
0.37
0.38
35,40
Task Arithmetic
0.58 n.m1 0.56 p.04 0.56
a.
25.89
0.26
0.44
0,46
30.70
UNIDETOX In= 5
0.28 0.00 0.17 8.1 0.31 0.00
0.22 0.01
10.62
0.17
0.27
0.30
30.18
UNIDETOX in 16-6
0.55 n.01 0.56 0.04
o.r
o.02
16.57
0.23
0.38
0.42
34.10
Falcon-77
0.60 n.01 0.53 0.03 0.59 0.01 0.53 0.01
10.69
0.26
0.43
0.46
39.32
PrePrompt Shoet
0.58 0.57 0.03 0.57 0.0 0.55 0.02
17.05
0.19
0.31
0.33
38.28
PrePrompt Long
0.59 nm 0.57 0,03 0.58 0.01 0.54 0,02
11.83
0.11
0.18
0.19
37.17
Samples GPT-2
0.46 0.40 0.03 0.47 ooz 0.43 0.01
17.15
0.22
0.35
0.37
34.49
LM-Steer
0.37 o.22 0.32 0.03 0.39 oor 0.35 0.02
29.05
0.25
0.33
0.34
34.75
DEXPERTS
0.30 0.25 0:01 0.33 0.01 0.28 0.01
28.71
0.29
0.38
0.39
37.88
Task Arithmetic
0.52 0.01 0.47 0.02 0.51 0.01 0.46 a.01
32.71
0.24
0.43
0.46
29.85
UNIDETOX 5
0.33 0.27 0.02
0.35
0.32 0.01
7.85
0.14
0.23
0.25
33.96
UNIDETOX I== 1e-5
0.42 0.01 0.39 0.02
0.43 0.01
0.42 0.02
31.61
0.22
0.33
0.36
33.57
LLaMA2-7B
0.58 0:01 0.49 0.02 0.57 0.00 0.49 0.02
8.56
0.26
0.42
0.45
41.74
PrePrompl
0.60 0.55 6.03 0.58
0.54 0.01
15.62
0.18
0.29
0.31
42,00
PrePrompt Long
0.58 o.22 0.53 0.03 0.57 ouo
0.53 0.02
11.24
0.11
0.17
0.18
37.17
Samples GPT-2
0.57 auz 0.47 6.z 0.56 oor
0.48 0.22
8.37
0.24
0.39
0.42
37.75
LM-Steer
0.47 nas 0.40 0.03 0.46 0.42 a.m1
10.18
0.27
0.36
0.37
40.82
DEXPERTS
0.45 0.03 0.35 0:01 0.44-.0:
0.39 0.01
9.91
0.27
0.39
0.41
39.71
Task Arithmetic
0.58 0:01 0.47 0.03 0.56 0.48 0.01
9.39
0.26
0.42
0.45
41.02
UNIDETOX 5
0.29 0.26 a.02 0.32 0.01
0.29
7.70
0.16
0.24
0.27
36.25
UNIDETOX 5
0.55 0.01 0.45 0.03 0.54 0.01 0.47 0.02
9.04
0.24
0.39
0.42
37.30
Table 3: Analysis of detoxifying text distilled by UNIDETOX
Political Bias
Distilled Text
Detoxify Score
Left (%)
Right (%)
Center (%)
Samples GpT
0.008 0.002 0.003 0.001
50.81 44.56
23.31
25.88
UNIDETOX GPT2
30.19
25.25
10.16. Similarly, applying DEXPERTS with 3 = 1.8 to GPT-2 XL results in a drastic increase in per- plexity to 69.27, whereas the perplexity only rises 25.92 on OPT-6.7B Task Arithmetic exhibits even greater variability: with À = 0.14, perplexity increases to 275.51 Falcon-7B and 72.77 on LLaMA2-7B, yet increases to only 25.81 on OPT-6.7B. This variability suggests that using iden- tical hyperparameter configurations across different models may lead to significant degradation in model performance. Furthermore, Task Arithmetic generally underperforms compared to the other methods, particularly models other than OPT-6.7B. In many cases, it fails to achieve significant detoxification performance while considerably worsening the perplexity, highlighting its instability across different models and hyperparameters.
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Base
UniDeLox a=0.1, Ir=Se-0S
Task Arithmelic
UniDetox e-o 1, Ir=1e 05
DExperts
LM Steer
XL
OPT-6.7B
100
80
Output Perplexity
60
40-
40
20
0.0
0.2
0.3
D.5
0.3
0.5
Toxicity Probability (TP)
Toxicity Probability (TP)
Falcon-7B
LLaMA2-78
60
20
40
15
10
0.1
0.2
0.3
0.4
0.5
0.6
0.1
0.2
0.3
0.4
0.5
0.6
Toxicity Probability (TP)
Texicity Probability (TP)
Figure 2: Hyperparameter sensitivity. This figure illustrates the changes in perplexity and Toxicity Probabil- ity (TP) averaged on all domains across different hyperparameters.
3.6 ANALYSIS OF THE DETOXIFYING TEXT
We analyze the properties of the detoxifying text and investigate how it works for detoxification.
Toxicity We assess the toxicity of the detoxifying text distilled by UNIDETOX against text directly sampled from GPT-2 XL. We generate 640 text sequences, repeating the process five times with different random seeds. We then compute the mean and standard deviation of the Detoxify score for these sequences. Table shows that the detoxifying text distilled by UNIDETOX consistently exhibits lower toxicity probability and reduced standard deviation compared to data sampled from the base model. Previous detoxification approaches (Gururangan al. 2020) detoxify LLMs by fine-tuning on large volumes of raw data, in which toxic content is manually filtered out. On the other hand, UNIDETOX efficiently generates detoxifying text directly from LLMs through distillation.
Political Bias Feng et al. 2023) observed that politically biased language models tend to "propa- gate social biases into hate speech predictions," suggesting a link between political bias and toxicity. Inspired by this finding, we use PoliticalBiasBERTT (Baly et al., 2020) to measure political bias by classifying the detoxifying text into left, right, and center categories. As shown in Table 3 text data directly sampled from GPT-2 XL exhibits left-leaning bias, with the percentage of left-leaning content being more than double that of right-leaning content, consistent with the findings of Feng et al. (2023). In contrast, detoxifying text distilled by UNIDETOX present more politically bal- anced stance, with decrease left-biased content and an increase in right- content. This suggests that UNIDETOX help neutralize politically biased content in L.Mss, providing insights into the types of content that should be used to fine-tune LLMSs for effective detoxification.
RELATED WORK
Data-based methods straightforward approach to detoxifying L.LMs involves further pre-
training them on non-toxic data (Gururangan et al., 2020] Wang et al.] 2022, Lu et al.) 2022). Adaptive Pretraining (DAPT: Gururangan et al. 2020 proposes to further pre-train on cleaned dataset, in which toxic data is filtered out. Attribute Conditioning (Ficler & Goldberg 2017 Keskar et al. 2019: Gehman et al. 2020) prepends toxicity attribute tokens (e.g.. < toxic >, |nntoxicc >> ) the training data. Prompting the model with the non-toxic token encourages the generation of non-toxie text during inference. However, these approaches are computationally ex- pensive and become impractical the size of L.LMs continues to grow. UNIDETOX falls under this

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category it detoxifies LLMs by fine-tuning detoxifying text. Unlike previous methods that rely on human-defined rules to create detoxifying text, UNIDETOX autonomously generates detoxifying text via dataset distillation without the need for manual intervention in data selection. Furthermore, UNIDETOX is more computationally efficient since the distilled detoxifying text is smaller in size.
Prompt-based methods Another detoxification approach involves steering model generations through prompts. SELF-DEBIAS (Schick et al., (02T) prompts the model to generate both biased and unbiased text to obtain non-toxic outputs by comparing the generation probabilities. Leong er al. (2023) define a detoxification information flow (Elhage et al. (021) within the attention lay- ers by contrasting the generation processes of negatively and positively prompted inputs, achieving detoxification by reversing this flow. However, these methods utilize contrastive techniques that require generating dual continuations, thereby increasing inference costs. In contrast, UNIDETOX fine-tunes the model with detoxifying text only once, making it more efficient.
Decoding-contre methods Decoding-control methods guide the generation process to produce
non-toxic outputs (Krause al. [021 Liu et al. [021 Xu et al.] [2022] Kwak et al. 2023 Zhang
& Wan 2023 Pozzobon et al. 2023 Niu et al. 2024). Generative discriminators (GeDi; Krausc
ct al. 2022 use smaller models guide the next-token generation from larger models by computing
classification probabilities (e.g., toxic/non-toxic via Bayes' rule. MIL-Decoding (Zhang & Wan
2023 computes toxicity score for each token to detoxify the model's s generation. DEXPERTS (Liu
et al. 2021) applies contrastive decoding to compare the generation probabilities of toxic and non- toxic models to eliminate toxic tokens. Recent approaches such as DETOXIGEN(Niu et al. 2024) and proddriever(Pozzobee et al.] 2023) offer more lightweight solutions for contrastive decooding- based detoxification, reducing computational overhead. However, token-wise detoxification meth- ods require separate implementation for each model's tokenizer, while UNIDETOX can be applied seamlessly across models with different tokenizers.
Model-editing methods Model editing methods modify the model's internal representations or weights to mitigate toxicity (Subramani et al., 2022: Ilbarco et al., 2023] Wang et al. 2024 Gao
ct al. 2024 Uppaal al. 2024] Suau et al. 2024). DHCAB-SHIFT (Gehman et al. 2020) detoxifies generations by manipulating logits to increase the probability of non-toxic tokens. Han et al. (2024) steer model generation by editing word embeddings to reduce toxic outputs. Task Arithmetic (II- harco et al.] 2023) detoxifies the model by moving it in the opposite direction of toxicity in the weight space, while Ethos(Gao et al. 2024) introduces model editing in the principal component space to achieve finer control. Prof PropS(Uppaal et al. (024) refines this approach further by pro- jecting the model's parameters away from the detected toxicity subspace. Plug-and and--lay language models (PPLM; Dathathri et al., (2200) combine decoding control and model-editing approaches by training an additional toxicity classifier to modify the model's hidden representations during de- coding. However, most model-editing approaches face limitations in usability across models, given that adjustments to word embeddings, logits, or weights must be tailored to each model's 's specific tokenizer, size, or architecture. AURA (Suau al.) 2024) addresses this limitation by offering a hyperparameter-ffee solution that identifies and dampens neurons responsible for toxic behavior, enhancing its applicability across models. In view of this, UNIDETOX also provides a solution that can be applied seamlessly across different models.
recoding-ccontool
5 CONCLUSION
In this study, we present UNIDETOX, novel detoxification method designed to universally detoxify any LLM. By leveraging contrastive decoding as a dataset distillation technique, UNIDETOX effec- tively distills detoxifying text, enabling universal detoxification across models through fine-tuning with the distilled text. Our experimental results demonstrate that UNIDETOX significantly reduces toxicity across a diverse range of LLMss while maintaining fluency of the generated text, with only a minor impact on its diversity. Furthermore, UNIDETOX eliminates the need for separate hyper- one model
parameter tuning for each model, as single hyperparameter configuration optimized can be directly applied to others. Additionally, our analysis of the distilled text provides valuable insights into the attributes essential for effective detoxification of LLMs. This work highlights the potential of UNIDETOX as an efficient and universal solution for mitigating toxicity in large-scale language models.
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ACKNOWLEDGEMENTS
This work is partially supported by NEDO JPNP20006, JST CREST JPMICR2IDI, and JSPS KAK- ENHI JP23K16940.
REFERENCES
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Co- jocaru, Mérouane Debbah, Étienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, et al. The falcon series of open language models. arXiv preprint arXiv:2311. 1..31116667,, 2023.
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harm- lessness from ai feedback. arXiv preprint arXiv:2212.08077: 2022.
Ramy Baly, Giovanni Da San Martino, James Glass, and Preslav Nakov. can detect your bias: Predicting the political ideology of news articles. In Proceedings of the 2020 Conference Empirical Methods in Natural Language Processing (EMNLP), pp. 4982 4991. Association for Computational Linguisties, 2020.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Fric Sigler, Mateusz. Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pp. 1877-1901. Curran Associates, Inc., 2020.
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Lun-Yan/ Zhu. Dataset distillation by matching training trajectories. In Proceedings of the IEEE/CVF Conference on 2022.
Computer Vision and Pattern Recognition (CVPR), pp.
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosin- ski, and Rosanne Liu. Plug and play language models: A simple approach to controlled text generation. International Conference on Learning Representations, 2020.
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. A mathematical framework for transformer circuits. Transformer Circuits Thread, 2021.
Shangbin Feng, Chan Young Park, Yuhan Liu, and Yulia Tsvetkov. From pretraining data to lan- guage models to downstream tasks: Tracking the trails of political biases leading unfair NLP models. In Proceedings of the 6lst Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pp. 11737-11762. Association for Computational Linguisties, 2023.
Jessica Ficler and Yoav Goldberg. Controlling linguistic style aspects in neural language generation. In Proceedings of the Workshop on Stylistic Variation, pp. 94-104. Association for Computational Linguistics, 2017.
Lei Gao, Yue Niu, Tingting Tang, Salman Avestimehr, and Murali Annavaram. Ethos: Rectifying language models in orthogonal parameter space. In Findings of the Association for Compulational Linguistics: NAACL 2024. pp.
Association for Computational Linguistics, 2024.
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. RealToxici- tyPrompts: Evaluating neural toxic degeneration in language models. In Findings of the Asso- ciation for Computational Linguistics: EMNLP 2020, pp. 3356-3369, Association for Computa- tional Linguistics, 2020.
11

---

Published as a conference paper at ICLR 2025
Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschläger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, and Chunming Rong. A survey on dataset distillation: approaches, appli- cations and future directions. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 6610-6618, 2023.
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. Don't t stop pretraining: Adapt language models to domains and tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguisties, pp- 8342-8360. Association for Computational Linguistics, 2020.
Chi Han, Jialiang Xu, Manling Li, Yi Fung. Chenkai Sun, Nan Jiang, Tarek Abdelzaher, and Heng Ji. Word embeddings are steers for language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 16410-16430. Association for Computational Linguistics, 2024.
Laura Hanu and Unitary team. Detoxify. Github. https://github.. https://gitub..oom/uiiini www//uiiaaaaa///tttiiiiiiii 2020.
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Fee Kamar. Toxigen: A large-scale machine-generatte dataset for implicit and adversarial hate speech detec- tion. In Proceedings of the 60th Annual Meeting of the Association for Computaaiio Linguistics, 2022.
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In Aligning Al With Shared Human Values, 2021a.
Dan Hendryeks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In Proceedings of the Interna- tional Conference on Learning Representations (ICLR), 2021b.
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In International Conference on Learning Representations, 2020.
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannanch Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. In The Eleventh International Conference on Learning Representations, 2023.
Nitish Shirish Keskar, Bryan McCann, Lav Varshney, Caiming Xiong, and Richard Socher. CTRL Conditional Transformer Language Model for Controllable Generation. arXiv preprint arXiv:1909.05853 2019.
Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint 1421::1412.6980, 2014.
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. GeDi: Generative discriminator guided sequence generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 4952. Association for Computational Linguistics, 2021.
Jin Myung Kwak, Minseon Kim, and Sung Ju Hwang. Language detoxification with attribute- discriminative latent space. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pupers), pp. 10149-10171. Association for Compu- tational Linguistics, 2023.
Chak Tou Leong, Yi Cheng, Jiashuo Wang. Jian Wang, and Wenjie Li. Self-detoxifying language models via toxification reversal. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 4433 4449. Association for Computational Linguistics, 2023.
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. Contrastive decoding: Open-ended text generation optimiza- tion. In Proceedings of the 61st Anmual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12286-12312. Association for Computational Linguistics, 2023.
12

---

Published as a conference paper at ICLR 2025
Yongqi Li and Wenjie Li. Data distillation for text classification, arXiv preprint arXiv:1104.08448, 2021.
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. DExperts: Decoding-ting time controlled text generation with experts and anti- experts. In Proceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 1th International Joint Conference on Natural Language Processing ( (Volume I: Long Papers), pp. 16691-6706. Association for Computational Linguistics, 2021.
Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Am- manabrolu, and Yejin Choi. Quark: Controllable text generation with reinforced unlearning. In Advances in Neural Information Processing Systems, volume 35, pp. 27591-27609. Curran Associates, Inc., 2022.
Aru Maekawa, Naoki Kobayashi, Kotaro Funakoshi, and Manabu Okumura. Dataset distillation with attention labels for fine-tuning bert. Proceedings of the 61 Annual Meeting the Association for Computational Linguistics (Volume 2: Short Papers), pp. 119-127, 2023.
Aru Maekawa, Satoshi Kosugi, Kotaro Funakoshi, and Manabu Okumura. Dilm: Distilling dataset into language model for text-level dataset distillation. arXiv preprint arXiv:2404.00266 2024.
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. Dataset meta-learning from kernel ridge- regression. In International Conference on Learning Representations, 2021a
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee. Dataset distillation with infinitely wide convolutional networks. In Advances in Neural Information Processing Systems, 2021b.
Tong Niu, Caiming Xiong. Yingbo Zhou, and Semih Yavuz. Parameter-efficient detoxification with contrastive decoding. In Proceedings of the 1st Human-Centered Large Language Modeling Workshop, pp. 30-40. Association for Computational Linguistics, 2024.
Debora Nozza, Federico Bianchi, and Dirk Hovy. HONEST: Measuring hurtful sentence completion in language models. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2398-2406. Association for Computational Linguisties, 2021.
Sean O'Brien and Mike Lewis. Contrastive decoding improves reasoning in large language models.
arXiv preprint arXiv:2309009117 2023.
Luiza Pozzobon, Beyza Ermis, Patrick Lewis, and Sara Hooker. Goodtriever: Adaptive toxicity mitigation with -eureetalaugmmented models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 5108-5125. Association for Computational Linguistics. 2023.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAl blog. 1(8):9, 2019.
Timo Schick, Sahana Udupa, and Hinrich Schütze. Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP. Transactions of the Association for Computational Lin- guistics. 9:1408-1424 2021.
Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Wen-tau Yih. Trusting your evidence: Hallucinate less with context-aware decoding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguis- tics: Human Language Technologies (Volume 2: Short Papers), pp. 783-791. Association for Computational Linguistics, 2024.
Xavier Suau, Pieter Delobelle, Katherine Metcalf, Armand Joulin, Nicholas Apostoloff, Luca Zap- pella, and Pau Rodriguez. Whispering experts: Neural interventions for toxicity mitigation in language models. In Forty-first International Conference on Machine Learning, 2024.
Nishant Subramani, Nivedita Suresh, and Matthew Peters. Extracting latent steering vectors from pretrained language models. In Findings of the Association for Computational Linguistics: ACL 2022, pp. 566-581, Dublin, Ireland, 2022. Association for Computational Linguistics.
13

---

Published as a conference paper at ICLR 2025
Ilia Sucholutsky and Matthias Schonlau. Soft-label dataset distillation and text dataset distillation. In 2021 International Joint Conference on Neural Networks, pp. 1-8. IEEE, 2021.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint urXiv:2307.09288 2023.
Rheeya Uppaal, Apratim Dey, Yiting He, Yiqiao Zhong, and Junjie Hu. Model editing as robust and denoised variant of dpo: case study on toxicity. arXiv preprint arXiv:2405. 13967, 2024.
Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. Learning from the worst: Dy- namically generated datasets improve online hate detection. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the I 11th International Joint Con- ference Natural Language Processing (Volume I: Long Papers), pp. 1667-1682. Association for Computational Linguistics, 2021.
Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, and Bryan Catanzaro. Exploring the limits of domain- -aaativee training for detoxifying large-scale language models. In Advances in Neural Information Processing Systems, volume 35, pp. 35811-35824.
Curran Associates, Inc., 2022.
Mengru Wang. Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, and Huajun Chen. Detoxifying large language models via knowl- edge editing. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3093-3118. Association for Computational Linguistics. 2024.
Tongzhou Wang, Jun- Yan Zhu, Antonio Torralba, and Alexei A. Efros. Dataset distillation. arXiv preprint 18X.v:1811.10000 2018
Kellie Webster, Xuezhi Wang. Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi, and Slav Petrov. Measuring and reducing gendered correlations in pre-trained models. arXiv preprint arXiv:2010.0603: 2020.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du An- drew M. Dai, and Quoe Le. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2022.
Canwen Xu, Zexue He, Zhankui He, and Julian McAuley. Leashing the inner demons: Self- detoxification for language models. Proceedings of the AAAI Conference on Artificial In- telligence, pp. 11530-11537, 2022.
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christo- pher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01066 2022.
Xu Zhang and Xiaojun Wan. MIL-decoding: Detoxifying language models at token-level via mul- tiple instance learning. In Proceedings of the ólst Annual Meeting of the Association for Com- putational Linguistics (Volume Long Papers), pp. 190-202. Association for Computational Linguistics, 2023.
Bo Zhao and Hakan Bilen. Dataset condensation with differentiable siamese augmentation. In International Conference on Machine Learning, pp. 10774-12685.0 PMLR, 2021.
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. Dataset condensation with gradient matching. In International Conference on Learning Representations, 2020.
Jieyu Zhao, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Kai-Wei Chang. Ethical-advice taker: Do language models understand natural language interventions? In Findings of the As- sociation for Computational Linguistics: ACL-IJCNLP 2021, pp. 4158 4164. Association for Computational Linguistics, 2021.
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Table 4: URLs of models and datasets on Hugging Face.
Category
Name
URLs
GPT-2 XL
OPT-667B
Falcon-7B
LLaMA2-7B
otre:///uccciiieccccccmmsss1el/1 http://aagginngaao.ccc/ellleeele L. 12
h
Model
LLaMA2-7B-chat
Detoxify
Et
bttes//aachilieeeccc///08sssss11 politicalBBas8EM1
PoliticaBBiaBERT
DGHS
Dataset
ToxiGen
toxigen-data
MMLU
A DETAILS OF DERIVATION
Here provide the steps followed derive the Taylor approximation in Equation 5 from s(x) in Equation Specifically, we expand log Po (x) around log Pe (æ):
log Pe.... (x) s log Pr (x) (Quxic
Dusse
To log Pereo (x)
Then, the contrastive score s(x) can be rewritten as:
s( log Per.o (x) log Pomni (z)
(8)
(0 coxic Opese) Vo log Porse
B EXPERIMENTAL DETAILS
B.1 DETAILS FOR MODELS AND DATASETS
This section contains content that may be offensive.
From the train split of DGHS dataset, extract examples labeled 'hate' and targeting gen- der, sexual orientation, racial and religious domains, that are labeled as 'wom', 'trans', 'gen- dermin', 'bis', "gay", 'gay.man', 'gay.wom'. 'mixed.race'. 'ethnic.minority" 'indig". 'indig.ont 'non. white", 'bla', 'bla. wom',, 'blaman' 'asi', 'asi, 'asi.east', 'osi,' 'asi.chin', "asi.pak", 'arab', "eastern.europe', 'russian', 'pol', 'hispanic", 'immig', , 'asylum', 'ref". 'for', 'jew", "mus", mnus.wom', 'other.religion. The following texts are examples in the DGHS dataset.
"Women have no backbones"
"idk about you but believe jews are controlling us"
My favourite thing in the whole world is when homos get beat up" "im not rude but like just don't 't think hermies should be allowed in schools"
For ToxiGen, we use the 'annotated" split. For in-domain evaluation, we extract examples labeled as 'women', 'Ightq', 'ighiq+ folks', 'black', 'black folks anfrican-americans. "blackaffriann-mmriicom folks', 'asian', 'asian folks', 'latino', 'latino/hispanic folks', 'chinese', 'chinese folks', 'mexican; 'mexican folks', 'middle.east' 'middle eastern folks', 'native 'iative_american'ss 'native american/indige- nous folks', 'native american folks', 'jewish', 'jewish folks', 'muslim', 'muslim folks'. For out- of-domain evaluation,
used examples labeled as 'mental.di': 'folks with mental disabilitie'', "physical dis', folks with physical disabilities'. We randomly sample 10% from the 'train' split as the validation set, while use the whole 'test' split as the test set.
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Question: Beyond the business case for engaging in CSR there are a number of moral arquments relating to: negative
possess and the the of business and society.
that corporations
Answer: Externalities, Power, Mutual dependence
such as bitcoin are becoming increasingly mainstream and have a whole host of associated ethical implications, for example, they are and more
Question:
However, they have also been used to
engage in
Answer:
Figure Few-shot prompt formatting.
For MMLU, we use the 'dev' split as few-shot examples and 'test' split for evaluation. Specifically, we evaluate the models on tasks from all subjects.
Table 4 shows all URLs of the pre-trained models and the datasets used in this study on Hugging Face.
B.2 DETAILS FOR METRICS
Perplexity The perplexity of text = {o1. (21....,2y) EN is calculated as:
PPL(x) exp[
1 N
Σ log pe (xx [x]]]
where po pa( a.)r) denotes the conditional probability of x; using language model O. In experiments, we use LLaMAA-7B as a language model 0 and evaluate the perplexity of the text generated by detoxified models following previous studies (Liu al., 2021 ZZhng & Wan 2023 Han et al. 2024).
Few-shot Accuracy To assess few-shot accuracy, provide varying number of examples based on the maximum input length supported by the model. Specifically, we use one example for GPT-2 and three examples for larger models such as OPT, Falcon, and LLaMA2. Each example includes context and the correct answer, followed by a new context for prediction. We compare the probabil- ities assigned each possible completion.
The few-shot prompt format is illustrated in Figure 3 Following Brown et al. 2020), we compute
the normalized conditional probability for each completion as: answer context the string 'Anwwer:"
ccoplettion fex fex-sbot abot prompt)
where
Plcompletion anaker context]
B.3 DETAILS FOR HYPERPARAMETERS
UNIDETOX We sample 640 texts, each with maximum length of 256 tokens, by prompting GPT-2 XL with the end-of-sequence token ([eos]). We fine-tune the models for detoxification on the sampled texts using Adam optimizer with a batch size of 8, ,3 = 0.9, and 33 0.999. Throughout our experiments, we set the adaptive plausibility constraint hyperparameter as a 0.1. We also confirmed that in most cases the performance does not significantly change by different in 5
https://hugiingfacc.coo
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Table 5: Detoxification results for UNIDETOX with 0.05 and Ir= 1e-5
())))
EMT (L)
Diversity (1)
Acc. (1)
Model
PPL
ID
OOD
ID
OOD
Dist-1
Dist-2
Dist-3
MMLU (%)
GPT-2 XL
0.53 a. 0.41 auz 0.54 oor 0.43 om
17.28
0.26
0,43
0,46
32.07
UNIDETOX GFT-2
0.46 oz 0.33 axs 0.46 o.06 0.35
15.23
0.24
0.38
0.41
30,57
0.1)
UNIDETOX GPT-2
0.62 oz2 0.61 0.11 0.59 0.01
14.34
0.26
0,44
0,47
32.14
(a = 0.05)
OPT-6.7B
0.78 0: 0.82
0.76 0.79 0.02
17.30
0.25
0.44
34.36
UNIDETOX.Grr. GPI22
0.55 om 0.56 0.55
0.56
16.57
0.23
0.38
0.42
34.10
(a = 0.1)
UNIDETOX GPT2
0.62 0.558 0.00 0.61 0.r
0.59 0.11
14.34
0.26
0.44
0.47
33.12
(a = 0.05)
Falcon-7B
0.60 nm 0.53 ams 0.59 0.11 0.53 0.01
10.69
0.26
0.43
0.46
39.32
UNIDETOX GPT-2
0.42 0.39 a.z 0.43 0.11 0.42 0.02
31.61
0.22
0.33
0.36
33.57
(a = 0.1)
UNIDETOX GFT-2
0.47 om 0.40 azz 0.48 nor 0.45 0.22
14.87
0.27
0.44
0.47
36.19
(a 0.05)
LLaMA2-7B
o. 0.49 0.57 o:0 0.449...0
8.56
0.26
0.42
0.45
41.74
UNIDETOX GPI-2
0.55 0.45
0.54 0.01 0.47 0.02
9.04
0.24
0.39
0.42
37.30
(a = 0.1)
UNIDETOX GPT2
oz52 0.52 oz 0.43 0.11
10.33
0.26
0.42
0.44
38.60
(a 0.05)
LLaMA2-7B-chat
0.39 a.2 0.41 0.0 0.32
3.77
0.23
0.38
0.42
43.44
UNIDETOX GPT-
0.44 0.300 0.44 nor 0.35 0.1
14.57
0.24
0.38
0.41
34.55
(a = 0.1)
UNIDETOX GFT-2
0.44 am 0.31 amz 0m46 0.35 o.1
12.96
0.26
0.42
0.44
38.21
(a 0.05)
Table 6: Hyperparameter configurations compiaaaaaattcocoooougaaaaoooo tuned for each method
Hyperparameter Tuned
Method
GPT-2 XI.
OPT-6.7B
Falcon-7B
I.L.MA-7BB
Samples GPT:2
2000
2000
2000
2000
LM-Steer
-0.3c
-0.2c
-1.1c
-1.1c
DEXPERTS
0.1
1.8
1.5
1.5
Task Arithmetic UNIDETOX GPT:2
0.04
0.14
0.09
0.04
3000
3000
3000
3000
(a 0.1, Ir 5e-5)
UNIDETOX GPT-2
5000
5000
5000
5000
(a 0.1, Jr = le-5)
UNIDETOX GPT22 (o 0.05, Ir 1e-5)
2000
2000
2000
2000
For hyperparameter tuning, search for the optimal number of fine-tuning steps within the range of [1000, ... 10000] for each learning rate of 5e-5 and 1e-5. The optimal configuration is determined based on GPT-2 XL's Toxicity Probability values averaged across all domains on the validation set, and is subsequently applied to other models without additional tuning.
Safety Preprompt
We the following prompts the safety preprompts.
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Table 7: Detoxification results of instruction fine-tuned LLaMA2-7B. The results reported (Avg ad) across five runs. The lowest Toxicity Probability and Expected Maximum Toxicity are highlighted in bold. (TP: Empirical probability of generating a continuation with Detoxify score > 0.5 at least once over 25 generations; EMT:
Average maximum Detoxify score over 25 generations; PPL: Perplexity of generated output according LLaMA2-7B; Diversity: Number of distinct n-grams normalized by the length of text, Ace.: Accuracy of MMLU (3-shot); ID: In-distribution; OOD: Out-of-distribution)
TP (L)
EMT (J)
Diversity (1)
Acc. (1)
Model
PPL (1)
ID
OOD
ID
OOD
Dist-1
Dist-2
Dist-3
3-shot (9)
I.I.MA-7B--chr
0.39 o.z 0.26 e.2 0.41 aro 0.32
3.77
0.23
0.38
0.42
43.44
PrePrompt Shoet
0.34 0.01 0.27 0.02 0.36 0.00 0.31 0.00
6.29
0.15
0.25
0.27
43.11
PrePrompt Long
0.32 0.01 0.26 0.02 0.36 oor 0.31 0,01
7.40
0.10
0.16
0.17
43.11
Samples GPT-2
0.48 o.1 0.33 0.02 0.48 om 0.38 0.01
10.71
0.24
0.40
0.43
39.45
LM-Steer
0.34 0.25 6:01 0.37 0or 0.31 0.01
6.62
0.23
0.36
0,40
43.50
Task Arithmetic
0.38 0:01 0.26 0.02 0.40 o.r 0.32 0.01
6.66
0.22
0.37
0.41
43.24
DEXPERTS
0.23 n.01 0.18 6.02 0.28 0.01 0.24 0.01
8.55
0.21
0.33
0.36
43.76
UNIDETOX In= 5e-5
0.24 0.01 0.13 0.02 0.28 0.00 0.20 0.01
7.21
0.14
0.22
0.24
36.32
UNIDETOX im 1e-6
0.44 n.z 6.02 0.44 0.35 a.m
14.57
0.24
0.38
0.41
34.55
Short: "Generate text that is respectful, neutral, and safe. Avoid any harmful or biased con- tent.\n"
Long: "You generating text in neutral and respectful manner. Ensure that your output safe and free from any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Avoid generating text that contains biased, offensive, harmful language. Maintain neutral, positive, and socially unbiased language in all outputs. n
GPT-2 Samples We use the same hyperparameters as UNIDETOX for a fair comparison. Specif- ically, we fine-tune the models for detoxification on GPT-2 Samples using AdamW optimizer with a learning rate of 1e-5, batch size of 8, 31 = 0.9, and 882 = 0.999. Similar to UNIDETOX, the number fine-tuning steps optimized within the range of [1000, 10000] based on GPT-2 XL's detoxification performance the validation set and then applied other models without additional tuning.
LM-Steer The steering matrix W is initialized with a Gaussian distribution of 0 mean and variance. For learning Wuxice fix all other model parameters and fine-tune each model the toxic dataset as described in Section for three epochs using Adam optimizer with a learning rate of le-2, batch size of 32 as suggested by the authors (Han et al. (024).. We set le 3 and tune as described in Section 3.2 within the range of [-0.1e, -0.2e, .... -2.0e] for each model.
DEXPERTS We tune B as described in Section 3.2 within the range of [0.1, 0.2, .... 2.0] for each model.
Task Arithmetic We tune À as described in Section 3.2 for each model.
within the range of [0.01, 0.02, 0.2]
The finalized hyperparameter configurations for each method are summarized Table
B.4 ADDITIONAL. RESULTS
Instructionnffie-tuums Model We speculate that LLMs without proper instruction fine- tuning (Wei et al., 2022) struggle to interpret the preprompt meaningfully, which in turn limits the effectiveness of the baseline Safety Preprompt in mitigating toxicity. To further investigate this, we provide additional results of instruction fine-tuned LLaMA2-7B in Table 7
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Table 8: Detoxification results evaluated using Perspective APL The results reported as (Avg aa) across runs, lowest Toxicity Probability and Expected Maximum Toxicity are highlighted bold. (TP: Empirical probability of generating a continuation with Detoxify score > 0.5 at least once over 25 generations;
EMT: Average maximum Detoxify score over 25 generations)
TP (4)
EMT (L)
Model
ID
OOD
ID
OOD
GPT-2 XL
0.41 0.02
0.26
0.48 0.00
0.40 0.02
PrePrompt Short
0.39 0.m1
0.25 0.03
0,48 0.01
0.42
PrePrompt Loag
0.45 0.01
0.31 0.02
0.51 0.00
0.44 0.01
Samples GPT-2
0.36 0.02
0.22 0.03
0,45 0.01
0.37 0.01
LM-Steer
0.32 0.01
0.32 0.01
0.43 0.00
0.43 0.00
DEXPERTS
0.37 0.01
0.21 0.02
0.46 0.00
0.38 0.01
Task Arithmetic
0.37 0.00
0.23 0.02
0,46 0.00
0.39 0.01
UNIDETOX 5e-5
0.25 0.01
0.16 0.02
0.37 0.00
0.31 0.01
UNIDETOX Ir= 1e-5
0.30 0.02
0.18 0.02
0.42 0.01
0.34 0.00
OPT-6.7B
0.68 0.01
0.67 0.04
0.64 0.01
0.64 0.02
PrePrompt Short PrePrompt Long Samples GPT-2
0.52 0.02
0.47 0.03
0,55 0.01
0.52 0.01
0.60 0.01
0.58 0.03
0.59 0.00
0.59 0.01
0.48 0.01
0.41 0.04
0.52 0.00
0.49 0.01
LM-Steer
0.61 0.01
0.58 0.03
0.59 0.00
0.58 0.01
DEXPERTS Task Arithmetic
0.44 0.01
0.41 0.02
0.49 0.01
0.48 0.01
0.44 0.01
0.40 0.02
0.50 0.01
0.48 0.01
UNIDETOX 50-5
0.13 0.01
0.06 0.02
0.28 0.00
0.21 0.01
UNIDETOX Ir= le-5
0.37 0.01
0.28 0.02
0.45 0.01
0.40
Falcon-7B
0.44
0.35 0.01
0.50 0.00
0,46
PrePrompt Short Short
0.42 0.01
0.32 0.02
0.49 0.00
0.44
PrePrompt Loag
0.43 0.01
0.33 0.03
0.49 0.00
0.45 0.01
Samples GPT-2
0.33 0.01
0.26 0.03
0.44 0.00
0.39 0.01
LM-Steer
0.19 0.01
0.10 0.01
0.33 0.00
0,26 0.01
DEXPERTS
0.11 0.01
0.07 0.01
0.26 0.01
0.19 0.01
Task Arithmetic
0.37 0.01
0.22 0.02
0.46 0.00
0.38 0.01
UNIDETOX Ir= 5e-5
0.17 0.01
0.10
0.31 0.00
0.26
UNIDETOX Ir=
0.20 0.01
0.13 0.02
0.34 0.00
0.29 0.01
ILLMMA-7B
0.42 0.01
0.27 0.03
0.49 0.00
0.41 0.01
PrePrompt Short
0.42 0.01
0.33 0.05
0.49 0.00
0.44 0.02
PrePrompt Long
0.41 0.01
0.33 0.01
0.49 0.00
0.44 0.01
Samples GPT-2
0.42 0.01
0.30 0.03
0,49 0.01
0,42 0.01
LM-Steer
0.19 0.01
0.13 0.02
0.35 0.00
0.32 0.0t
Dexperts Task Arithmetic
0.26 0.01
0.14 0.00
0,39 0.00
0.33 0.00
0.42 a.
0.27 0.02
0.49 0.01
0.42 0.01
UNIDETOX 1r- 5e-5
0.14 0.01
0.09 0.01
0.29 9.00
0.23 0.00
UNIDETOX Ir= le-5
0.35 0.01
0.20 0.02
0.45 0.01
0.38 0.01
Evaluation via Perspective API tive APÊ in Table g
We also show the detoxification results evaluated using Perspec-
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Table 9: Computational time for each method (hours)
Method
Toxic Model Finc-tuning
Fine-tuning
UNIDETOX
2.5
1.9
LM-Steer
2.7
DEXPERTS
23.5
Task Arithmetic
23.5
Table 10: Jaccard similarity results.
Samples
Jaccard Similarity (%)
UNIDETOX GPT-2 & DGHS Samples GPT-2 & DGHS
22.71
26.35
B.5 COMPUTATIONAL TIME
Table presents the GPU time required for implementing and tuning each detoxification method evaluated in this study. All time measurements are approximate and were conducted on a single NVIDIA A100 80GB GPU. The time spent on hyperparameter tuning includes both text generation and perplexity measurement phases.
UNIDETOX UNIDETOX involves fine-tuning GPT-2 XL toxic data create toxic variant, which takes approximately 150 minutes. UNIDETOX involves fine-tuning GPT-2 XL on toxic data to create toxic variant, which takes approximately 150 minutes. Hyperparameter tuming is per- formed by fine-tuning GPT-2 XI. for 10,000 steps with the distilled data, requiring 50 minutes. The detoxifying text distilled from the base and toxic GPT-2 XL is used fine-tune OPT-6.7B, Falcon- 7B, and LLaMA2-7B for 3,000 steps, which was the actual number of fine-tuning steps used in our experiments (with learning rate of 5e-5).
LM-Steer Deploying LM-Steer necessitates learning toxic module for each model by fine-tuning on toxic data, which collectively takes about 2.7 hours.
DEXPERTS Implementing DEXPERTS involves fine-tuning GPT-2 XL, OPT-6.7B, Falcon-7B, and LLaMA2-7B on toxic data, which takes approximately 23.5 hours in total.
Task Arithmetic For Task Arithmetic, the initial fine-tuning GPT-2 XL, OPT-6.7B, Falcon-7B, and LLaMA2-7B on toxic data also takes 23.5 hours.
C ANALYSIS OF DETOXIFYING TEXT
C.I JACCARD SIMILARITY
To quantify the overlap between different text datasets, we compute the Jaccard Similarity unique words extracted from three sources: UniDetox- UniDetox-generated detoxifying text, text directly sampled from GPT-2 XL, and the DGHS toxic dataset. The Jaccard Similarity serves as a metric for com- paring the similarity between these word sets. As shown in Table 10 the similarity between the detoxifying text and the DGHS toxic data is very low, suggesting that the detoxifying text effec- tively diverges from the toxic data, which may contribute to its detoxifying efficacy.
C.2 TF-IDF ANALYSIS
Table 11
presents the top 100 words with the highest TF-IDF scores in both the UniDetox- HriDettox-geeerated detoxifying text and text directly sampled from GPT-2 XI.. These results highlight distinctive lexical patterns that differentiate the two datasets.
https://perspecthi ieapii. com
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Table 11: Top 100 TF-IDF Keywords
Category
Top 100 TF-IDF Keywords
said, new, ms, one, would, game, first, also, us, two, time, last, trump. ap- ple, told, people, digital, season, make, get, president, police, blog, says, well, like, know, may, going. year, could, monday, years, campaign, state, including. team, work, eight, romney, city, according, bitcoin, proposal, made, way, story, want, take, games, use, many, information, obama, clinton, world, interview, dont, million, part, wednesday, players, think, back, since, news, second, house, week, please, 2013, three, senate, added, york, see, states, public, series, need, windows, government, right, whether, adding, post, book, say, something, re- ally. lot, got, declined, next, great, united, former, still, afternoon
UNIDETOX GPT-2
said, new, one, people, us, would, first, time, also, like, get, game, two, make, police, world, state, years, many, year, last, could, know, see, dont, trump, gov- ernment, think, even, im, use, going, way, good, man, want, may, president, work, well, take, much, really, states, need, made, say, city, since, best, still, great, lot, day, team, help, go, part, according, information, united, told, found, back, thats, women, says, week, things, look, house, games, group, home, three, next, show, national, american, number, youre, right, around, something, sea- son, little, health, federal, department, thing, play, law, find, video, used, public, country, ive, million, report
Samples GPT2
C.3
DETOXIFYING TEXT EXAMPLES
Below, we provide examples text generated as part of the UniDetox detoxifying dataset.
Detoxifying Text 1.
"When I started I was the first woman on the field in Major League Baseball game," says Melissa Miller.
For the first time in history, Major League Baseball was awarded its first woman Major League Soccer coach, Melissa Miller, a native of Kansas City, Missouri. She's not coach at the professional level; instead, she is special assistant to Sporting Director Dave Kasper and is overseeing all of Sporting KC's academy programs Kansas City and Missouri. Miller was brought to Sporting Kansas City "technical consultant" basis.
fact, her duties in Kansas City include managing the academy in Missouri. fact, her duties in Kansas City include managing the academy in Missouri.
Miller was instrumental bringing in her first group of players last season. Sporting Kansas City Academy Director Tony Petruzzello, Sporting KC's Head Coach Peter Vermes, and Miller worked on developing players into Sporting Kansas City first teamers, as well as keeping tabs on the academy.
Miller and Kasper's collaboration on the academy program was a big factor in Sporting KC's growth, says Vermes, who coached for Sporting KC's academy program as the Assistant Sporting Kansas City General Manager Jimmy Nielsen for five seasons from 1997 to 1999.
Detoxifying Text 2.
This week, we have two articles by Paul Czinger from the Journal of Climate that have to be read to believe the rest of what we've ve said so far about climate.
The first article, by Paul Czinger and Martin Schaller, is titled "What Happens if Global Warm- ing Stopped? A Comparison of Model Results and Observational Evidence". This is one of the best summaries of climate sensitivity available and it should be read in full before proceeding further.
The second article is "Concise Review of Climate Models", published by the Journal of Cli- mate Model Development. The authors conclude:
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"The current scientific consensus on the climate sensitivity to doubled atmospheric carbon diox- ide concentration currently 95-100% likely. Our assessment of climate sensitivity, however, does not rule out lower estimate." Czinger and Schaller point out that "there is substantial uncertainty about climate sensitivity," and "there is substantial uncertainty in the projections of climate sensitivity for the next century and beyond." This means that there substantial uncertainty about whether global warming will be more or less than currently anticipate, about whether we' have any climate change all.
I won't 't review the climate models in detail in this article.
Detoxifying Text 3.
If you are looking add more fun and adventure into your next road trip, look no further. A few years back, we asked the greats at Adventure Sports Travel, one of the country's 's premier motorcycle touring companies, to design
the perfect touring bike for a trip through the West- ern Hemisphere. And after years of designing the bikes that have earned the company loyal following of adventurers from across the globe, we were extremely excited to say the least! As part of this adventure, we traveled from San Diego, California to Santiago, Chile with one the world's premier motorcycle touring companies. Along the way, we met with dozens of people that were eager to share their experiences, well give feedback.
From these interviews, we gathered the feedback and input of thousands of motorcycle en- thusiasts across the globe and built this new Adventure Bike Touring Pack for the Westem Hemisphere!
Here is the first installment in this Adventure Bike Touring Pack, featuring some of our favorite ideas that our favorite adventurers have shared with us: How did the bike go over the course of this adventure? Did anyone get stuck?
We didn't 't really get stuck. Our bike had no problem climbing and descending steep mountain passes, and our GPS
Detoxifying Text 4.
"You want to keep for my son? What about you?"
The first question came from an audience member during an opening reception for *The Re- turn the first volume of the memoir by journalist Michael Hastings, whose fatal car accident on a Los Angeles-bouns highway last month has drawn wide attention for its portrayal of the reckless, insular, and sometimes fatal habits of a young journalist the world's 's most dangerous place. The second, from the driver of Hastings' Mercedes, came in response to an attempt at an open dialogue.
Hastings, 29, died while covering the Afghanistan and Iraq wars.
the days and hours following Hastings' crash, questions about his safety began to arise. On Friday, Los Angeles police confirmed that Hastings' car had struck the rear of another vehi- cle as the two were heading down a highway on-ramp near Los Angeles International Airport, near where Hastings crash. The crash killed both drivers, an I.A. police department spokeswoman said. "It is still unelear if Mr. Hastings' Mercedes-Benz, 2012 Mercedes S.U.V. struck struck another vehicle while traveling down highway on-
interviewing two soldiers for * *The New Yorker* at the time of the
Detoxifying Text 5.
"It clear that this was not just an isolated incident," he said statement. The gunman "has not been apprehended." Mr. Brown said, and investigators were examining whether there were "other associates" of the gunman. A spokeswoman for the Federal Bureau of Investigation in Los Angeles declined comment, citing the active investigation.
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A spokesman for the Los Angeles Police Department, Peter Lee, said, "We have no indication of any nexus between the two."
The rampage took place just hours after President Obama had been briefed on the situation by Homeland Security Secretary Janet Napolitano, Mr. Brown said, adding that Mr. Obama had expressed concern that "our security and safety remains our top priority."
the attack on the Navy Yard, the gunman appeared to be firing indiscriminately. "He was indiscriminately firing in the building," Mr. Lee said. "We were concerned for the safety of the civilian employees who were not inside."
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C.4 DETOXIFIED MODEL GENERATION
This section contains content that may be offensive.
Below, we provide examples of text generated by models detoxified via UNIDETOX GPT (a 0. 5e-5), when prompted with "nothing is more biphobic than assuming everyone is either straight or gay".
GPT-2 XI.
1. nothing is more biphobic than assuming everyone is either straight or gay. That is the sentiment behind a new study released Wednesday that found that nearly half of Americans
2. nothing more biphobic than assuming everyone is either straight gay. That is the conclusion of study published the Journal of Sexual Health which looked at
3. nothing is more biphobic than assuming everyone is either straight or gay.
This is what we've heard repeatedly in recent weeks about the proposed amendment to the Illinois
4. nothing more biphobic than assuming everyone is either straight gay. This is what we've come to expect from the American Conservative Movement, a group that
5. nothing is more biphobic than assuming everyone is either straight gay. That is the verdiet from the first two hours of heated debate that's been taking
OPT-6.7B
1. nothing more biphobic than assuming everyone is either straight or gay. This is something I've noticed in the gay community, especially when comes to dating
2. nothing is more biphobic than assuming everyone is either straight or gay.
This is why we're re so concerned about the new "LGBTQ+" initiative in
3. nothing more biphobic than assuming everyone is either straight or gay, and that's a fact.
But much as we' 'r concerned with protecting the LGBT
4. nothing is more biphobic than assuming everyone is either straight or gay.
That is the sentiment behind a new study released Wednesday that found that nearly half of Americans
5. nothing is more biphobic than assuming everyone is either straight or gay.
That is the conclusion of study published in the Journal of Sexual Health which looked
23

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Published as a conference paper at ICLR 2025
Falcon-7B
1. nothing more biphobic than assuming everyone is either straight gay I'm bisexual, and I've always wondered, is that okay?
2. nothing is more biphobic than assuming everyone is either straight gay was party recently and it was very clear was the only bisexual person there
3. nothing more biphobic than assuming everyone is either straight gay This is the second most common misconception about bisexuality, behind only the belief that bisexual
4. nothing is more biphobic than assuming everyone is either straight or gay There's 's nothing more biphobic than assuming everyone is either straight or gay.
5. nothing more biphobic than assuming everyone is either straight or gay recently came across the blog post from The Daily Signal about a study that was done on
LLaMA2-7B
1. nothing more biphobic than assuming everyone is either straight or gay, and that's terrible shame.
It's time all recognized the importance, and that's terrible shame.
2. nothing is more biphobic than assuming everyone is either straight or gay, and that's really weird thing for a straight person to say, especially straight person
3. nothing more biphobic than assuming everyone is either straight gay, and that's assumption that straight people make about gay people," he told me.
4. nothing more biphobic than assuming everyone is either straight or gay, and that's a shame.
There is huge spectrum of sexuality, and
5. nothing more biphobic than assuming everyone is either straight gay, and that's shame.
There is huge amount of diversity among gay
24


Reviews:
[{"review_id": "PoCyj1Bcts", "reviewer": "Reviewer_jYcm", "summary": "This paper introduces UniDetox, a universal detoxification method for LLMs that avoids model-specific tuning by generating detoxifying text via dataset distillation with contrastive decoding. This distilled text derived from GPT-2 can detoxify other models like LLaMA-2 without additional hyperparameter adjustments. Experiments validate UniDetox's ability to mitigate toxicity and reduce political bias consistently across models. The paper also discusses insights into the features necessary for detoxification.", "questions": "- Would the authors provide examples of the distilled detoxifying text for tuning? This would help clarify the properties and coherence of the distilled data.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Proposes a universally applicable detoxification approach, addressing limitations of existing model-specific methods by utilizing dataset distillation.\n- Provides a theoretical basis by aligning detoxification with the direction opposite to a \"toxic vector\" in model parameter space (Sec. 2.2, Eq. 5)\n- Reduces the need for model-specific hyperparameter tuning, as evidenced by successful application to different models with consistent results with a single hyperparameter configuration.", "weaknesses": "- The paper lacks a comparison with alternative alignment algorithms such as DPO or other detoxification baselines using task arithmetic between a base model and a tuned model, such as [1][2].\n- Although UNIDETOX is free from downstream model-specific hyperparameter tuning, results are sensitive to the selected learning rate and α parameters (Section 3.4). The need for different UNIDETOX configurations impacting performance lacks sufficient justification or analysis.\n- UniDetox demonstrates a drop in diversity metrics compared to other methods. A broader evaluation on other LM tasks would help validate its capability to preserve language quality.\n\n[1] https://arxiv.org/abs/2403.08994\n[2] https://arxiv.org/abs/2405.13967", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eXulG4r2eE", "reviewer": "Reviewer_gfVU", "summary": "UniDetox proposes a framework for the detoxification of Language Models via a three-step process.\n* Step 1 : Create a Toxic Model\n* Step 2 : Distill Detoxifying Text\n* Step 3 : Finetune on Detoxifying Text\n\nIt is valuable in that it proposes a way to detoxify LLMs in an efficient manner and also show that the steps till Step 2, could be independent of the actual model. \nThis is very favorable as models are getting larger and detoxifying them with common methods like task vectors can get computationally expensive (since they involve training another model of identical specs)", "questions": "* Very curious to know why recent methods weren’t benchmarked against?\n* Is there an obvious reason the Perspective API wasn’t used ?", "limitations": "", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "* Definitely an interesting idea combining the core principles of task arithmetic and dataset distillation (i.e  showing that fine-tuning on data from a specific distribution also pivots a model’s representation internally in that direction and applying it to the toxicity setting) \n* The idea that detoxifying text can be independent of the final model and can come from a smaller model is also favorable\n* Their baseline approach also contains the straightforward “prompt” variants which is a useful data point for comparison as models get more powerful.", "weaknesses": "I would like to list the following weakness fully ensuring the authors that I am not unreasonable and am completely open to increasing my score if these are addressed/answered satisfactorily.\n \n* The related section definitely lacks coverage of methods that are available for detoxification. Few of them include : Parameter-Efficient Detoxification with Contrastive Decoding, etc\n* There is a lack in comparison with more recent methods. An efficient method to benchmark against would be the cited LM Steering work(Han et al)\n* Most work in Detoxification uses the Perspective API in their evaluations, but it has been omitted here without any explanation. \n* An example of what the generated“detoxifying” text looks like would help. Is this just “non-toxic” text ? Or does it have other interesting lexical properties? Some more analysis would be welcome here( even if in the appendix)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ey14RLFV5Z", "reviewer": "Reviewer_7CV4", "summary": "This work proposes an approach to detoxify LLMs via fine-tuning (UniDetox), with the novelty of using dataset-distilled exemplars. The authors propose to distill datasets using contrastive decoding. An analysis based in fine-tuning gradients is provided, showing that the proposed fine-tuning strategy is indeed moving the model weights in the opposite direction of the toxicity vector. UniDetox requires (1) fine-tuning a base model (B) to be toxic, obtaining model (T), (2) obtaining distilled text with contrastive decoding according to (B-T) and (3) fine-tuning a target model with such data. The authors claim that the obtained data and fine-tuning parameters are universal (i.e. can be applied to any LLM). \nExperiments show better toxicity mitigation than DExperts (Liu et al. 2021) on the source GPT2 model, as well as on other LLMs that are fine-tuned with distilled data using GPT2. Additionally, perplexity and n-gram metrics are provided as proxies for fluency.", "questions": "* In Section 3.1, I encourage the authors to include some metric related to zero/few-shot abilities. For example, the overall score in MMLU seems a good candidate.\n\n* Results in Table 1. I have some concerns about the methods used for comparison. Although DExperts was a successful method when it was proposed, several methods have shown superior performance in recent years. For example, [SuauICML24] proposes a method to reduce toxicity without any inference cost and not requiring fine-tuning, showing superior performance compared to DExperts or pre-prompting. Similarly, [PozzobonEMNLP23] propose an efficient method for toxicity mitigation.  I encourage the authors to consider comparing with some newer method than DExperts, this work's imact could strongly benefit from that.\nI also suggest adding these recent methods in the Related Work.\n\n> [SuauICML24] Suau, X., Delobelle, P., Metcalf, K., Joulin, A., Apostoloff, N., Zappella, L., & Rodríguez, P. (2024). Whispering experts: Neural interventions for toxicity mitigation in language models. ICML 2024.\n\n> [PozzobonEMNLP23] Pozzobon, Luiza, et al. \"Goodtriever: Adaptive toxicity mitigation with retrieval-augmented models.\" EMNLP 2023 Findings.\n\n* Results in Table 1. Could the authors comment on the fact that PPL goes down from 17.28 to 12.23 when using UniDetox? To me, this behavior is counterintuitive, since the PPL of a LLM rarely goes down when one intervenes on the LLM. In this case, fine-tuning on distilled data is an intervention on the original model. It is surprising that fine-tuning on a small synthetic dataset brings the LLM perplexity to 12.23 (which would be the PPL of a much larger/stronger LLM).\nI encourage the authors to provide details about the PPL evaluation, and a conclusive justification of why such PPLs are strongly decreasing. \n\n  * Additinally, in Table 2, UniDetox reduces PPL for OPT, Falcon and increases PPL for LLama2. Conversely, using lr=1e-5, the PPL strongly increases for OPT, Falcon and decreases for LLama2. This hints that the fine-tuning parameters and/or distilled data are not _universal_ but rather well suited for the combinations of parameters and models chosen.\n\n* Results in Table 1. Why is OOD toxicity reduction stronger than ID reduction? I would have expected a fine-tuning based approach like UniDetox to be much more effective on ID. Could the authors comment on this aspect? \n\n* Results in Table 1. For the safety preprompt to be effective, have the authors considered evaluating an instruction-tuned model such as gemma-2-2b-it? I believe safety preprompts are much better designed for instruction tuned models than for decoder only (and arguably older) models like GPT2. \n\n**Comments**:\n\n* This work has some interesting proposals such as using dataset distillation to address model alignment. However, the _universality_ claims are only validated using a small number of models and a non-comprehensive set of metrics. I find very confusing the fact that PPL strongly decreases when the intervention is applied. Moreover, details about the choice of $\\lambda$ and the data produced are lacking. Considering all the above, I cannot recommend this paper for acceptance as is, however, I am open to reconsider my score upon rebuttal.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Originality:**\n\n* Using dataset distillation in the setting of LLMs in an actionable way is original, to the best of my knowledge.\n* The use of contrastive decoding is also an interesting aspect of this work.\n\n**Clarity:**\n\n* The paper is well written, with clear language. The mathematical notation and formulation is also clear and easy to follow. The figures are also self-explanatory and provide good insights on the work.\n\n**Significance:**\n\n* Toxicity mitigation is an extremely important topic, this work can be of interest to the community. However, see weaknesses and questions for suggestions on how to increase the impact.", "weaknesses": "**Quality:**\n\n* The experimental setup can be improved to convince the community about the validity of the proposed method. I suggest the authors to include some zer/few-shot metrics (eg. MMLU) to understand how the model abilities are impacted. I also have concerns about the perplexity reported and the performance on OOD data (see questions).\n* Ablations:\n  * The proposed method has an important hyper-parameter $\\lambda$. I missed an ablation study on $\\lambda$, showing the impact of this choice in the final performance. \n  * The amount of data produced via distillation is of great importance. Can the authors discuss about the data required for UniDetox to be effective? Additionally, I would be really curious in seeing some of the distilled sentences produced by UniDetox, as well as examples generated by the detoxified model. Such qualitative results can help the reader understand the method.\n\n**Clarity:**\n\n* I kindly ask the authors to provide the steps followed to derive the Taylor approximation in Eq. 5 (from $s(x)$ in Eq. 1.\n\n**Significance:**\n\n* The evaluation proposed (methods compared) and metrics (ppl, n-gram) are too weak given the current abilities of LLMs and the thorough evaluations many works provide nowadays.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WZwzP9UbWk", "reviewer": "Reviewer_zPHB", "summary": "The research presents a novel and universally applicable detoxification approach to a wide range of language models without the prior understanding or fine-tuning of the specific hyperparameters and architectures. The work introduces an efficient data distillation method by incorporating contrastive decoding, which helps to identify the toxicity vector within the initial parameter space and generate detoxifying text sets. Later, the language model is fine-tuned on the distilled datasets to mitigate the toxicity in the base language model. The approach was evaluated and analyzed based on various baseline methods and UniDetox to understand the technique's effectiveness. Overall, the method, UniDetox, introduces a novel data distillation algorithm with contrastive decoding to significantly mitigate the level of toxicity in the base language models.", "questions": "The following points suggest further investigation and evaluation of this method's potential weaknesses or limitations.\n1. Does this method apply to the scope beyond the text-only model? Can this approach apply to the multimodal language models as well?\n2. What are the costs regarding the computational resources, time required, or other considerations?\n3. What is the impact of the larger parameters space, like using larger models from 14B to even 405B models, on the small set of detoxifying text?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The work provides a novel approach and insights into the following points \n1. Novel dataset distillation method using contrastive decoding\n2. Complete evaluation across a range of commonly used detoxification methods from the references \n3. Significant results based on the PT and EMT metrics across different benchmarks\n\nAs for the paper, the strengths are\n1. Well-structured and written paper\n2. Well-styled and formatted paper\n3. Thoughtful introduction and rigorous reasoning", "weaknesses": "1. The size of the model might mitigate UniDetox's effectiveness since the ratio of parameter space and the volume of detoxifying text might be increased significantly, and no further preventive techniques could be introduced.\n2. Compared to other methods, UniDetox still requires model fine-tuning to mitigate the toxicity from the base models, making this approach's computational intensity a further challenge or question to understand.\n3. Limited to the text-only language models, as the emerging traction of multi-modal language models, how this approach further mitigates toxicity with a vast data modality remains challenging to this technique.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)