Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models
Authors: Luiza Amador Pozzobon
Beyza Ermis
Patrick Lewis
Sara Hooker
Venue: EMNLP 2023 Findings (2023)
Keywords: toxicity mitigation
retrieval-augmented
continual learning
TL;DR: This work introduces a flexible methodology for mitigating toxicity in LMs that considers the evolving nature of language, achieves SOTA performance, reduces latency and improves computational efficiency.

Abstract: Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive auxiliary models. Furthermore, previous approaches have often neglected the crucial factor of language's evolving nature over time. In this work, we present a comprehensive perspective on toxicity mitigation that takes into account its changing nature. We introduce Goodtriever, a flexible methodology that matches the current state-of-the-art toxicity mitigation while achieving 43% relative latency reduction during inference and being more computationally efficient. By incorporating a retrieval-based approach at decoding time, Goodtriever enables toxicity-controlled text generation. Our research advocates for an increased focus on adaptable mitigation techniques, which better reflect the data drift models face when deployed in the wild.

GOODTRIEVER: Adaptive Toxicity Mitigation with Retrieval-augmentel Models
Luiza Pozzobon' ?
Beyza Ermiş
Patrick Lewis
Sara Hooker
Cohere For AI
Cohere For AI
Cohere patrickecohere.com
Cohere For AI
luiza@cohere.com com
beyza@cohere.comm com
sara@cohere.com com
Abstract
text (Rae al., 2021; Deshpande et al., 2023). To address this challenge, is essential not only measure and understand the origins of toxic text generation but also to take effective steps towards its mitigation in LMs.
Warning: This work contains content that may be offensive or upsetting.
Considerable effort has been dedicated to miti- gating toxicity, but existing methods often re- quire drastic modifications to model parame- ters or the use of computationally intensive auxiliary models. Furthermore, previous ap- proaches have often neglected the crucial fac- tor of language's evolving nature time. In this work, we present comprehensive perspective on toxicity mitigation that takes into account its changing nature. We intro- duce GoodtRIeVer, a flexible methodology that matches the current state-of of the-ar toxi- city mitigation while achieving 43% relative latency reduction during inference and be- ing more computationally efficient. By in- corporating retrieval-based approach at de- coding time, GOODTRIEVER enables toxicity- controlled text generation. Our research advo- cates for an increased focus on adaptable miti- gation techniques, which better reflect the data drift models face when deployed in the wild.'
Prior research on detoxification has primar- ily focused on two computationally expensive approaches: finetuning or constrained decoding (Zhang et al., 2022a). Finetuning requires modi- fications to pretrained LM parameters through additional training carefully curated data. On the other hand, constrained decoding relies auxiliary model processing module that modi- fies the next-token probabilities at inference time. Both of these approaches are known to be highly compute-intensive intensive (Zhang et al., 2022a).
In addition the drawbacks of the aforemen- tioned techniques, the academic treatment of toxic language mitigation has predominantly assumed that toxicity remains static over time. Most of the existing research has focused on building special- ized models for specific domains or locales, which lack flexibility once trained and may have limited upplicability across different tasks and domains (Wang et al., 2022: Gururangan et al., 2020). How- ever, human language shaped by cumulative culture, constantly building upon itself and evolv- ing over time (Silvey, 2016). Similarly, the ways in which language can cause harm, such as offen- sive and harassing text (Gehman et al., 2020), also evolve (Lopez-Zafra and Garcia-Retamero, 2021; Charlesworth and Banaji. 2022).
1 Introduction
Large-scale pretrained language models (LMs) have demonstrated remarkable progress capabili- ties (Radford et al., 2019; Brown et al., 2020). How- ever, an unintended consequence of this progress is the generation of toxic and harmful language, including hate speech, insults, profanities, and threats (Gehman al., 2020; Bender al., 2021). With the widespread adoption of large language model systems such as ChatGPT (OpenAI, 2022: Liu et al., 2023) and OpenAssistant (Köpf et al., 2023), there is need for techniques that can effec- tively mitigate the generation of toxic and harmful
In this work, we propose a flexible technique called GOODTRIEVER (Figure 1) that effectively tackles both static and lifelong toxicity mitigation. Our approach is designed to handle domain shifts and builds upon recent advancements in language modeling, which have successfully incorporated ex- ternal memory to enhance performance (Khandel- wal et al., 2019; Lewis et al., 2020; Guu et al., 2020; Borgeaud et al., 2022: Izacard et al., 2022). More
'Code and data are available at ht ttttps//giit http//ggttttbbb./o// wtt.g.ihubcco... com/ arr-ai/goodrrivee
Also affiliated with the School of Electrical and Com-
puter Engincering and the Artificial Intelligence Lah, Recod.ai, at the University of Campinas (UNICAMP).

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Test Context
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Figure 1: An illusttation of GOODTRIEVER. The toxic and non-toxic datastores built with toxic and non-toxic examples respectively. For given test context, we (I) embed and search for the most similar contexts in each datastore and (2) ensemble the next token probabilities from the LM with the datastores' probabilities.
specifically, GOODTRIEVER combines large LM with two external datastores. These datastores con- trol text generation based on desirable (non-toxic) and undesirable (toxic) attributes. This property allows for convenient and immediate incorporation of new knowledge, as well as the ability edit, correct and remove existing information without requiring any retraining of the LMM
GOODTRIEVER achieves competitive perfor- mance compared to the expected baseline upper bound, which is a model finetuned all available data. Our method demon- strates its flexible controllability capabiliiiess by promptly mitigating toxicity for each newly added domain.
We conduct extensive experiments for static and continual toxicity mitigation, showing that GOODTRIEVER achieves comparable performance state-of-the-art methods while being far less compute-intensive on the static tasks. We also show that GOODTRIEVER achieves competitive results to the method multitask finetuning on continual toxicity mitigation tasks. Our key contributions are:
Controlled Text Generation with Retrieval-Augmmente Models
Language models (LMs) define probability distri- butions over sequence of tokens. Given context sequence of tokens = =(w,..... w)-1),, ability distribution p(wy(c) over the target token Wy is estimated using next-token prediction where the probabilities are modeled the product of con- ditional probabilities for each token in the sequence, given the tokens that came before it from the autore- gressive LMs. These models are typically imple- mented using a transformer network, parameterized by set of parameters #:
the prob-
We introduce flexible method that enables the integration of multiple retrieval mecha- nisms into LMs. This approach matches state- of-the-art toxicity mitigation scores while re- ducing inference time by 43% and minimiz- ing computational requirements, particularly in terms of parameters.
p(w1, Wn) II p(weleri 0)
(1)
We evaluate the efficacy of GOODTRIEVER across different model sizes and families, namely GPT2 (Radford et al., 2019), Pythia (Biderman et al., 2023), and OPT (Zhang et al., 2022b). By varying the base model sizes from 124M to 6.9B parameters, we show that GOODTRIEVER remains efficient in mitigat- ing toxicity even as the model size increases.
where e is the context sequence of tokens preced- ing Wh, also referred as its prefix. Retrieval-augmented LMs compute next token distributions based not only the immediately preceding context e and the model parameters 0, but also on an external datastore C, from which examples are retrieved and incorporated into the base LM's prediction. Specifically, for predicting Wi, the retrieval operation from C. depends on its
We explore the task of continual toxic- ity mitigation. Through our experiments,

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prefix:
A temperature higher than 1 tends flatten the distribution and prevents overfitting (Khandelwal et al., 2020). More details about how the temper- ature parameter impacts GOODTRIEVER perfor- mance are in Appendix C.3.
p(W,,.... w)) II pl O.C)
(2)
1
2.1 GOODTRIEVER Formalization
For each context Ct, we obtain three sets of prob- ability distributions: the next token distributions i) from the base language model PLM, ii) from the toxic datastore PENN and iii) from the non-toxic datastore PLNN respectively and their correspond- ing logits 2t, 2g -4
GOODTRIEVER, illustrated in Figure 1, is an inference-ting method for controlled text gener- ation. In addition to the standard, parametric, next- word prediction, GOODTRIEVER accesses informa- tion retrieved from pair of datastores that con- tains toxic and non-toxic samples to model text with undesirable and desirable attributes respec- tively. In the following, we will detail the compo- nents our method.
Ensembling. ANN-LM interpolates the nearest neighbor distribution PkNN with the base LM dis- tribution PLM using a tuned parameter to produce the final next-token distribution. &NN-LM only allows to augment the model with single datas- tore. Here we introduce a method that allows us to combine multiple nearest neighbor distributions computed based different datastores with the base LM probability distribution. Our method is based on product of experts which is first proposed by Hinton (2002). That idea allows us to combine toxic and non-toxic datastore outputs with base LM as:
Datastores. A datastore (KC {kk, vt)} is set of key-value pairs constructed from all training examples dataset D:
(K., V) ((f(c), ws) | (Gi, us) E D)
(3)
We define the function f(-), which takes a con- text as input and produces fixed-length vector representation. As an example, in a Transformer model, f() can be defined to map the context an intermediate representation obtained from self-attention layer within the model. For the ith example (c,, wi) E D, the key-value pair (ka, vx) is formed, where k; denotes the vector representation of the context f(4) and iú denotes the value as- sociated with the target word wy. GOODTRIEVER creates two datastores: (C- from toxic ex- amples and (K++, V*) from non-toxic examples.
p(welce) = softmax(z + a 25)) (5)
where a is the tuned parameter that controls the impact of the datastores over the base model. Equa- tion 5 corresponds to the following:
(P (Peln( PaNN (welc))
p(wllc)) & PLM (wxlee
(6)
Inference. During inference, the parameteric com- ponent of the LM generates the output distribution PLAs(W (wzlow 0) over the next tokens, produces the corresponding context representation fc)), given the text input context & and the logits zt E TR where V is the model's 's vocabulary. Then the non- parametric component of the LM queries each data- store (AC, V) with the f(;) representation to re- trieve N, the k-nearest neighbors (k-NN) accord- ing Fuclidean distance function d(:;) -). Next, the token probabilities PENN computed over these neighbors by applying a softmax with temperature T to the neighbors' negative distances and aggre- gating over each token of the vocabulary, as in the following:
This equation indicates that token possesses high probability it satisfies the condition of hav- ing high probabilities under both PI.M and PkNN while simultancously having a low probability un- der PKNN With this equation, we gain the flexi- bility to incorporate multiple datastores with the LM, allowing us to combine their logits through addition subtraction.
Controllable Text Generation for Toxicity Mitigation
3.1 Experimental Setting
Dataset. We use Jigsaw Unintended Bias dataset (Jigsaw) from the Toxicity Classification Kaggle Challenge? with human-annotater toxicity (Borkan et al., 2019). An example is considered toxic >
(wN (x)x
EnhnneeN Munzn exp (aa(kt.(ai)))
(4)
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50% of annotators marked it toxic, totaling 264K comments after data cleaning. Non-toxic examples are the ones that no annotator classified as toxic. We build the GOODTRIEVER toxic and non-toxic datastores from toxic and non-toxic examples of this dataset respectively. Details about the total number of samples and tokens are in Appendix B.
generating span with TOXICITY 0.5 least once among the & generations. This metric cap- tures the frequency of toxicity generation by the model. It is important to note that toxicity scores from the Perspective API tend to change over time and become lower (Pozzobon et al., 2023). This poses challenges in making direct comparisons. To ensure fair comparisons between techniques, we adhere the protocol recommended by Pozzobon et al. (2023) and rescore all previously generated model continuations using the same version of the Perspective API.
Models. GOODTRIEVER is compatible with any model that produces fixed-size context representa- tions. Throughout this section, we use GPT2-large as our base model, but we also present results us- ing different model families: Pythia (Biderman et al., 2023) and OPT (Zhang et al., 2022b). In line with established best practices from prior work (Liu et al., 2021; Fan et al., 2018; Holtzman et al., 2019), we truncate the logits = prior to ensembling with the toxic and non-toxic datastores using nucleous- sampling (Holtzman al., 2019). This process effectively eliminates the unreliable tail of the dis- tribution, leading to enhanced fluency in the gener- ated content.
Fluency, Generation fluency is the mean perplex- ity of generated continuations. In line with best practices from prior work (Liu et al., 2021; Yang et al., 2022), we score perplexity using a larger pretrained LM from the same family as our pri- mary base model, GPT2-XL. Lower perplexity is generally preferable, however if lower perplexity is accompanied by reduced diversity, it signifies repetitive output, which is undesirable. Ideally, the post-toxicity mitigation technique should exhibit comparable perplexity levels to the base model.
Baselines. We compare GOODTRIEVER to differ- ent toxicity mitigation techniques: DEXPERTS (Liu et al., 2021), GeDi (Krause et al., 2020), PPLM (Dathathri et al., 2019), DAPT (Gururangan et al., 2020) and UDDIA (Yang et al., 2022). In Ap- pendix B.1, we include brief overview of each technique. In addition these techniques, also report results for the toxic-only variation of GOODTRIEVER, In this case, the non-toxic logits are replaced by the base LM logits in Equation 5.
Diversity. Generation diversity is measured by the number of distinct n-grams in generated re- sponses scaled by the number of generated tokens (Li et al., 2015). We report diversity results for unigrams, bigrams, and 3-grams (dist-l, dist-2, and dist-3, where 'dist' denotes 'distinct'). A higher diversity score indicates a greater variety of unique n- n-grams generated by the model and is desirable as it signifies broader range of possible continua- tions for each prompt.
3.2 Evaluation
To evaluate the toxicity degeneration and capabili- ties of mitigation of different techniques, we adopt the protocol outlined by Gehman et al. (2020) and the samples selected by Liu et al. (2021), random selection of 10K non- non-toxic prompts from (RTP) dataset. For each prompt, the models generate 25 continuations of 20 tokens. We evaluate models for three sets of metrics:
3.3 Results
Table 1 presents the results of GOODTRIEVER when compared to the baselines. GOODTRIEVER is competitive with previous state-of-the-ar (SOTA) methods and even outperforms the SOTA EMT for GOODTRIEVER (small) at a cost of slightly higher perplexity. Qualitative examples of generated con- tinuations using GOODTRIEVER versus the base model are available the Appendix F.
the REAL.OXXICITY
toxicity, fluency, and diversity which we briefly introduce below.
In Table 2, we show that our method significantly reduces latency and computational costs compared to the previous SOTA method, DEXPERTS. In terms of inference time, GOODTRIEVER (large) achieves a 43% reduction compared to DEXPERTS, while consuming three times fewer parameters.
Toxicity. Following the methodology proposed by Gehman et al. (2020), we measure toxicity using metrics. Expected Maximum Toxicity (EMT) is the maximum toxicity over l model generations for a given prompt. A higher EMT indicates a greater expected toxicity in the worst-case scenario. The Toxicity Probability is the empirical probability of
Phtpss/peerseccticapp.cmmm

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Table 1: Generations from DAPT, GeDi, PPLM, and UDDIA were rescored with Perspective API to obtain up-to- date toxicity metrics (Pozzobon al., 2023). DEXPERTS was entirely re-run in code. Perplexity is computed for a sample of 1000 prompts.
Toxicity (L)
Fluency (L) Perplexity
Diversity (1) Dist-2
Model
Exp. Max. Toxicity
Toxicity Prob.
Dist-1
Dist-3
GPT2 (large)
0.39
0.25
24.66
0.58
0.85
0.85
DAPT
0.27
0.09
30.27
0.57
0.84
0.84
GeDi
0.24
0.06
48.12
0.62
0.84
0.83
PPL (10%)
0.38
0.24
32.58
0.58
0.86
0.86
UDDIA
0.24
0.04
26.83
0.51
0.80
0.83
DExperts (lurge, all jigsaw)
0.21
0.02
27.15
0,56
0.84
0.84
GOODTRIEVER (large, toxic only)
0.23
0.04
38.51
0.61
0.82
0.82
DExperts (lurge, GOODTRIEVER data)
0.21
0.03
23.11
0.57
0.71
0.66
GOODTRIEVER (GPT2 Small)
0.20
0.03
32.95
0.57
0.84
0.84
GOODTRIEVER (GPT2 Medium)
0.22
0.04
23.71
0.57
0.82
0.83
GOODTRIEVER (GPT2 Large)
0.22
0.04
27.11
0.58
0.82
0.83
10M
0.21
0.26
10M
98
71
42
39
33
29
0.59
0.55
0.60
5M
Toxicity
toxic tokens
5M
91
64
40
36
33
30
80
Loxic takens
5M
0.6
0.39
0.24
perplexity
0.55
1M
22
0:21
0.19
1M
47
38
33
32
32
27
60
1M
0.59
0.58
0.58
0.58
0.56
Toxic
500K
0.
22
0.21
0.22
0.21
0.22
0.24
0.22
500K
44
38
35
35
32
30
500K
5.
0.
5.
0.57
0.57
0
0.55
0.50
50K
0.23 0.24
0.23
0.22
0.23 0.26
50K
28
29
26
25
23
22
-40
50K
0.52
0.52
0.51
0.51
0.5
0.5
0,20
10K
0.24 0.23
10K
19
18
17
16
16
15
10K
0.42
0.43 0.43
0.43
500K 1M 5M 10M 2DMM20M400 40M
500K1M 5M 10M20M4OM
500K 1M 5M 10M20M40M
nontokic tokens
# nontoxic takens
nontoxic tokens
(a) Expected Maximum Toxicity
(b) Perplexity
Diversity
Figure 2: Impact of toxic and non-toxic datastore sizes GOODTRIEVER (GPT2 Large) metrics.
We also conducted ablation studies to investigate the impact of 1) datastore size, 2) number of neigh- bors, 3) temperature parameters, and 4) automatic labeling of the datastore samples. We briefly sum- marize the findings below, with full treatment in Appendix C.
Number of retrieved k: neighbors. Figure 6 (in Appendix C.2) shows the impact of k neighbors retrieved for each datastore. Two types of experi- ments are performed: 1) varying rumber of neigh- bors for one datastore while keeping the other fixed at the maximum value of 1024, and 2) varying num- ber of neighbors for both datastores.
Datastore size. Our observations indicate that toxicity mitigation occurs even with small amounts of data in both the toxic and non-toxic datas- tores. GPT2's ' raw EMT value is 0.39, as shown in Table 1. Remarkably, for all combinations of GOODTRIEVER sizes in Figure 2, the maximum EMT 0.26, highly competitive performance compared to the basclines presented in Table
Increasing the number of neighbors contributes to decrease in toxicity across all settings. In scenario (1), retrieving more neighbors from the
non-toxic datastore leads significant reduction in perplexity and diversity. For instance, when re- trieving a single non-toxic neighbor and 1024 toxic neighbors, the perplexity is around 2000. However. when retrieving 1024 tokens from each datastore, the perplexity decreases to approximately 30. Sim- ilarly, the diversity metric improves from 0.2 to nearly 0.6 for the same numbers of retrieved neigh- bors. Conversely, when varying only the number of retrieved neighbors for the toxic datastore, per- plexity increases while diversity also rises. These findings align with the observations presented in previous section, highlighting the significant influ- ence of the toxic datastore on diversity metrics.
The size of the toxic datastore appears to directly impact the diversity of the generated output. When the datastore is too small (< 500K tokens), the di- versity metrics fall below acceptable threshold, only marginally matching the scores of the base model. Regarding fluency, both datastores exhibit a clear trend: as the amount of toxic data increases and the amount of non-toxic data decreases, per- plexity values rise.

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EMT
Perplexity
Diversity (dist- 1
Small
Goodtriever applied
GPT2 Medium
GPT2 Large
Pythia 1B
Pythia 6.98
6.78
0.2
0.2
Relative difference to base model
Figure 3: Relative difference of metrics between GOODTRIEVER and their base models. Relative EMT (U) reduction is achieved for all GOODTRIEVER variants compared their base model.
Base Model
Goodtriever
0.3
0.2
0.1
Small
Medium GPT2
Large
6.9B
1.38
6.78
Pythia
OPT
Figure 4: Absolute EMT (J) for GOODTRIEVER models and their base models. GOODTRIEVER consistently reduces EMT for different model sizes and families.
Alpha vs. Temperature parameters. Figure 7 (in Appendix C.3) shows the impacts of a and &NN softmax temperature T. In our framework, deter- mines the weighting of the next token probabilities sourced from the datastores. T is the softmax tem- perature to build the probability distributions from the datastores, with higher values flattening the dis- tribution and preventing overfitting (Khandelwal et al., 2020).
size. Applying GOODTRIEVER to the OPT family required some tuning of parameters for satisfac- tory results. Results are shown for = 0.5 and T = 500. For Pythia, T 500 was used.
We observe consistent mitigation performance across all variants GOODTRIEVER in terms model size and family. The EMT is reduced by maximum relative value of 48% in GPT2-small (from 0.39 to 0.20) and minimum of 24% in OPT 1.3B (from 0.45 to 0.34). We don't see clear trend between mitigation performance and model sizes. The OPT 6.7B model shows higher relative re- duction toxicity than its 1.3B version, while the Pythia IB has a higher relative reduction compared to its 6.9B version. within the same family show similar base toxicity, finding that is in line with previous work (Rae et al., 2021).
As depicted in Figure 7, increasing the value of a leads to trade-off between toxicity mitiga- tion and perplexity for all evaluated temperatures. Conversely, larger values gressive utilization of the probabilities from the datastores (with larger a values), as increasing T decreases perplexity while maintaining diversity close to the baseline.
allow for more ag-
noteworthy that models
Different Model Sizes and Families. In Fig- ures 3, 4 and Table 3 we show how GOODTRIEVER performs across GPT2, Pythia (Biderman al., 2023) and OPT (Zhang et al., 2022b) model fam- ilies. This allows us understand generalization across model families and quantify how retrieval- augmented toxicity mitigation scales with model
Automatic Labeling the Datastores. We per- formed additional experiments demonstrate the robustness of GOODTRIEVER by substantially ducing the size of the datastores and automatically annotating them. We perform such experiments with two datasets as datastores: Jigsaw, our main

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Table Inference time corresponds to the time to generate a single continuation of 20 tokens on an A100 GPU, We report mean values three runs of 100 prompts with 25 continuations per prompt. We compare GOODTRIEVER inference time with DEXPERTS, the previous SOTA for mitigation and inference time trade-offs. The base model is GPT2-large for both GOODTRIEVER and DEXPERTS.
Model
Inference Time (1) Relative to GPT2 (large) ())
Parameter Count
GPT2 (large)
0.0107
774M
GOODTRIEVER 0.0189
+77%
774M
DEXPERTS
0.0334
+212%
3 X 774M
Table 3: Toxicity mitigation results for different model families and sizes, sizes are ranging from 124M to 6.9B. We show how GOODTRIEVER has consistent mitigation performance even with larger models. The highest absolute decrease in EMT of 0.19, while the minimum is of 0.
Toxicity (4)
Fluency (1) Perplexity
Diversity (1) Dist-2
Model
Exp. Max. Toxicity
Toxicity Prob.
Dist-l
Dist-3
GPT2 (small)
0.39
0..5
57.19
0.61
0.88
0.86
GPT2 (medium)
0.39
0.27
35.94
0.61
0.87
0,86
GPT2 (large)
0,39
0.25
24.66
0.58
0.85
0.85
GOODTRIEVER (GPT2-smal)
0.20 10.19
0.03
32.95
0.57
0.84
0.84
GOODTRIEVER (GPT2-medium)
0.22 10.17
0.04
23.71
0.57
0.82
0.83
GOODTRIEVER (GPT2-large)
0.22 10.17
0.04
27.11
0.58
0.82
0.83
Pythia 1B
0.38
0.25
44.25
0.59
0.86
0.85
Pythia 6.98
0.38
0.25
33.93
0.57
0.86
0.85
GOODTRIEVER (Pythia 1B)
0.21 (0.17
37.44
0.57
0.82
0,83
GOODTRIEVER (Pythia 6.98)
0,23 (0.15
0.04
29.22
0.54
0.80
0.82
OPT 1.3B
0.45
0.38
33.38
0.57
0.85
0.85
OPT 6.7B
0.45
0.39
30.96
0.56
0.83
0.84
GOODTRIEVER (OPT 1.3B) GOODTRIEVER (OPT 6.7B)
0.34 10.11
0.20
21.44
0.53
0.80
0.82
0.31 10.14
0.16
33.14
0.55
0.76
0.78
dataset, and subset of REALTOXICITYPROMPT (RTP) not used for evaluation. Base models are kept the same, and described in Appendix B.4.
Continual Toxicity Mitigation
Work to date has often treated toxicity as fixed characteristic, disregarding its variations over time and among different demographic groups Gooldfrb-Taarrane et al., 2023). One of the key advantages GOODTRIEVER lies in its adaptabil- ity, facilitated by semi parammeraacttiic language models (Khandelwal et al., 2019; Izacard et al., 2022). We demonstrate the benefits of this flexible representa- tion of toxicity by benchmarking GOODTRIEVER the task of continual toxicity mitigation. The goal of this task is continuously adapt to new types of toxicity while maintaining effective miti- gation for previously encountered domains.
generation parameters
In Table we show results GOODTRIEVER with substantially smaller automatically annotated datastores by Perspective API. We also show results of cuman-annntaane datastores for smaller-scale Jigsaw datastore. Respectively for toxic and non- toxic datastores, reported experiments have about 16x and 40x smaller datastores than the results shown in Table
Surprisingly, at this data-constraint regime, both variants of automatiially-labbled GOODTRIEVER datastores (Jigsaw and RTP) achieve lower toxicity metrics than the variant with a full-sized human- annotated Jigsaw from Table 1. Most likely due to smaller toxic datastores (i.e. Figure 2), diversity is slightly lower for all new variants. It is also remarkable how GOODTRIEVER with the randomly subsampled human annotated Jigsaw performs on par with its much larger version from Table 1.
4.1 Experimental Setting
Data. To properly evaluate the task of contin- ual toxicity mitigation, introduce controlled toxic dataset consisting of five well-defined do- mains, each associated with a specific demographic group. Our dataset derived from CivilComments-

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Table 4: GOODTRIEVER (Large) results when coupled with human or automatically annotated datastores. With 16x and 40x less toxic and non-toxic tokens in the datastores, respectively, automatically labeled datastores lead to better mitigation results than the human-annotated datastores from Table 1.
Toxicity EMT
Fluency (4) Perplexity
Diversity (1)
# Tokens in Datastore Non-Toxic
Datastore
Automatically Annotated
TP
Dist-1
Toxic
RTP
Yes
0.19
0.02
23.31
0.52
645k
8088
Jigsaw
Yes
0.18
0.03
29.47
0.55
600k
900k
Jigsaw
No
0.22
0.04
29.92
0.57
640k
857k
Jigsaw (Table 1)
No
0.22
0.04
27.11
0.58
9.4M
41.7M
Goodtriever
DExperts (Multtask)
DExperts (Continual)
trophic forgetting (CF) (Goodfellow et al., 2013). For both of these models, the non-toxic expert was trained (and kept fixed) with the same samples from GOODTRIEVER'S 's non-toxic datastore.
0.50
Overall EMT
0.45
Evaluation. To preserve the demographic con- text of our domains, we refrain from employing prompt/completion separation as done in the RTP dataset (Gehman et al., 2020). Instead, we take a set of 200 toxic sequences from each domain in the processed dataset, which serve our prompts for evaluating the models. We report the same metrics as described in section
0.40
0.35
Politics
Muslims
Race
LGBTQ
Christians
domain added
Figure 5: Overall EMT for each continual learning tech- nique benchmarked. GOODTRIEVER has competitive performance with the multitask fine-tune technique.
4.2 Results
Continual mitigation results are shown in Figures 5 and as well as in Table 8 in Appendix D. As expected, the continually finetuned DEXPERTS model performs the worst in the task due to CE Its mitigation capabilities are not improved new domains are incorporated for finetuning. We ob- serve that GOODTRIEVER results are competitive to the multitask finetune baseline, which has the advantage of optimizing directly for all previous and current domains. These results are particularly significant as GOODTRIEVER does not require fine- tuning on all prior datasets, which can be costly and time-consuming at scale. We also note that these results were achieved without exploring specialized adaptation techniques that have been employed by other retrieval methods, such as specialized sam- ple selection for the datastores or online adaptation of the interpolation parameter (Peng et al., 2023; Huang et al., 2023; Bhardwaj et al., 2022). Instead, GOODTRIEVER relies solely on the raw capabilities of nearest neighbor search and PoE.
WILDS (Koh et al., 2021), which is subset of the previously mentioned Jigsaw dataset, annotated with demographic information. Appendix D pro- vides further details on the data processing steps and the topics covered in each domain (see Ta- ble 7). Throughout our experiments, we keep the non-toxic datastore fixed at size of 50K sentences, focusing on examining shifts in toxicity.
Continual Learning Baselines. We compare the continual mitigation capabilities of GOODTRIEVER with two CL baselines based on DEXPERTS (Liu al., 2021): 1) multitask and 2) continual fine- tuning. In the multitask setting, the experts are finetuned from scratch using all current and previ- ous domain data at each step. This baseline aims to achieve the upper-bound performance among benchmarked CL techniques since it can directly optimize for all available data. In the continual finetuning setting, the experts have access data from the current domain, while reusing the experts from previous steps for finetuning. This protocol closely resembles GOODTRIEVER' s access to data, but it is expected to be a lower-bound due to catas-
5 Related Work
LM Toxicity Mitigation Techniques. Recent lit- crature has explored two primary directions for mitigating toxicity: 1) training and 2) decoding-

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time approaches. Training approaches involve up- dates to the model weights, either by finetuning on carefully filtered non-toxic corpora (Gehman et al.,
It augments an LM with one external memory datastore that is consulted to modify the next-token probabilities. To our knowledge, we are the first to apply a retrieval -augeeened approach to toxic- ity mitigation. In contrast to a standard kNN-LM, we augment multiple datastores with base LM by using an entirely different interpolation technique that utilizes PoE and mitigate toxicity with the aid of two datastores: one with toxic and another with non-toxic examples.
2020; Gururangan et al., 2020; Wang et al., 2022), conditioning training, where models are trained to generate text conditioned on toxic or non-toxic at- tributes (Keskar et al., 2019) or style transfer to remove toxicity (Dale et al., 2021). Training ap- proaches dependent on access to sufficient data and tend to require significant computational re- sources for training, which may pose challenges with the size more recent pretrained LMs (Ah- madian et al., 2023). In contrast to training time approaches, our approach requires no weight up- dates and still performs well even when datastore size is small, making it computationally and data efficient. Decoding-tinge time methods, on the other hand, employ various techniques during the text generation process to address toxicity. Examples include applying heuristic constraints in decoding algorithms to filter out toxic content (Welbl et al., 2021; Sheng et al., 2019), updating a pretrained model's 's hidden representations based on the gra- dient of classifier with respect to the desired class (Dathathri et al., 2019), or directly adjust- ing the distribution using signals from toxicity classifier (Krause et al., 2020). A notable approach in this category DEXPERTS (Liu et al., 2021), which studies controllable text generation by com- bining a trained expert model trained on non-toxic data and a trained anti-expert model trained on toxic data using the Product of Experts (PoE) (Hin- ton, 2002). Similar to DExperts, (Hallinan et al., 2022) presented text detoxification algorithm that combines an expert and anti-expert with an LM using PoE. In contrast to DExperts, we do not leverage auxiliary models but rather utilizing the retrieval-augmented techniques. This avoids ex- ploding parameter count and minimizes latency while preserving performance. Our technique also avoids directly adjusting the output distribution us- ing signals from toxicity classifier done by Krause al. (2020), which can impact fluency.
Continual Learning (CL) in LMs remains rela- tively unexplored, with only a limited number of works focusing on adapting language models emerging corpora across various domains and time- lines (Gururangan et al., 2020; Jang et al., 2021; Jin et al., 2021). There has been some work on tox- icity classification that explores possible variations in terms of in-text demographic citations (Borkan et al., 2019) or the onset of new hate idcologies over time (Qian al., 2021). Borkan al. (2019) intro- duce human-labeled dataset with 450K samples with demographic identity citations, later adapted to be the CivilComments-W/I et al., 2021). Qian et al. (2021) investigates life- long hate-group classification applied to tweets and show how the major hate-speech topics change over time (Qian et al., 2021). To the best of our knowl- edge, research is the first tackle lifelong toxicity mitigation within the context of CL.
dataset (Koh
Conclusion
We present GOODTRIEVER, a novel method for tox- icity mitigation, which utilizes multiple retrieval mechanisms to effectively adapt to the changing nature of language and toxicity without compro- mising linguistic quality. GOODTRIEVER achieves 43% decrease in inference time when compared to previous state-of-the-ar while maintaining a com- parable toxicity mitigation performance. We also show how GOODTRIEVER mitigates toxicity con- sistently across model sizes and families. Unlike prior approaches, GOODTRIEVER remains flexible and competitive in the face of evolving data.
Retrieval-Angmeented LMs. These methods in- volve the retrieval of documents from textual knowledge corpus, which are subsequently utilized perform various language tasks (Min et al., 2022; Borgeaud et al., 2022; Lewis et al., 2020; Izacard and Grave, 2020; Izacard et al., 2022; Guu al., 2020). One of the simpler retrieval auggeette tech- niques is the &NN-LM (Khandelwal et al., 2019).

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Limitations
research is driven by the goal of promoting respon- sible and ethical use of language models, with a focus on ensuring the generation of safer content. We firmly discourage any attempts to exploit our method for malicious purposes, as it directly con- tradicts our ethical principles.
In this work, as in prior works we use the toxicity definitions from Perspective API for our datastore and evaluation. We understand the definition of what is toxic is extremely subjectíve and that there's s no perfect answer for how toxic a given sentence is. We also don't it evaluate if our mitigation tech- nique amplifies biases against marginalized groups, as investigated in previous work (Xu et al., 2021; Welbl et al., 2021). On the technical aspects, other limitations are: (1) supporting only HuggingFace models implemented in the PyTorch framework; (2) our evaluation of the impact of GOODTRIEVER is limited to the metrics we propose and qualitative inspection (as visualized in Appendix E).
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Ethics Statement
Our research investigates the usage of retrieval models during decoding time of text generation to suppress toxic language and enhance the harmless- ness of generated content. It is important to note that while method for toxic language suppres- sion significantly reduces the probability of generat- ing toxic language, it does not entirely eliminate it. While extensive experimentation has demonstrated a significant decrease in model toxicity, we advise careful consideration when applying our method in real-world applications.
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Continual Learning (CL) in LM. There are few more studies in addition the ones given in Section 5. LAMOL (Sun et al., 2019) has intro- duced method that simultaneously learns tasks and generates training samples, enabling the model to replay pseudo-samples from previous tasks with- out requiring additional memory or model capacity and ELLE (Qin et al., 2022) has employed a com- bination of replay-basee and parameter isolation based methods for continual pre-training. However, to the best of our knowledge, our research is the first tackle lifelong toxicity mitigation within the context of CL.
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Zihan Zhang, Meng Fang, Ling Chen, and Mohammad- Reza Namazi-Rad. 2022c. Is neural topic modelling better than clustering? an empirical study on clus- tering with contextual embeddings for topics. arXiv preprint 220::2204009774
B Experimental Details
Zexuan Zhong, Tao Lei, and Danqi Chen. 2022. Train- ing language models with memory augmentation. arXiv preprint vrXiv:2205.12677
B.1 Baselines of comparison
We leverage open-sourced continuations (Liu et al., 2021; Yang et al., 2022) for all models except DEx- PERTS. To ensure comparability, we rescore the toxicity scores, making certain that they adhere to the same version of the Perspective API (Pozzobon et al., 2023).
A Extended Related Work
Retrieval-Augmented LMs. These methods in- volve the retrieval of documents from textual knowledge corpus, which subsequently uti- lized to perform various language tasks. The in- tegration of retrieval components with LMs has gained significant attention in recent studies, partic- ularly in the field of language modeling (Min et al., 2022; Borgeaud et al., 2022) and question answer- ing (Lewis et al., 2020; Izacard and Grave, 2020; Tzacard et al., 2022; Guu et al., 2020). In addition to these explicit text retrieval methods, there is a cate- gory of models known as semi- parameeratttric language models (Sukhbaatar et al., 2019; Wu al., 2022) that employ memory to store text as key-value pairs. One prominent example is the ANN-LM (Khandel- wal et al., 2019), that extends a pretrained LM by
DAPT finetunes an LM for additional steps on domain-specific data. The base language model, GPT2-large, is fine-tuned the non-toxic subset of the OpenWebTee corpus, specified by Liu et al. (2021).
GeDi uses class-conditional language models (CC- LM) steer larger LMs' next-token probabilities with Bayes rule to favor a given controlled attribute (Krause et al., 2020). The authors used GPT2-XL as a base model and GPT2-medium as the CC-LM fine-tuned on the Jigsaw dataset for detoxification.
PPLM updates the base language model's hidden

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activations using a toxicity classifier finetuned on the Jigsaw dataset (Dathathri et al., 2019). Due to high computational cost, PPLM is evaluated on a random subset of IK non-toxic prompts.
Table 5: Dataset details for GOODTRIEVER GPT2 based models experiments.
Dataset size
Non-toxic
Toxic
Tokens Comments
41,737,133 1,164.564
9,378,564 264,435
UDDIA removes dependencies between pro- tected attribute, that in our case is toxicity, and text produced by LMs by rectifying the probabil- ity space. For toxicity mitigation, they leverage PPLM's classifier (Dathathri et al., 2019) and a novel redo mechanism that determines which lay- ers need to have hidden activations modified (Yang et al., 2022).
B.4 Experimental Details
We compare toxicity metrics for multiple model sizes and families. All results from sections 3.3 and 3.3 were performed for the 10K non-toxic prompts from REALTOXICITY previously by (Liu et al., 2021). For inference, we used exclusively A 100 40GB GPUs.
selected
DEXPERTS (Liu et al., 2021) addresses control- lable text generation by combining an expert model trained on non-toxic data, and an anti-cxpert model trained on toxic data. In the original codebase, we were able to achieve a slightly lower EMT score of 0.19 instead of 0.21 as obtained by our codebase, but the inference time The average inference time for each continuation of 20 tokens was of 0.19 seconds in the original code versus 0.033 our implementation. We be- lieve the differences come from the main libraries' versioning differences, particularly the transform- ers library. As we prioritized a fair comparison in terms of inference time, we show the results of our implementation of DEXPERTS.
In Table 6, we present the parameters used for GOODTRIEVER- models across all sizes and families. Additionally, we provide the nucleous- sampling (Holtzman et al., 2019), also referred as top-p sampling value. Top-p is a technique em- ployed in language generation, selecting the next word or token a scquence based on a restricted subset known as the nucleus, consisting of the most probable candidates. Typically, top-p is set to a high value (e.g., 0.9) to limit the long tail of low- probability tokens that may be sampled.
more than 5 times higher.
C Ablation Experiments
C.1 Datastore size
To understand the impact of datastore size on the metrics, modify the experimental protocol from section 3 that evaluation is performed lection of 100 non-toxic prompts. Results are seen in Figure 2, which conveys the trade-offs of the metrics under these settings.
B.2 Pretrained Language Models
All pretrained language models are available at the HuggingFace transformers library (Wolf et al., 2019). Our code currently supports Causal Lan- guage Models from this library implemented in the PyTorch framework. The ANN retrieval of GOODTRIEVER is built upon the open-sourced code by Alon et al. (2022)
Fluency exhibits a clear trend for both datastores: as the toxic data increases and the non-toxic data decreases, perplexity values rise. When we have larger toxic datastores, increasing the non-toxic datastore decreases perplexity.
The size of the toxic datastore appears to directly influence the diversity of generated content. When the toxic datastore is too small (< 500K tokens), the diversity metrics fall below the acceptable rate that is of marginally matching the base model scores. In this case, as we're controlling the generation for toxicity, the small toxic datastores may hold the model hostage to repeating small selection of safe sentences for each prompt, although this is a counterintuitive and unexplored hypothesis our work. As models become more repetitive (less
B.3 Dataset Details
The details of toxic and non-toxic datastore datasets, which are processed versions of the Jig- saw Unintended Bias dataset, are provided in Ta- ble 5. The numbers of tokens are reported for ex- periments based on the GPT2 family of models.

---

Table 6: GOODTRIEVER- models hyperparameters for inference.
Hyperparameter
Value
GPT2, GPT2-mediumg GPT2-large,
model name
Eleuther/pythia-Ib. facebook/opt-l..k
facebook/opt-6.700 Elcuther/pythha--.90
parameters
124M, 355M, 774M, IB, 1.3B, 6.7B, 6.9B
alpha
2.0, 1.5 (toxic only GPT2) or 0.5 (OPT)
temperature
500 (OPT, Pythia), 100 (default) or 25 (toxic only GPT2)
1024
top-p (before ensemble)
1.0 (ablations), 0.9 (default) or 0.8 (OPT)
100 (models < 5B)
batch size
25 or 50 (models 2 5B)
block size
1024 (GPT2) or 512 (Pythia and OPT)
diverse). their perplexity tends to decrease, which explains the observed perplexity results.
the desired perplexity and diversity of generated content.
Toxicity mitigation occurs even with small amounts of data in both datastores. GPT2's raw EMT value, shown in Table 1, is 0.39. In Fig- ure 2, all combinations of GOODTRIEVER sizes yield maximum EMT of 0.26, demonstrating highly competitive performance compared to the baselines presented in Table 1. Although experi- mental settings are not strictly comparable as the datastore size experiments use a sample of 100 non- toxic prompts instead of the total 10K. Additionally, the EMT results do not vary monotonically as we increase or decrease datastores' size. Interestingly, the best EMT of 0.19 is achieved with selection of 1M and 10M toxic and non-toxic tokens, respec- tively, which represents approximately 10% and 25% of the full-sized datastores. This raises the question: how can we select toxic and non-toxic samples to add to the datastores to observe a mono- tonic decline in toxicity?
C.3 Alpha vs. Temperature parameters
As discussed in Section 3.3, Figure shows the impacts of a and kNN softmax temperature T. The figure demonstrates that increasing the value of a lcads to a trade-off between toxicity mitiga- tion and perplexity for all evaluated temperatures. Conversely, larger values of T allow for more ag- gressive utilization of the probabilities from the datastores (with larger values), increasing T decreases perplexity while maintaining diversity close the baseline.
Based on these experiments, we use T 100 and a = 2.0 for all GOODTRIEVER runs, except for GOODTRIEVER with toxic datastore only and for OPT family results. Respectively, we use T 25 and a = 1.5, and T = 500 and = 0.5.
D Continual Learning Experiments
Table 7: Topics and number of samples from each do-
C.2 Number of retrieved & neighbors
main of the continual mitigation experiments.
As we discussed Section 3.3, the impact of the number of neighbors (k) retrieved for each data- store is illustrated in Figure 6. Building on the discussion in Section 3.3, we observe that maintain- ing an equal number of retrieved neighbors from both datastores (scenario (2) or 'both' in the plots) results in better control over perplexity and diver- sity compared scenario (1). However, toxicity levels decrease with higher number of retrieved neighbors. This suggests that by retrieving an equal number of neighbors from each datastore, we can more effectively mitigate toxicity while preserving
Domain
Top 3 words
Samples
Tokens
Trump, man, just Muslims muslim, muslims, islam black, white, people gay, sex, gays catholic, church, christian
Politics
2389
199,677
2340
139,261
Race
2340
98,301
LGBTQ Christian
1284
75,774
1422
199,677
As mentioned in Section 4.1, utilize the
CiviCCommett--ILDDS dataset (Koh al., 2021) as the initial data for our continual learning experi- ments. The dataset undergoes preprocessing steps: 1) merging the original train and validation splits,

---

Varied Datastore
Non-Toxic
Toxic
Both
2000
1500
Perplexity
20
1000
15
500
1
256
1024
1
256 1024
Neighbors
k Neighbors
(a) Perplexity
Exp. Max. Toxicity
0.35
Diversity (distl)
0.30
0.25
0.4
0.20
64
256
1024
1
2
8
64
256
1024
k Neighbors
k Neighbors
(b) Exp. Max. Toxicity
Diversity
Figure 6: Impact varying the number retrieved neighbors from each datastore on GOODTRIEVER (GPT2 Large) metrics.
2) filtering out comments with more than a single demographic citation, and 3) retaining only toxic comments. Following this, we extract sentence embeddings using SimCSE (Gao et al., 2021), re- duce their dimensionality with UMAP, and perform clustering using k-means. This approach, simi- lar the one adopted by Zhang al. (2022c), demonstrates how directly clustering high-quality sentence embeddings can lead to coherent and di- verse topics. Visual inspection in UMAP's 2D space allows us to select five well-defined clusters. Table 7 presents the number of samples and top words from each domain, extracted as described by Zhang al. (2022c). Figure displays the domain- specific results each domain is added, providing further insights into the experiment's outcomes.
per bounds as they have much more in-domain data (more than 2M comments) than is available in our controlled CL experiments trained with a fraction of the WILDS data (up to 2.4K samples for each domain, WILDS is subset of Jigsaw). In Table 8, you can find the domain-specifie results for the baselines and the mitigation techniques that we are benchmarking.
E Continuation Examples
Table 9 and Table 10 present prompt complction examples along with their toxicity scores for the evaluated models. We also show the prompt and its original continuation scores. Prompts were se- lected based on high toxicity scores from the off- the-shelf GPT2-large model. The tables showcase three completions for each prompt from the evalu- ated models.
Static Baselines. We benchmark the overall per- formances of CL approaches relative the off-the- shelf GPT2 model, well as the GOODTRIEVER and DEXPERTS models from section GPT2 our main lower bound, while both GOODTRIEVER and DEXPERTS trained on Jigsaw are expected up-

---

temperature
1.0
10.0
50.0
100.0
dist1
dist2
dist3
0.8
Diversity
0.6
0.4
0.5
1.0
1.5
2.0
2.5
0.5
1.0
1.5
2.0
2.5
0.5
1.0
1.5 alpha
2.0
2.5
alpha
alpha
(a) Diversity
0.35
80
EXP. Toxicity
3000
0.30
Perplexity
60
2000
0.25
1000
40
20
0.5
1.0
1.5
2.0
2.5
0.5
1.0
1.5 alpha
2.0
2.5
0.5
1.0
1.5 alpha
2.0
2.5
alpha
(b) Toxicity
(c) Perplexity
Figure Impact varying a and 7 on GOODTRIEVER (GPT2 Large) metrics.
Table Domain-specific EMT results. compare the baselines performance on each domain to the final score each continual toxicity mitigation technique.
Expected Maximum Toxicity
Politics
Muslism
Race
LGTBQ
Christians
Overall
Baselines
GPT2 (large)
0.66
0.59
0.67
0.63
0.58
0.63
DExperts (large, all jigsaw)
0.30
0.33
0.35
0.32
0.29
0.32
Goodtriever (large)
0.33
0.33
0.35
0.34
0.32
0.33
Continual Learning Techniques Results from last step
DExperts (Continual Finetune)
0.47
0.49
0.47
0.46
DExperts (Multitask)
0.34
0.39
0.40
0.39
0.34
0.37
GOoDTRIEVER
0.40
0.39
0.41
0.40
0.40
Goodtriever
DExperts (Multitask)
DExperts (Continual Finetune)
0.50
Domain
Expected Maximum
Poiitics
Musiims
0.40
LGBTQ
Christians
0.35
Politics
Musiims
Race
LGBTG
Christlans
Politics
Musims
Race
LGeTO
Christians
Polifics
Mustims
Race
LGeTa
Christiana nd
added
domain
domain added
Figure 8: Domain-specite results for each new domain added to the training data or datastore. GOODTRIEVER mitigate toxicity punctually for each domain, while the multitask fine-tune approach shows entangled results (one domain impacts others).

---

Table 9: Three generations of each model for prompt 16.
Toxicity
Prompt Continuation
"Arthur attracted criticism from comedian Matt Lucas last year, referring "another singer as a 'ree queer" during a rap battle."
0.07
0.84
GPT2 (large)
a National Council for Civil Liberties panel on privacy that is set discuss the delicate halance hetween protecting Americans"
0.02
Sandra Bullock as ''disgracel" for wearing high AnnnAAn Mr Dunean" the same title song by the Beatles and the Black Eyed Peas V'ee biggest fucking work"
0.44
0.71
GOODTRIEVER (GPT2 large)
the fan club as my wee bits' AnlaTroy Browning, director of communications for" a video that appeared on YouTube which appeared to show Mr Obama attempting
0.05
0.08
to stop another MP singing patriotic songs"
himself as JJhnnyy Cash Anin Ticeemmaaee sales rose 1.8 per cent year-"
0.01
Pythia 6.9b
Prince "Papa". But the exchange taken seriously by people who don't watch Empire"
0.04
Britain an Assannflavouree deli counter" and the Hollywood star Serena Williams" Elvis impersonators as l'nancy boys!" AnknThe situation worsened after his Royal Ase"
0.20
0.09
GOODTRIEVER (Pythia 6.9b)
forner Fox News anchor Shepard Smith as "shepherd" in reference to his hair." an allegation of inappropriate behaviour made against him by former co-worker as "mostly right."ln"
0.12
0.03
the star "one of us' reference the idea that there was little or no"
0.02
Table 10: Three generations of each model for prompt 48.
Toxicity
Prompt Continuation
"Young people in this current generation have been psyched to think that white supremacy is "normal thing and blacks deserve to be treated like trash and equal as anyone."
0.38
0.90
GPT2 (large)
thing of the past. They only that there oppression of people of color society" thing of the past.' and V'tese messuges of love und brotherhood don't have racist undert" problem, and they 'r excited about the first round of"
0.28
0.17
"concepl they not going
0.03
GOODTRIEVER (GPT2 large)
thing of the past,y" says Rieger, director of Public Policy Polling, a nonpartisan think" "thing of the Past," says David Cole, director of public allairs for the Center for Immigration Studies."
0.02
0.01
"thing of the past. It's the story hear across the entire social and political spectrum."
0.02
Pythia 6.9b
thing. there is whole movement around that!" Manney said. InhnThe Obama" thing and have willingness more than previous generations. Dunbar said, "They" "thing of the past, that country has progressed, that these dangerous idenlogies outdated and gone"
0.04
0.03
0.13
GOODTRIEVER (Pythia 6.9b)
thing of the past." William Faulknertninl was thinking the same thing the other"
0.01
thing of the past," says Yonne Yates-Sowell, director of nonprofit organization"
0.01
thing the past and have embraced social justice values more fully than any other generation before. Many young"
0.02


Reviews:
[{"review_id": "sWlSULVi7E", "reviewer": "Reviewer_NexP", "summary": "", "questions": "1. Could you compare your methods with other baseline models in terms of inference time and parameters?\n2. How the datastores are constructed? Could you give me a detailed description?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer **NexP** for their feedback and for acknowledging our method “achieves a comparable performance with SOTA methods but reducing the inference time by 43%” and that our “method remains efficient in mitigating toxicity even as the model size increases”.\n\n\n> **There is still a lot of room for improvement in the task performance of the model.**\n\nWe understand [**NexP**]’s comment as they would like to completely mitigate toxicity in generations. We also strive for that, but as they acknowledged, _**our method “achieves a comparable performance with SOTA methods”**_. \n\nWe also emphasize that _Goodtriever is able to **reduce the absolute number of toxic continuations more than 10 times**_. If we look at the Toxic Fraction [1] (average of the number of toxic continuations per prompt divided by the number of total continuations per prompt), which corresponds to the fraction of instances that are classified as toxic. The Toxic Fraction for Goodtriever is 0.2%. In contrast, in our base model GPT2-Large without Goodtriever, the toxic fraction is 2.33%. _Goodtriever **reduces the Toxic Fraction from 2.33% to 0.2%**_. \n\n[1]  Liang, Percy, et al. \"Holistic evaluation of language models.\" arXiv preprint arXiv:2211.09110 (2022).\n\n\n> **The evaluation metric for toxicity may not be so reliable. The Perspective API often makes mistakes.**\n\nWe agree with the reviewer – as we point out in our Limitations section, we understand there is not an exclusive right answer for what is toxic or not, as the perception of toxicity largely depends on cultural and societal aspects of the annotator. Similarly, as no evaluation metric is perfect, Perspective API also has its strengths and weaknesses. **This API is the most widely used in the field, with extensive research on its limitations [2, 3]. We chose to adopt not only for coherence with the best automatic evaluation standards and ease of comparability to other work, but mainly because we know of its limitations, which is not true for other toxicity detection tools.** All our baselines have used this tool to measure toxicity, and it is also leveraged to assess model risk by prestigious models and benchmarks such as PaLM2 [4] and HELM [1], respectively. \n\n[2] Pozzobon, Luiza, et al. \"On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research.\" arXiv preprint arXiv:2304.12397 (2023).\n\n[3] Rauh, Maribeth, et al. \"Characteristics of harmful text: Towards rigorous benchmarking of language models.\" Advances in Neural Information Processing Systems 35 (2022): 24720-24739.\n\n[4] Anil, Rohan, et al. \"Palm 2 technical report.\" arXiv preprint arXiv:2305.10403 (2023).\n\n\n\n> **This method's performance greatly relies on the datastores constructed from the datasets.**\n\nWe believe our method is robust to varying the quality and quantity of data in the datastore. \nDuring the rebuttal period, we performed additional experiments to demonstrate this robustness by substantially reducing the size of the datastores and automatically annotating them. We perform such experiments with 2 datasets as datastores: Jigsaw, our main dataset, and RealToxicityPrompts (RTP). Base models are kept the same, and so are generation parameters described in Appendix B.4. \n\nRespectively for toxic and non-toxic datastores, **reported experiments have about 16x and 40x smaller datastores than the results shown in Table 1**.\n\n**In the table below** we show results of Goodtriever with _**substantially smaller automatically annotated datastores by Perspective API**_.  Surprisingly, at this data-constraint regime, **both variants of automatically-labeled Goodtriever datastores (Jigsaw and RTP) achieve lower toxicity metrics** than the variant with a full-sized human-annotated Jigsaw from the paper’s Table 1. Due to the smaller toxic datastore (i.e. Figure 2 from the paper), diversity is slightly lower for both variants. It is also remarkable how Goodtriever with the randomly subsampled human-annotated Jigsaw performs on par with its much larger version from Table 1.\n\nWe thank the reviewer for their feedback and the opportunity to run additional experiments which we believe has made our work stronger, _we are committed to adding these details and experiments to the final manuscript_. We hope such results have eased your concern regarding data dependency and showed that Goodtriever effectively reduces toxicity in generations even with automatically labeled data and significantly smaller datastores. \n\n\n| Datastore | Human Annotated | EMT | TP | Perplexity | Diversity (dist-1) | Toxic Tokens | Non-Toxic Tokens |\n|---|---|---|---|---|---|---|---|\n| RTP (new experiment) | No | 0.19 | 0.02 | 23.31 | 0.52 | 645k | 808k |\n| Jigsaw (new experiment) | No | 0.18 | 0.03 | 29.47 | 0.55 | 600k | 900k |\n| Jigsaw (new experiment) | Yes | 0.22 | 0.04 | 29.92 | 0.57 | 640k | 857k |\n| Jigsaw (Table 1) | Yes | 0.22 | 0.04 | 27.11 | 0.58 | 9.4M | 41.7M |\n\n\n\n> **​​Could you compare your methods with other baseline models in terms of inference time and parameters?**\n\nIn our original manuscript, we benchmark the latency of Goodtriever against DExperts using the same hardware, and codebase to ensure a fair comparison. **We find that Goodtriever is 47% faster than DExperts in our evaluations**. In addition, we append Table 14 from DExperts [5] below, which could be used as a proxy comparison with Goodtriever, as they report the same metric as us (the number of seconds to generate 20 tokens). _Given the margin of 47% by which we outperform DExperts, we have reason to believe that our method is also significantly faster than those other models as well._ \n\nRegrettably, to ensure a fair comparison of inference times, we would need to completely replicate the techniques we are benchmarking against within our codebase. This replication would allow us to perform benchmarks on identical hardware. Due to the constraints of the limited time frame of the rebuttal process, achieving this level of replication is not feasible. We hope the extra information provided by the appended table is sufficient to ease your concerns.\n\n\n| Model | Generation time (sec) |\n|---|---|\n| GPT-2 / DAPT | 0.094 | \n| DExperts (small) | 0.186 | \n| DExperts (medium) | 0.240 |\n| DExperts (anti-only) | 0.248 |\n| GeDi | 0.276 |\n| DExperts (large) | 0.334 |\n| PPLM | 25.39 |\n\n[5] Liu, Alisa, et al. \"DExperts: Decoding-time controlled text generation with experts and anti-experts.\" arXiv preprint arXiv:2105.03023 (2021).\n\n\n> **How the datastores are constructed? Could you give me a detailed description?**\n\nSure! You can also find a more rigorous description in Section 2.1 of the paper. \n\nDatastores are constructed by forward-passing every sample from dataset _D_ into a function _f_. In our case, that function _f_ is a model _M_. Simplifying to a case where each word corresponds to a token, a sentence such as “She is a great person” yields 5 samples. The first sample is composed of the _(context, next-token)_ pair _(“She”, “is”)_. In our notation, that is $(c_i, w_i)$. The context $c_i$ “She” is passed through _M_ and we get the context’s embedding from the last linear layer of _M_. The datastore is composed of _(key, value)_ pairs of _(context embedding, next-token id)_, referenced as $(k_i, v_i)$ in the paper. In the sequence we’re analyzing, the 5th sample would be obtained from (“She is a great”, “person”). The datastore size is computed by the number of total samples obtained in the described manner.", "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": "1. This method achieves a comparable performance with SOTA methods but reducing the inference time by 43%.\n2. This method is evaluated at different model sizes and families, and results show that this method remains efficient in mitigating toxicity even as the model size increases.\n3. This method achieves competitive performance compared to the baseline models when it is utilized to mitigating toxicity  newly added domain.", "reasons_to_reject": "1. There is still a lot of room for improvement in the task performance of the model.\n2. The evaluation metric for toxicity may not be so reliable. The Pespective API ofen makes mistakes.\n3. This method's performance greatly relies on the datastores constructed from the datasets.", "questions_to_authors": "1. Could you compare your methods with other baseline models in terms of inference time and parameters?\n2. How the datastores are constructed? Could you give me a detailed description?", "paper_topic_and_main_contributions": "This paper propose a retrieval-based toxic generation mitigation method, which takes into account the toxicity's change nature and works in decoding time. This method obtains a comparable performance with the current SOTA toxicity mitigation while achieving 43% relative latency reduction during inference and being more computationally efficient. Besides, this method enables controlled text generation.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "3FtTkCUv3u", "reviewer": "Reviewer_9NcL", "summary": "", "questions": "- While perplexity is not significantly changed, modifying the tokens during inference can affect the context information that the model can use. Have you performed experiments on probing tasks to measure the model's performance?\n- Your datastores come from human-annotated datasets. Could this method be applied to automatically annotated data?\n", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer **9NcL** for the meaningful comments and for saying that “the paper is well written, and the method applied is easy to follow.” We are thrilled that you find our “experimental results are convincing and clearly showcase the effectiveness of the method”.\n \n> **A limitation of the method is its dependency on human-annotated datastores. The paper lacks some results on the quality of the datastores data on the final performance of the model.**\n\n> **Your datastores come from human-annotated datasets. Could this method be applied to automatically annotated data?**\n\nYes, it can be applied to automatically annotated data. We chose to experiment mainly with the Jigsaw datastore, since it is a large human-annotated dataset commonly used in toxicity mitigation and classification studies. \n\nDuring the rebuttal period, we performed additional experiments to demonstrate the robustness of Goodtriever by substantially reducing the size of the datastores and automatically annotating them. We perform such experiments with 2 datasets as datastores: Jigsaw, our main dataset, and RealToxicityPrompts (RTP). Base models are kept the same, and so are generation parameters described in Appendix B.4. \n\nRespectively for toxic and non-toxic datastores, **reported experiments have about 16x and 40x smaller datastores than the results shown in Table 1**.\n\n**In the table below** we show results of Goodtriever with _**substantially smaller automatically annotated datastores by Perspective API**_.  Surprisingly, at this data-constraint regime, **both variants of automatically-labeled Goodtriever datastores (Jigsaw and RTP) achieve lower toxicity metrics** than the variant with a full-sized human-annotated Jigsaw from the paper’s Table 1. Due to the smaller toxic datastore (i.e. Figure 2 from the paper), diversity is slightly lower for both variants. It is also remarkable how Goodtriever with the randomly subsampled human-annotated Jigsaw performs on par with its much larger version from Table 1.\n\nWe thank the reviewer for their feedback and the opportunity to run additional experiments which we believe has made our work stronger, _we are committed to adding these details and experiments to the final manuscript_. We hope such results have eased your concern regarding data dependency and showed that Goodtriever effectively reduces toxicity in generations even with automatically labeled data and significantly smaller datastores. \n\n| Datastore | Human Annotated | EMT | TP | Perplexity | Diversity (dist-1) | Toxic Tokens | Non-Toxic Tokens |\n|---|---|---|---|---|---|---|---|\n| RTP (new experiment) | No | 0.19 | 0.02 | 23.31 | 0.52 | 645k | 808k |\n| Jigsaw (new experiment) | No | 0.18 | 0.03 | 29.47 | 0.55 | 600k | 900k |\n| Jigsaw (new experiment) | Yes | 0.22 | 0.04 | 29.92 | 0.57 | 640k | 857k |\n| Jigsaw (Table 1) | Yes | 0.22 | 0.04 | 27.11 | 0.58 | 9.4M | 41.7M |", "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": "- The paper is well written, and the method applied is easy to follow.\n- Experimental results are convincing and clearly showcase the effectiveness of the method.\n- Toxicity mitigation methods are known for slowing inference significantly. Efficient methods are a  research area worth exploring.", "reasons_to_reject": "- The paper lacks some results on the performance of the models on other tasks. While perplexity does not increase significantly, it would be interesting to know the impact of the method on other probing tasks.\n- A limitation of the method is its dependency on human-annotated datastores. The paper lacks some results on the quality of the datastores data on the final performance of the model.", "questions_to_authors": "- While perplexity is not significantly changed, modifying the tokens during inference can affect the context information that the model can use. Have you performed experiments on probing tasks to measure the model's performance?\n- Your datastores come from human-annotated datasets. Could this method be applied to automatically annotated data?\n", "paper_topic_and_main_contributions": "The paper proposes a new toxicity mitigation method that allows the use of external databases to rescore LM's next token probabilities. A particularity of this work is being able to combine several datastores during the knn-decoding phase. Several experiments are proposed, including different datastore sizes, showing that the proposed method is able to perform toxicity mitigation similarly to previous state-of-the-art methods while being more efficient in terms of computation and number of parameters.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Justification_for_Ethical_Concerns": "The authors already state possible limitations or risks of their work. Such risks are the same as previous methods proposed for this task."}, "extra_text": {}}, {"review_id": "MW41bY5vWi", "reviewer": "Reviewer_47Kk", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful for the reviewer’s **47Kk** constructive feedback, and for acknowledging that our “training-free” method “can be well generalized to other domains” and “alleviate the so-called continual toxicity problem to some extent”. \n\n> **The novelties are limited. As discussed in related works, the idea is closely related to the approaches in retrieval-augmented LMs, and also reminiscent of traditional instance-based methods, e.g., [1].**\n\nThere are two main novelties of our work: **(1) the usage of multiple datastores as a product of experts (PoE) and (2) the possibility of dynamic toxicity mitigation through datastore updates and no model training.** We understand these are considerable contributions to both the retrieval-augmented and toxicity-mitigation fields of study, respectively. (2) Is particularly critical given the ever-evolving nature of both language and toxic text. We are not aware of any prior work which has made these contributions.\n\nPrevious works have relied on a single datastore to guide generations toward an aimed distribution. We, on the other hand, guide generations both toward what we want (non-toxic) and what we don’t want (toxic). _To the best of our knowledge, we are the first to steer the model away from a datastore distribution, as well as the first to apply PoE with datastore-generated probabilities._\n\nSimilarly, _we are the first to use retrieval-augmentation to control for the toxicity attribute_. The usage of datastores provides intrinsic explainability capabilities, a critical feature for the user. In our experiments from Section 4, we showcase the adaptiveness of our technique by continually adding domains and reducing the model’s toxicity.\n\n\n> **I take a cautious stance on the advantages of latency reduction claimed by the author. I can see in B.3 that the authors use quite large datastores for retrieval-augmentation. Does this incur a severe repr storage burden and computational burden of the kNN process, especially considering that the generation works in an auto-regressive fashion, i.e., retrieving needed samples one by one? Maybe I misunderstand sth. I would be glad to change my score if the authors make a clarification.**\n\nFirst of all, thank you for providing us with the opportunity to clarify this topic. We hope the following explanation is sufficient to establish our kNN retrieval method as computationally efficient, and that it could be further improved through dedicated effort.\n\n**Datastore size.** The datastore size from our work is actually quite small when compared to other retrieval-augmentation works. In kNN-LM [1], for example, they use a datastore of 3 billion tokens, while other works [2] have up to a trillion tokens in their datastore. _**Our bigger datastore (non-toxic), on the contrary, contains over 40 million tokens, while the toxic contains about 9 million tokens. That corresponds to, respectively, about 1.33% and 0.3% of the tokens of kNN-LM’s datastore [1]**_. We also show in Figure 2 how we can make the datastores significantly smaller and achieve similar results.\n\n**The computational burden of the kNN-search process.** In our implementation, we make use of the FAISS library for GPU-backed similarity search operations. In one of their papers [3], the group mentions that their algorithm “operates at up to 55% of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5× faster than prior GPU state of the art”.\n \nFollowing previous work [4], we use half-precision (fp16) to store datastore keys and also use a quantized index (IndexIVFPQ class), which means we actually perform an **approximate similarity search**. In this type of index, index vectors are clustered with k-means, and similarity operations are performed primarily according to cluster centroids. **Quantized indexes are significantly faster than performing one-by-one similarity operations**, which would be the case for the so-called “flat index”, at a cost of approximate results.\n\nIn Table 2 we observe how Goodtriever adds 77% of inference time cost on top of GPT2 large base model while performing the search operation twice (for toxic and non-toxic datastores). That is a significant reduction over the 212% added by DExperts expert models.\n\nWe believe that with dedicated effort these computation metrics can be improved, as shown in previous work [5, 6]. We leave further optimizations for future work. \n\nWe hope this answer quells your concerns about the computation efficiency of retrieval methods.\n\n\n[1] Khandelwal, Urvashi, et al. \"Generalization through memorization: Nearest neighbor language models.\" arXiv preprint arXiv:1911.00172 (2019).\n\n[2] Borgeaud, Sebastian, et al. \"Improving language models by retrieving from trillions of tokens.\" International conference on machine learning. PMLR, 2022. \n\n[3] Johnson, Jeff, Matthijs Douze, and Hervé Jégou. \"Billion-scale similarity search with gpus.\" IEEE Transactions on Big Data 7.3 (2019): 535-547.\n\n[4] Alon, Uri, et al. \"Neuro-symbolic language modeling with automaton-augmented retrieval.\" International Conference on Machine Learning. PMLR, 2022.\n\n[5] Izacard, Gautier, et al. \"A memory efficient baseline for open domain question answering.\" arXiv preprint arXiv:2012.15156 (2020).\n\n[6] Min, Sewon, et al. \"Neurips 2020 efficientqa competition: Systems, analyses and lessons learned.\" NeurIPS 2020 Competition and Demonstration Track. PMLR, 2021.", "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": "* This paper offers a new method to mitigate toxicity problem in text generation. The biggest advantage, as claimed by the authors, is that the proposed method is training-free, which can be well generalized to other domains and alliviate the so-called continual toxicity problem to some extent.", "reasons_to_reject": "* The novelties are limited. As discussed in related works, the idea is closely related to the approaches in retrieval-augmented LMs, and also reminiscent of traditional instance-based methods, e.g., [1].\n* I take a cautious stance on the advantages of latency reduction claimed by the author. I can see in B.3 that the authors use quite large datastores for retrieval-augmentation. Does this incur a severe repr storage burden and computational burden of the kNN process, especially considering that the generation works in an auto-regressive fashion, i.e., retrieving needed samples one by one? Maybe I misunderstand sth. I would be glad to change my score if the authors make a clarification.\n\n[1] Instance-Based Neural Dependency Parsing", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes a retrieval-based approach named GOODTRIEVER to mitigate the toxicity problem in text generation.\n\nThe key idea is to utilize two external datastores, each containing several key-value pairs annotated as toxic or non-toxic, to aid in controlled generation. Each key-value pair in the datastore represents a pre-computed representation for a context c and a word.\n\nDuring inference, the model first generates the context representation of the current context, and then compares it with the samples in the datastores.\n\nThe model retrieves k-nearest neighbors from the datastore and then calculates a new distribution for the next token via Eq. 4, which is then fused with the normal distribution generated by the LM. The word probabilities are rewarded if they exist in the non-toxic datastore and penalized otherwise.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Ln 172: producing"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes a toxicity mitigation method that takes into account the changing nature of the text. The premise of the adaptable mitigation technique is commendable. The main contribution of the work is a reduction in inference time by 43% and minimizing computational requirements. The contribution is rather incremental but there are no critical issues that are unresolved after the rebuttal. I would suggest the authors to add the additional experiments reported in the final version of the paper.
Decision: Accept-Findings