Bag of Tricks for Training Data Extraction from Language Models

Weichen Yu * 1 Tianyu Pang 2 Qian Liu 2 Chao Du 2 Bingyi Kang 2 Yan Huang 1 Min Lin 2 Shuicheng Yan 2

Abstract

19.7
20.8

Sampling
Strategy

With the advance of language models, privacy
protection is receiving more attention. Training
data extraction is therefore of great importance,
as it can serve as a potential tool to assess privacy leakage. However, due to the difficulty of
this task, most of the existing methods are proofof-concept and still not effective enough. In this
paper, we investigate and benchmark tricks for improving training data extraction using a publicly
available dataset. Because most existing extraction methods use a pipeline of generating-thenranking, i.e., generating text candidates as potential training data and then ranking them based
on specific criteria, our research focuses on the
tricks for both text generation (e.g., sampling strategy) and text ranking (e.g., token-level criteria).
The experimental results show that several previously overlooked tricks can be crucial to the
success of training data extraction. Based on the
GPT-Neo 1.3B evaluation results, our proposed
tricks outperform the baseline by a large margin
in most cases, providing a much stronger baseline for future research. The code is available at
https://github.com/weichen-yu/LM-Extraction.

34.4

(Sec 5.1)

33.5

Prob. Dist.
Adjustment

19.5
32.6

(Sec 5.2)

Exp. Bias
Reduction

46.7
24.6

(Sec 5.3)

Look-ahead

36.7

(Sec 5.4)

37.1

Sen. Level
Criteria

36.6

(Sec 6.1)

37.1

Tok. Level
Criteria
(Sec 6.2)

40.6
(%)

10

20

30

40

Figure 1. Overview for the bag of tricks explored in this work, with
an evaluation of precision (MP ). Bars in pink denote the methods
in the improved suffix generation, and bars in orange denote the
methods in the improved suffix ranking. The dashed bars indicate
the best method in each category.

age that hinders the widespread adoption of LMs (Carlini
et al., 2021; 2022; Lehman et al., 2021).
As privacy security has been an important issue of public
concern, a crucial topic is to develop efficient methods for
evaluating privacy leakage. Thus, the focus of our research
is on the adversarial task of training data extraction from
LMs, a relatively new area of study (Carlini et al., 2021;
Lehman et al., 2021). Existing extraction methods have
yielded successful records, but there are instances in which
these methods are even less effective than simply selecting
the most popular entity based on prior score. In addition,
successful data extraction requires a high generation ratio,
i.e., the need to generate and rank a large number of candidates in order to identify a single successful instance. These
suboptimal results suggest that, despite the viability of training data extraction and developed pioneering methods as
demonstrated in previous research, this task is still relatively
new with an abundance of problems to solve.

1. Introduction
Recent advances in language models (LMs) have led to impressive performance in a variety of downstream language
tasks (Kenton & Toutanova, 2019; Brown et al., 2020). It
has been demonstrated, however, that training data can be
extracted from LMs due to the memorization effects (Kenton & Toutanova, 2019; Carlini et al., 2019; Feldman, 2020;
Brown et al., 2020). These training data may contain sensitive information such as names, email addresses, phone
numbers, and physical addresses, resulting in privacy leak∗
Work done during an internship at Sea AI Lab. 1 Institute of
Automation, Chinese Academy of Sciences. 2 Sea AI Lab. Correspondence to: Tianyu Pang <tianyupang@sea.com>, Qian Liu
<liuqian@sea.com>, Yan Huang <yhuang@nlpr.ia.ac.cn>.

In this study, we aim to develop techniques for efficient
training data extraction. We adhere to the criteria of the
recent Training Data Extraction Challenge,1 which employs

Proceedings of the 40 th International Conference on Machine
Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. Copyright
2023 by the author(s).

1

1

Website link of Training Data Extraction Challenge

Bag of Tricks for Training Data Extraction from Language Models

a 1.3B parameter GPT-Neo model (Black et al., 2021) for
targeted extraction of 1-eidetic memorized data. Targeted
extraction refers to the scenario where a prefix of the data is
provided, such as ‘Yu’s phone number is’, and the adversary
attempts to recover the suffix ‘12345’. Accroding to Carlini
et al. (2021), κ-eidetic memorization is defined as the capacity of a language model to memorize a string that appears
κ times in the training material. The targeted and 1-eidetic
extraction poses a greater risk and is more challenging than
non-targeted and κ-eidetic (for κ > 1) settings.

provided an explanation for forgetting memorized examples.
Carlini et al. (2022) analyzed three factors that affect the
memorization of training data. For natural data distributions,
Feldman (2020) showed that label memorization is required
to achieve near-optimal performance. In the application of
model extraction, Lehman et al. (2021) indicated that pretrained BERT, when trained on clinical notes, poses a risk
of sensitive data leakage, especially when the data exhibits
a high level of repetition or ‘note bloat’ (Liu et al., 2022).
Jayaraman et al. (2022) proposed an active extraction attack
that employs canonical patterns and differential privacy to
defend against pattern extraction attacks.

Through ablation studies, we assess a variety of simple
techniques in natural language processing (NLP) and empirically evaluate their impact on successful extraction rates,
as in Figure 1. Our empirical analysis reveals that the extraction performance may be sensitive to the experimental
setup. With proper settings, the results show that several
previously overlooked tricks can contribute to significant
improvements of training data extraction. Based on the GPTNeo 1.3B evaluation results, our proposed tricks outperform
the baseline by a large margin in most cases, providing a
much stronger baseline for future research. Nonetheless,
utilizing more than one training data extraction trick does
not necessarily boost the performance, and in some cases,
even shows incompatibility and results in inferior precision.
These findings suggest that judicious selection and combination of the tricks are essential for optimal performance.

2.2. Membership Inference Attacks
The membership inference attack (MIA) (Shokri et al., 2017)
is another adversarial task in data protection that is closely
associated with training data extraction. It aims to determine whether a particular record is present in its training
dataset, given black-box access to a model. MIA has been
demonstrated to be effective on numerous machine learning tasks, including classification (Sablayrolles et al., 2019;
Choquette-Choo et al., 2021; Rezaei & Liu, 2021) and generation (Hayes et al., 2019; Hilprecht et al., 2019).
The methods utilized by MIA fall into two categories:
classifier-based methods and metric-based methods (Hu
et al., 2022). Classifier-based methods involve training a
binary classifier to recognize the complex relationship between members and non-members, with shadow training
being a commonly used technique (Shokri et al., 2017; He
et al., 2020; Wang et al., 2021). Metric-based methods, on
the other hand, make membership inferences by first calculating metrics on the model prediction vectors (Yeom et al.,
2018; Salem et al., 2018; Sablayrolles et al., 2019; Song &
Mittal, 2021; Choquette-Choo et al., 2021). Several defense
methods based on differential privacy (Leino & Fredrikson,
2020; Naseri et al., 2020; Choquette-Choo et al., 2021), data
pruning (Wang et al., 2021), data augmentation (Kaya &
Dumitras, 2021) and causal inference (Tople et al., 2020)
have been proposed to mitigate this vulnerability.

2. Related Work
We briefly introduce training data extraction, membership
inference attacks, and other memorization-based attacks.
2.1. Training Data Extraction
The extraction of training data from a pretrained language
model, also referred to as language model data extraction,
is a method for recovering the examples used to train the
model. Despite being a relatively new task, many of the
underlying technologies and analysis methods, including
membership inference (Shokri et al., 2017) and leveraging
network memorization for attacks (Thomas et al., 2020;
Leino & Fredrikson, 2020), were introduced much earlier.

2.3. Other Memorization-Based Attacks

Carlini et al. (2021) were among the first to define the concepts of model knowledge extraction and κ-eidetic memorization, as well as to propose promising training strategies
for data extraction. Both the theoretical properties of memorization and the application of model extraction in sensitive
fields, such as the analysis of clinical notes, have been the
focus of subsequent research in this field.

It has been discovered that large pretrained models are susceptible to memorizing information from the training data,
which can lead to a variety of attacks. In addition to training
data extraction and membership inference attacks, there are
other memorization-based attacks that target these models.
Model extraction attacks and the corresponding protection
methods (Tramèr et al., 2016; Juuti et al., 2019; Gong et al.,
2020; Wu et al., 2022) focus on the issue of duplicating the
functionality of a given model. In this type of attacks, the
adversary attempts to build a second model with a similar
predictive performance to the original black-box model.

Recently, Kandpal et al. (2022) demonstrated that in language models, the efficacy of data extraction is frequently
attributable to duplication in commonly used web-scraped
training sets. Using nondeterminism, Jagielski et al. (2022)
2

Bag of Tricks for Training Data Extraction from Language Models

The objective of attribute inference attacks is to extract specific personal attributes such as locations, occupations, and
interests from the model (Fredrikson et al., 2015; Gong &
Liu, 2016; Ganju et al., 2018; Parisot et al., 2021). The objective of property inference attacks is to extract properties
of the training data that the model producer may not have
intended to share, such as the environment in which the data
was generated or the proportion of the data that belongs to
a particular class. The primary distinction between training data extraction attacks and attribute/property inference
attacks is that attribute/property inference attacks do not
require prior knowledge of the attributes or properties to be
extracted, whereas training data extraction attacks require
the generated information to be identical to the training data
at the sentence level, which is more difficult and dangerous.

(Carlini et al., 2021), we assume the language model generates a suffix s by the most-likely criterion. Then we can
write a formal definition of targeted extraction as
Definition 1. (Targeted extraction) Given a prefix p contained in the training data and a pretrained language
model fθ . Targeted extraction is to generate the suffix by
s = argmaxs′ fθ (s′ |p).
As to κ-eidetic memorized data, we follow the definition
in Carlini et al. (2021) that the sentence [p, s] appears in at
most κ examples in the training data. In practice, the length
of the generated sentence is typically fixed using truncating
and concatenation techniques applied to the training dataset.
If a generated sentence is shorter than the specified length,
padding tokens are used to bring it up to the required length.
In this study, the generated sentence length is 100.

3. Preliminary

3.3. Evaluation Metrics

We recap the basic setups employed in our study. These
setups mainly follow the guidelines of the Training Data
Extraction Challenge. We then define the threat model and
evaluation metrics.

Non-targeted extraction has been evaluated using the number of memorized examples in previous studies (Carlini
et al., 2021). To evaluate more comprehensively, we use
three metrics to evaluate performance in this targeted data
extraction task, including precision MP , recall MR and
Hamming distance MH .

3.1. Basic Setups
Dataset. The dataset used in this study is a subset of 20,000
examples from the Pile’s training dataset (Gao et al., 2020).
Each example consists of a 50-token prefix and a 50-token
suffix. The attacker’s task is to predict the suffix given
the prefix. All the 100-token long sentences in this dataset
appear only once in the training set. For the purposes of
this study, we divide the dataset into a training set of 19,000
samples and a testing set of 1,000 samples.

Precision MP . The proportion of correctly generated suffixes over the total number of given prefixes is referred to
as precision MP . Notice that for a correct generation, the
suffix and ground truth must be identical in both sentence
length and generated tokens.
Recall MR . The proportion of correctly generated suffixes
over the total number of generated suffixes is indicated by
recall MR . The metric used in the Training Data Extraction Challenge is denoted by ej , which is defined as the
number of correctly recovered suffixes when the number
of incorrectly generated suffixes reaches a threshold of j.
ej can assess the effectiveness of the attack. We define
ej
MR = ej +j
, we will use MR instead of ej in the following
paragraphs. In our experiments, the value of j is chosen to
be proportional to the size of the test set, and it is set to 100
with a test set of 1,000 prefixes.

Language model. We employ the GPT-Neo 1.3B model implemented on HuggingFace Transformers (Wolf et al., 2020),
which is a transformer model designed using EleutherAI’s
replication of the GPT-3 architecture (Brown et al., 2020),
and trained on the Pile dataset. GPT-Neo is an autoregressive language model fθ parameterized by θ, which generates
a sequence of tokens x0 , x1 , · · · , xN via the chain rule
fθ (x0 , x1 , · · · , xN ) =

N
Y



fθ xn |x[0,n−1] ,

(1)

Hamming distance MH . The Hamming distance denotes
the difference between two equal-length strings, calculated
as the number of positions where the corresponding symbols
differ. We can quantitatively evaluate the similarity between
the generated suffixes and the ground truth using the average
Hamming distance, providing a token-level evaluation
of the
P
extraction methods’ performance. MH = N1 n xn ⊕ gtn ,
where a ⊕ b = 1 if a = b, else 0. N is the number of tokens
in a generated sentence, xn is the generated token, and gtn
is the corresponding ground truth token.

n=0

where x[0,n−1] = x<n = {x0 , · · · , xn−1 } for notation
compactness. At the sentence level, given a prefix p, we
denote the probability of generating a certain suffix s conditional on the prefix p as fθ (s|p).
3.2. Threat Model
In this study, we focus on the threat model of targeted extraction of κ-eidetic memorized data, where we choose κ = 1.
According to the model knowledge extraction defined in

We would like to point out that the commonly used metrics
3

Bag of Tricks for Training Data Extraction from Language Models

that measure the repetition or quality of generated sentences,
e.g., the Zipfian coefficient and REP (Welleck et al., 2019),
may not be aligned with the goal of training data extraction. Thus, we propose the metrics listed above, which are
specifically designed to assess the efficacy of training data
extraction methods. These metrics, MP , MR , and MH , are
closely linked with the success rate of the extracted data
rather than the quality of generated language.

Table 1. Results of MP , MR , and MH under different numbers of
beams in beam search. All results are reported on a single trial.

4. Pipeline Overview
A basic training data extraction pipeline can be divided into
two stages. The first stage is suffix generation, which means
coming up with a set of suffixes based on a prefix. The
autoregressive language model fθ computes the probability
of each token in a vocabulary list and generates the next
token by sampling from the probability distribution. A basic
strategy employed to control the number of generations is
limiting to the top-k tokens with k = 10, which means the
LMs only sample from the tokens with top-k probability.
The second stage is suffix ranking, which entails estimating
the likelihood of these suffixes and retaining only those with
a high probability. Typically this is accomplished through
the use of membership inference attacks, determined based
on the perplexity metric (Carlini et al., 2019; 2021) as
!
N
1 X
log fθ (xn |x[0:n−1] ) ,
(2)
P = exp −
N n=0

Beam

Memory

MP (%) ↑

MR (%) ↑

MH ↓

7
6
5
4
3
2
1

21531M
21379M
20731M
17051M
14333M
12023M
8783M

51.1
51.0
51.3
50.7
50.7
50.6
37.0

76.3
76.4
76.3
76.3
76.2
76.2
76.5

15.501
15.575
15.499
15.763
15.764
15.931
20.245

Figure 2. Histogram of token logits. The histogram depicts the
distribution of logit values obtained from 1,000 suffixes, each
containing 50 tokens. A spline interpolation technique is employed
to smooth the histogram, with the original histogram included in
the appendix for reference.

where N is the number of tokens in a generated sentence.
Here, padding tokens are not included in the calculation.
We explore and evaluate various tricks for both stages. The
results for suffix generation are presented in Section 5, and
the results for suffix ranking are presented in Section 6.

As an approximate solution, beam search only keeps a predetermined number of best partial solutions. Since it always
takes the top ones at each step, it is often criticized for
its lack of diversity (Holtzman et al., 2019). As shown in
Table 1, although the required memory is increasing, the performance gain from a larger beam width diminishes quickly
when the beam width exceeds two. Due to the randomness
in generation, sampling methods always introduce more
diversity than beam search. Therefore, we mainly study
sampling strategies below, including top-k sampling, nucleus sampling and typical sampling.

5. Improved Suffix Generation
To improve suffix generation, we analyze the logits distributions of both the ground-truth and generated tokens. As
shown in Figure 2, there is a significant difference between
the two distributions. To address this, we examine the effects
of a variety of NLP techniques, such as changes to sampling
strategies and probability distributions, as described below.

Top-k denotes the truncated sampling method with the
truncation set defined as the top-k highest-probability tokens (Fan et al., 2018; Holtzman et al., 2018; Radford et al.,
2019). Figure 3(a) demonstrates that a larger k may increase
the diversity of the generated sentences but also decrease
the precision and hamming distance. The result is consistent
with that observed in other NLP tasks Meister et al. (2022).

5.1. Sampling Strategy
The most popular decoding objective, especially for conditional generation in NLP tasks, is maximization-based
decoding. Based on the provided prefix, it searches for the
suffix with the highest likelihood. It is also suited for our
training data extraction task since the models are directly
maximized for the likelihood of the training data. However,
finding the theoretically argmax sequence from models is
intractable (Holtzman et al., 2019). The common practice is
using beam search (Freitag & Al-Onaizan, 2017) instead.

Nucleus-η (Holtzman et al., 2019) truncates the vocabulary
based on the summed probability. Namely, the smallest set
of the most likely tokens with total probabilities equal to
or greater than η are selected. Meister et al. (2022) demonstrate that lower values of η are more conducive to story
4

Bag of Tricks for Training Data Extraction from Language Models

Precision (%)

0

MH

20.6
20.4
20.2
20.0
19.8
19.6

52
50
48
46
44
42
40

Precision (%)

Typical-ϕ (Meister et al., 2022) recommends selecting a
token with information content similar to the expected information content. Namely, it guarantees the probability mass
from the original distribution that will be taken into account
in typical decoding. This sampling strategy can improve
sentence consistency while reducing degenerate repetitions.
The typical-ϕ strategy is equivalent to a subset optimization
problem with an entropy rate constraint. Typical-ϕ exhibits
a non-monotonic tendency, which is similar to its effect on
abstract summary and story generation tasks.

42
41
40
39
38
37

MP

21.5
20.5
19.5
18.5
17.5
16.5

52
49
46
43
40
37
0 0.2 0.4 0.6 0.8 1
(c) Typical-ϕ

19.5
19.0
18.5
18.0
17.5
17.0
16.5
0 0.2 0.4 0.6 0.8 1
(b) Nucleus-η

10 20 30 40
(a) Top-k
Precision (%)

Precision (%)

generation. In contrast, the extraction task is optimal at
η ≈ 0.6, which yields a 31% improvement in extraction
precision over the baseline. Larger or smaller η both shows
decreased performance (Figure 3(b)).

58.0
50.0
42.0
34.0
26.0
18.0
10.0

50
40
30
20
10
0
10−1
100
101
(d) Temperature

Figure 3. Experimental results under different values of top-k,
nucleus-η, typical-ϕ and temperature T . All results are reported
on 5 trials. The y-axis left denotes precision (%)(↑), and right
denotes Hamming distance (↓).

5.2. Probability Distribution Adjustment

Token Precision (%)

Besides the sampling strategies that truncate the sampling
distribution, we introduce in this section another strategy
that directly adjusts the probability distribution fθ (xn |x<n ).
Below we introduce two tricks, adjusting the temperature
and repetition penalty, to refine the distribution. These tricks
bring nearly no additional computation cost but can still lead
to a performance improvement.
Temperature T (Hinton et al., 2015) is a technique of local
renormalization with an annealing term. A higher temperature T > 1 results in decreased confidence in the language
model’s predictions but may also increase the diversity of
the generated suffixes. The study conducted by Carlini
et al. (2021) found that gradually decreasing the temperature throughout the generation process can be beneficial.
The effect of the temperature is presented in Table 2. It is
important to note that as the temperature is increased, the
number of generated suffixes required to include the ground
truth also increases, causing the efficiency to degrade. It is
important to find a balance between diversity and efficiency.

90
80
70
60
50
40
30
0

5

10 15 20 25 30 35 40 45 50
Generated Token Length

Figure 4. Generated token length w.r.t. token precision (%) for the
n-th generated token. The generated suffix length is 50.

‘number of Yu is 12345’ or ‘Yu’s address is at XXX. The
phone number of Yu is 12345’. The prefix in the training
set, as in Table 12, is not always a complete sentence. To
better mimic the training settings, we propose to adjust the
context window size and adjust the position shifting.
5.3.1. DYNAMIC C ONTEXT W INDOW

Repetition penalty is constructed on the conditional language model (Keskar et al., 2019). A repetition penalty is
introduced by locally modifying the generation probability
of each token based on whether it is a repetition of the previous token. The logit of the repeated token is divided by
a value r before entering the softmax layer. Setting r > 1
penalizes repetition while r < 1 encourages it. Our results
in Table 3 show that repetition penalty has mostly negative
effects on the task of training data extraction.

The length of the training window may differ from the length
of the extraction window. As a result, we propose adjusting
the context window size, i.e. the number of previously
generated tokens, as shown in Eq. (3). Furthermore, we
encourage the results of different context window sizes to
collaborate in determining the next generated token as
fθ (xn ; W)





 (3)
= hW fθ xn |x[n−w1 ,n−1] , ..., fθ xn |x[n−wm ,n−1] ,

5.3. Exposure Bias Reduction

where hW denotes the ensemble method, W denotes the
ensemble hyperparameter, including the number of different
context window sizes m and each window size wi . We use
m = 4 and wi ∈ {n, n − 1, n − 2, n − 3} in our methods.
Carefully chosen hyperparameters m and wi may improve
the performance even more.

For efficient vectorization, it is common practice to pack
multiple sentences into a fixed-length sequence when training language models. As an example, consider the sentence
‘The phone number of Yu is 12345’ may be truncated or
prefixed with another sentence in the training set, such as
5

Bag of Tricks for Training Data Extraction from Language Models
Table 2. Results of MP , MR , and MH under different temperature.
Temperature = 1 is the baseline. All results are reported on 5 trials.
Temperature

MP (%)(↑)

MR (%)(↑)

MH (↓)

Varying

48.0

76.3

19.614

0.3
1

48.9
37.0

76.4
76.5

16.341
20.245

Table 3. Results of MP , MR , and MH under different repetition
penalty. Repetition penalty r = 1 is the baseline. All results are
reported on 5 trials.
Figure 5. Histogram of the rank of the ground truth perplexity. The
x-axis represents the rank of the ground truth perplexity within a
list of 100 suffix perplexities.

We present two implementation options. The first option, as
specified in Eq. (4), entails computing the probabilities generated by utilizing various lengths of previously generated
tokens x[n−wi ,n−1] , and then producing the final probabilities via a weighted average sum of these probabilities, as


Pm
i=1 ϵi fθ xn |x[n−wi ,n−1]
Pm
fθ (xn ; Ww ) =
, (4)
i=1 ϵi

fθ (xn ; Wv ) =
(
V(fθ (xn )) =

m i=1

V(fθ (xn |x[n−wi ,n−1] );

MP (%)(↑)

MR (%)(↑)

MH (↓)

0.9
1
1.1
1.2
1.3
1.5

19.8
37.0
37.3
37.1
36.7
34.7

66.4
76.5
76.5
76.5
76.4
75.7

27.927
19.614
20.181
20.323
20.332
21.154

ensemble methods can significantly improve on the baseline approach, achieving improvements of 143% and 139%,
respectively, and we discovered that the weighted average
strategy performs slightly better than the voting strategy.
One common failure mode observed is that when a wrong
token is generated, it causes subsequent tokens to also be
wrong (exemplars shown in Table 12). The window size
ensemble introduced here can help reduce this problem.

where Ww denotes the hyperparameters in the solution,
comprising of m, wi and ϵi . ϵi denotes the weighting coefficient of each probability. The second option as specified
in Eq. (5), is based on a voting mechanism, in which each
model trained with a distinct context window length casts
its vote for the tokens it is most confident in, formulated as
m
1 X

Repetition penalty

5.3.2. DYNAMIC P OSITION S HIFTING
Positional embeddings are added to the token feature in
models like GPT-Neo. During training, this is added per
batch of sentences, causing the same sentence to have different offsets in positional embedding in different training
batches and during generation. To improve the extraction
of memorized suffixes, we propose to recover the positions
used during training by evaluating different shifted positions
and selecting the ’best’ one. Specifically, for a given prefix
p, we evaluate different position C = ci , where ci is a list of
consecutive natural numbers, ci = {ci1 , · · · }, s.t. |ci | = |p|
and calculate the corresponding perplexity values. The position with the lowest perplexity value is then chosen as the
position from which to generate the suffix as

(5)

ρ − R(fθ (xn )), if R(fθ (xn )) ≤ ρ;
(6)
0,
otherwise,

where V(·) denotes the voting function, R(·) denotes the
rank function, and it votes for the tokens that it has confidence in. Wv denotes the hyperparameters in the solution,
comprising of wi , m and ρ.
It is stated in Carlini et al. (2022) that the proportion of extractable sequences increases log-linearly with the number
of context tokens. We observed a similar phenomenon in
our experiments, where the generation accuracy of a token
decreases as the prefix becomes shorter. However, we discovered that combining multiple context window lengths
significantly improves accuracy. The probabilities produced
by different window lengths can be combined to significantly improve extraction accuracy. Our implementation
of Ww employs the weighting coefficient ϵi = 0.9i , and
Wv assigns [5, 4, 3, 2, 1] points to its top-5 confident tokens, ρ = 5. The results presented in Table 4 show that

c = argmin P(p, ci ); ϕ̂(xi ) = ψ(cn ) + ϕ(xn ),

(7)

ci ∈C

where ψ(·) denotes positional encoding layers, ϕ(·) denotes
the feature mapping function, ϕ̂ denotes the feature mapping
function consisting positional encoding, and P computes
the perplexity of the prefix. The experimental results are
presented in Table 4. C = {[0, 1, · · · , |p|], [1, 2, · · · , |p| +
1], · · · } is evaluated. The data show that, while using posi6

Bag of Tricks for Training Data Extraction from Language Models
Table 4. Results of MP , MR , and MH under context window
length adjustments. All results are reported on a single trial.

Table 6. Hyper-parameters selection for auto-tuning. Multiple configurations of final hyperparameters are found to yield equivalent
performances, and a representative example is presented.

MP (%)(↑)

MR (%)(↑)

MH (↓)

Baseline

19.5

65.6

26.948

Parameters

Range

Step

Initial

Final

Context Win Ww
Context Win Wv

47.4
46.7

77.6
77.5

16.993
17.164

Position Shifting

16.4

39.0

21.154

Top-k
Nucleus-η
Typical-ϕ
Temperature T
Repetition Penalty

[1, 50]
[0.1, 1]
[0.1, 1]
[0.1, 5]
[0.8, 1.3]

1
0.01
0.01
0.1
0.01

10
0.6
0.6
0.3
1

24
0.8
0.9
0.58
1.04

Table 5. Results of MP , MR , and MH under auto-tuning. All
results are reported on 5 trials.
Strategy
Baseline
Manual selection
Auto-tuning

MP (%)(↑)

MR (%)(↑)

MH (↓)

37.0
48.8
49.4

76.5
76.4
76.6

19.614
16.379
16.127

Table 7. Experimental results of MP , MR , and MH under lookahead. All results are reported on a single trial.

Baseline
Look-ahead

tion shifting improves the MH metric, it may have a negative impact on precision and recall.

Table 12 highlights a common issue encountered during the
training data extraction process, where only one or two tokens are incorrectly generated or placed in an inappropriate
position. To address this problem, we propose a technique
that involves looking ν steps ahead and using the probability of the subsequent tokens to inform the generation of the
current token. The goal of look-ahead is to use the posterior
distribution to help compute the current token generation
probability. We begin by presenting the precise mathematical formulation of the optimal probability and then introduce
the implementation, which employs an estimation due to
efficiency considerations. The posterior is calculated as
(8)

(9)

Track(xstart , xend |xcond )
X X
X
=
...
fθ (xstart |xcond )
x′end−1

26.948

33.1
35.5
36.7

71.6
72.6
73.0

24.262
22.157
21.333

(11)

The aforementioned tricks in Section 5.1 involve various
hyperparameters, and simply using the best parameters is
usually suboptimal. Manually searching for the best hyperparameters, also known as ’babysitting,’ can be timeconsuming. We use a versatile architecture auto-tuning
method (Akiba et al., 2019), which incorporates efficient
search and pruning strategies, to determine the optimized
hyperparameters following advanced frameworks (Snoek
et al., 2012; Koch et al., 2018; Akiba et al., 2019). As the
search algorithm, we use covariance matrix adaptation evolutionary strategies (CMA-ES) (Hansen et al., 2003). The
search objective in our experiment is set to MP , and the
parameters that are searched over include top-k, nucleus-η,
typical-ϕ, temperature T , and repetition penalty r.

where Track is calculated as,

x′start+1 x′start+2

65.6

5.5. Hyperparameter Optimization

More generally, let Track(xstart , xend |xcond ) be the probability product of the track starting from xstart and ending at xend ,
conditioned on xcond . Then we can write ν-step posterior as
Track(xn , xn+ν |x<n )
,
′
x′n Track(xn , xn+ν |x<n )

19.5

where R(·) is the rank function. The experimental results,
as detailed in Table 4, show that using a look-ahead strategy
leads to a significant improvement in precision. Furthermore, an increase in the value of λ results in a corresponding improvement in performance. It is important to note,
however, that increasing the value of λ also increases the
computational cost, with a complexity of O(λ · N ), where
N denotes the length of the generated tokens.

n

fθ (xn |xn+ν , x<n ) = P

MH (↓)

X = {x′n |R(fθ (x′n |x<n )) ≥ λ},

fθ (xn |xn+1 , x<n )
fθ (xn+1 |xn , x<n ) fθ (xn |x<n )
.
′
′
x′ fθ (xn+1 |xn , x<n ) fθ (xn |x<n )

MR (%)(↑)

Based on the 1-step look-ahead formulation in Eq. (8), we
utilize the posterior probability to calculate the confidence of
the current token. However, due to the size of the vocabulary
list (more than 50,000), we only select λ tokens whose
probability fθ (x′n |x<n ) ranks among the highest, as

5.4. Look-Ahead

=P

λ=10
λ=20
λ=30

MP (%)(↑)

(10)
fθ (xstart+1 |xcond , xstart )...

fθ (xend |xcond , xstart , x′start+1 ..., x′end−1 ).
7

Bag of Tricks for Training Data Extraction from Language Models
Table 10. Experimental results of precision, recall, and Hamming
distance for compatibility analysis. All results are reported on a
single trial.

Table 8. Experimental results of precision, recall, and Hamming
distance under different ranking methods. All results are reported
on a single trial.
Method

MP (%)(↑)

MR (%)(↑)

MH (↓)

Baseline

37.0

76.5

19.614

Perplexity ÷ Zlib
Perplexity × Zlib
Cumprod

37.1
37.1
36.6

76.0
76.3
39.8

20.191
20.368
20.127

High confidence
Surprised patterns

40.6
37.1

77.3
75.5

19.518
20.072

Context win + Beams=2
Auto-tuning + Beams=2
Context win + Auto-tuning
Context win + High confidence

MP (%) ↑

MR (%) ↑

MH ↓

19.5
46.8
33.5
54.8

65.6
77.5
76.6
81.5

26.948
17.144
20.921
13.621

MR (%) ↑

MH ↓

46.7
46.4
46.5
46.8

77.5
76.2
77.5
77.5

17.154
17.331
17.370
17.144

Cumprod. We consider alternative metrics such as the
cumprod, which depicts the
QNtandem probability of a generation sentence as Lc = ( n=0 log p(xn |x0 , .., xn−1 ))−N .
However, the effect of tandem probability on precision is
marginal and has a negative effect on MR .

Table 9. Experimental results of precision, recall, and Hamming
distance for compatibility analysis. All results are reported on a
single trial.
Baseline(GPT-Neo 1.3B)
Context win + High confidence(GPT-Neo 1.3B)
Baseline(GPT-Neo 2.7B)
Context win + High confidence(GPT-Neo 2.7B)

MP (%) ↑

6.2. Token-Level Criteria
Reward on high confidence. The presence of a high degree of confidence in memorized data is one of the defining
features of the phenomenon known as the’memorization
effect’ (Goodfellow et al., 2016; Zhang et al., 2021; Arpit
et al., 2017). We propose implementing a strategy that rewards suffixes with high-confidence tokens based on this
insight. If the sentence contains confident tokens, the possibility of the generated token is higher than a threshold, and
the difference between the generated token and other token
is higher than a threshold, we rank it higher. Specifically,
for the token xn in a generated suffix, if the probability of is
higher than a threshold 0.9, then we subtract a given number
0.1 from the score of suffix si (the original score for si is
its perplexity). This trick makes a noticeable improvement
as seen in Table 8.

Table 6 contains the detailed search parameter settings. We
also provide the baseline parameters as initial values to the
search algorithm to speed up convergence. The number of
search rounds is limited to 1,000. The experimental results
are shown in Table 5. Simply using the best parameters
outlined in Section. 5.1 with k=5, η=0.6, ϕ=0.6, T =0.4,
r=1 yields a precision of 48.8%. Implementing auto-tuning
results in a 37% improvement over baseline, and auto-tuning
performs slightly better than the hypermeter manual section.

6. Improved Suffix Ranking
Following the generation of multiple suffixes, a ranking process is carried out in which less likely suffixes are eliminated
using perplexity P as a metric. However, our statistical analysis in Figure 5 reveals that the ground-truth sentences do
not consistently have the lowest perplexity values. Thus, we
propose additional ranking metrics to address this disparity.

Encouraging surprised patterns. According to recent
studies (Holtzman et al., 2019), human text generation frequently exhibits a pattern in which tokens with high perplexity are intermittently included, as opposed to consistently selecting tokens with low perplexity. To address this
problem, we propose a simple solution that encourages the
presence of surprised tokens (high perplexity tokens) by
calculating the perplexity of a generated prompt based on
only the majority tokens:
1 X
log p(xn |x[0:n−1] ),
Ls =
|X̂ |
(12)
xn ∈X̂

6.1. Sentence-Level Criteria
Zlib. The entropy of a text, as determined by the Zlib
compression algorithm (Gailly & Adler, 2004) using the
number of bits, is a quantitative indicator of the sequence’s
information content. We use the ratio of the perplexity
of a given sentence as computed by the GPT-Neo model,
and the Zlib entropy of the same sentence as a metric for
membership inference, as outlined in the work of Carlini
et al. (2021). Besides, we investigate the potential utility
of producting perplexity and Zlib entropy, as both metrics
tend to decrease when the model shows a high degree of
confidence in its predictions. Both metrics produce only
a marginal improvement in the overall performance of the
membership inference task, as shown in Table 8.

X̂ = {xj |µ − 3σ < log p(xn |x[0:n−1]) ) < µ + 3σ},
where µ and σ denotes the mean and standard of the
p(xn |x[0:n−1] ) in a batch. The inclusion of surprised tokens
in a generation does not have a negative impact on overall
sentence perplexity when using this method, thereby increasing the likelihood of their selection during membership
inference. As demonstrated in Table 8, the improvement is
relatively marginal.

8

Bag of Tricks for Training Data Extraction from Language Models

6.3. Compatibility Analysis

Brown, Tom, Mann, Benjamin, Ryder, Nick, Subbiah,
Melanie, Kaplan, Jared D, Dhariwal, Prafulla, Neelakantan, Arvind, Shyam, Pranav, Sastry, Girish, Askell,
Amanda, et al. Language models are few-shot learners.
In Advances in Neural Information Processing Systems
(NeurIPS), 2020.

It has been discovered that accumulating the aforementioned
tricks does not always result in a proportionate increase in
performance. We investigate the interactive effects of combining various techniques in a targeted and efficient manner,
in order to establish a viable baseline method. The results
are shown in Table 10. The application of multiple of the
aforementioned techniques yields no significant improvement in performance. Even when using a beam width of 2
and maintaining the same settings as in the previous experiments, the results do not show a significant improvement.
The empirical findings presented in this section suggest that
to achieve optimal results, a more deliberate and strategic
combination of various methods is required.

Carlini, Nicholas, Liu, Chang, Erlingsson, Úlfar, Kos, Jernej,
and Song, Dawn. The secret sharer: Evaluating and testing unintended memorization in neural networks. In
Proceedings of the USENIX Conference on Security Symposium (SEC), 2019.
Carlini, Nicholas, Tramer, Florian, Wallace, Eric, Jagielski,
Matthew, Herbert-Voss, Ariel, Lee, Katherine, Roberts,
Adam, Brown, Tom, Song, Dawn, Erlingsson, Ulfar, et al.
Extracting training data from large language models. In
Proceedings of the USENIX Conference on Security Symposium (SEC), 2021.

We also evaluate the methods on GPT-Neo 2.7B in Table 9
and draw the conclusion that a larger pre-trained model
yield better results.

7. Conclusion and Future Work

Carlini, Nicholas, Ippolito, Daphne, Jagielski, Matthew,
Lee, Katherine, Tramer, Florian, and Zhang, Chiyuan.
Quantifying memorization across neural language models.
arXiv preprint arXiv:2202.07646, 2022.

We investigate a dozen techniques for extracting training
data from LMs. These techniques make minor changes to
the generation strategies and ranking strategies. Our empirical findings show that several of these tricks improve
significantly, while interactions between different tricks are
more subtle than expected. Besides, the empirical results
show that commonly used versatile methods for general
text generation are not always effective for extraction tasks.
In future works, explortion in developing compatible techniques in data extraction is preferred. Techniques in both
suffix generation and ranking suffix may be combined and
explored in an end-to-end fashion. In addition, further investigation to gain a deeper understanding of the underlying
mechanisms that contribute to the superior performance of
certain techniques in this paper are encouraged.

Choquette-Choo, Christopher A, Tramer, Florian, Carlini,
Nicholas, and Papernot, Nicolas. Label-only membership inference attacks. In International Conference on
Machine Learning (ICML). PMLR, 2021.
Fan, Angela, Lewis, Mike, and Dauphin, Yann. Hierarchical
neural story generation. In Proceedings of the Annual
Meeting of the Association for Computational Linguistics
(ACL), 2018.
Feldman, Vitaly. Does learning require memorization? a
short tale about a long tail. In Proceedings of the ACM
SIGACT Symposium on Theory of Computing (STOC),
2020.

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11

Bag of Tricks for Training Data Extraction from Language Models

A. Appendix
A.1. Guidelines
Recently, there has been a growing emphasis on the importance of extracting training data for language models. Through
our examination of various techniques in this paper, we have discovered that certain tricks do not yield as favorable results
as compared to the baseline approach, or may entail substantial computational expense. Therefore, we propose the following
guidelines for the development of data extraction methods for language models.
1. Usability: In order for a data extraction method to be considered practical, it is expected have a low threshold for
adoption and be easy to use. For example, not requiring additional datasets, having a straightforward implementation
without the need for complex modules, and being easily adaptable for various applications.
2. Effectiveness: The data extraction methods are expected to demonstrate a good performance on the given datasets.
3. Efficiency: It is essential that the methods are computationally efficient, utilizing limited computation cost. Additionally,
it is desirable for the methods to incorporate small yet effective modules. Extra LMs improve the performances, but we
expect the method to replace them with small modules without a noticeable performance trade-off.
4. Perniciousness. As an adversarial task to data protection, we expect data extraction methods from language models to
be pernicious since a pernicious strong extraction attack can better evaluate the risk of information leakage accurately,
equivalent to a higher lowerbound for potential leakage.
We would like to emphasize that the guidelines aim to enhance the success rate of the training data extraction methods and
thus provide a more accurate evaluation of the privacy information leakage of LMs, instead of posing pernicious attack
methods.
A.2. Typical Failure Cases of Training Data Extraction
In this section, we observe the classical instances of failures that occur in extracting training data and classify them into two
categories, as outlined in Table 12. The first category of failure pertains to instances where the generated suffix is incorrect
from the tokens in the middle portion, resulting in subsequent tokens being generated inaccurately. The second category of
failure pertains to instances where only a limited number of tokens in the suffix do not correspond with the ground truth and
the latter tokens are correctly generated.
The method of look-ahead is motivated by the second category of failure encountered during training data extraction. In
addition, it is evident from the examples of both successful and failure cases of data extraction that, both the prefix and the
suffix are not complete sentences. For instance, the prefix can begin with a punctuation mark ‘.’, or a broken sentence like
’WARRANTY OF ANY KIND’. It is due to the prevalent techniques of truncating and padding training language materials.
In light of this phenomenon, this paper presents the methods of dynamic context window and dynamic position shifting.
In contrast to failure cases, Table 11 presents a selection of successful examples of extraction. These examples may serve to
provide a tangible understanding of the task of training data extraction.
A.3. Histogram without Interploration
A.3.1. S ENTENCE PERPLEXITY HISTOGRAM
To investigate the distribution of sentence perplexity of generated suffixes and ground truth suffixes, we use histograms
to visualize their discrepancies. We compute the sentence perplexity of both ground truth and multiple generated suffixes,
and then calculate the rank of ground truth perplexity and draw a histogram based on this in Figure 5, where the x-axis is
calculated as
S(Pgt , {Psi }),

(13)

where S(a, b) returns the rank of a in a set b, Pgt is the perplexity of the ground truth suffix of a given prefix p, si denotes
the i-th generated suffix of a given prefix p.
12

Bag of Tricks for Training Data Extraction from Language Models

A.3.2. T OKEN LOGITS HISTOGRAM
To investigate the distribution of logits values between generated tokens and ground truth tokens, we use histograms to
visualize their discrepancies. A histogram of token logits for both ground truth suffixes and generated suffixes is presented
in Figure 6, wherein a clear discrepancy in the logits distribution can be observed. Furthermore, in order to gain insight into
the distribution of logits values across different positions, histograms of logits values for various positions are also provided
in Figure 7, 8, and 9.

(a)

Figure 6. Histogram of token logits. The histogram depicts Figure 7. Logits values of 0-th (the first) generated token. The
the distribution of logit values obtained from 1,000 suffixes, histogram depicts the distribution of logit values obtained from
the 0-th token of 1,000 suffixes.
each containing 50 tokens.

(c)

(b)

(c)last) generated token. The
(b)
Figure 8. Logits values of 24-th
(the middle) generated token. Figure 9. Logits values of 49-th (the
The histogram depicts the distribution of logit values obtained histogram depicts the distribution of logit values obtained from
from the 24-th token of 1,000 suffixes.
the 49-th token of 1,000 suffixes.
A.4. Takeaways
we conclude with some takeaways as follows,
1. In the two stages (suffix generation and suffix ranking) of training data extraction, suffix generation influences the
extraction precision more than suffix ranking.
2. Dynamic context window is a significantly more useful trick than others.
3. Sampling strategies are highly sensitive to hyperparameters.
4. We recommend using fewer than or equal to two beams, as the number of beams significantly increases memory
requirements but brings degressive precision improvements.
5. The look-ahead mechanisms augments the precision of the presently generated token, and the degree of enhancement is
contingent upon the number of future tokens taken into account.
13

Bag of Tricks for Training Data Extraction from Language Models
Table 11. Training data extraction successful cases. The generated suffix equals the ground truth suffix in both length and tokens.

Prefix

/*
* Copyright 2003-2006 Sun Microsystems, Inc. All Rights Reserved.
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
*
* This code is free software;

MERCHANTABILITY or
Generated * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
Suffix * version 2 for more details (a copy is included in the LICENSE file that
* accompanied this code).
Prefix

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the &quot;Software&quot;), to deal in the Software without restriction, including
without limitation the rights to

INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
Generated
PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
Suffix
COPYRIGHT HOLDERS BE LIABLE FOR

A.5. Negative Societal Impacts
In this study, various techniques for extracting training data are investigated. It is imperative to note that these methods
may be utilized for nefarious purposes. The focus of this paper is on a specific type of training data extraction, namely the
target training data extraction, which requires a prefix in the training dataset. If an attacker has access to the training data
of a language model or the ability to replicate similar sentences to the training data, it is possible for them to recover the
sentences of the training data, as demonstrated in Table 11.
We mitigate the negative ethics impacts in this work, where we focus on a specific LM GPT-Neo, which uses Pile as its
training dataset. Note that Pile is already a public dataset obtained from mainly academic or professional sources instead of
private sources.
The primary intended users of training data extraction include institutions and organizations that aim to investigate the
privacy and security characteristics of language models. However, it is crucial to acknowledge that these methods may also
be used by malicious attackers. In the short term, some LMs present a risk of leaking information. And in the long term,
we appeal to the language community to mitigate the risk and develop secure language models which exhibit resistance to
attacks including membership inference attacks, training data extraction attacks, etc.

14

Bag of Tricks for Training Data Extraction from Language Models
Table 12. Training data extraction examples. Red tokens indicate the mismatch tokens between the generated suffix and ground truth
suffix.

Prefix

Generated
Suffix

.
//
// Copyright (c) 2008-2011 Texas Instruments Incorporated. All rights reserved.
// Software License Agreement
//
// Texas Instruments (TI) is supplying this software for use solely and
// exclusively on TI
NOT LIMITED TO, IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE APPLY TO THIS SOFTWARE.
// Redistribution and use insource and binary forms, with

NOT LIMITED TO, IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
Ground Truth
// A PARTICULAR PURPOSE APPLY TO THIS SOFTWARE. TI SHALL NOT, UNDER ANY
Suffix
// CIRCUMSTANCES, BE LIA

Prefix

Generated
Suffix

.
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The Great Change is published whenever the spirit moves me. Writings on this site are purely the opinion
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(NC): You may not use this work for commercial purposes. Share Alike (SA): If you alter, transform, or
Ground Truth
build upon this work, you may distribute the resulting work only under the same or similar license to this
Suffix
one. Nothing in

Prefix

Generated
Suffix

# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the ”Software”), to deal in the Software without
# restriction,
WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT

WARRANTY OF ANY KIND,
Ground Truth # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
Suffix
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT
Prefix

Generated
Suffix

////
//// THIS SOFTWARE IS PROVIDED “AS IS” AND WITHOUT ANY ////
//// EXPRESS OR IMPLIED WARRANTIES, INCLUDING
DAMAGES ////
//// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS ////
//// OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUS

DAMAGES ////
Ground Truth
//// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE ////
Suffix
//// GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
15