Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models
Authors: Tianjian Li
Haoran Xu
Philipp Koehn
Daniel Khashabi
Kenton Murray
Venue: ICLR 2024 spotlight (2024)
Keywords: language generation
language modeling
machine translation
robustness
estimating data quality
Primary area: representation learning for computer vision, audio, language, and other modalities
TL;DR: We propose to truncate tokens with high L2 error norm to improve robustness of text generation models to noise.

Abstract: Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becoming more commonplace, how can we enhance the robustness of models trained on a massive amount of noisy web-crawled text? In our work, we propose Error Norm Truncation (ENT), a robust enhancement method to the standard training objective that truncates noisy data. Compared to methods that only uses the negative log-likelihood loss to estimate data quality, our method provides a more accurate estimation by considering the distribution of non-target tokens, which is often overlooked by previous work. Through comprehensive experiments across language modeling, machine translation, and text summarization, we show that equipping text generation models with ENT improves generation quality over standard training and previous soft and hard truncation methods. Furthermore, we show that our method improves the robustness of models against two of the most detrimental types of noise in machine translation, resulting in an increase of more than 2 BLEU points over the MLE baseline when up to 50\% of noise is added to the data.

Published as a conference paper at ICLR 2024
ERROR NORM TRUNCATION: ROBUST TRAINING IN THE PRESENCE OF DATA NOISE FOR TEXT GENERATION MODELS
Tianjian Li, Haoran Xu, Philipp Koehn, Daniel Khashabi Kenton Murray Center for Language and Speech Processing Johns Hopkins University. Baltimore MD {t11104, hxu64
ABSTRACT
Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becom- ing more commonplace, how can enhance the robustness of models trained on a massive amount of noisy web-crawled text? In our work, we propose Error Norm Truncation (ENT), robust enhancement to the standard training objective that truncates noisy data. Compared to methods that only use the negative log- likelihood loss over target words to estimate data quality, method provides a more accurate estimation by considering the distribution of non-target tokens, which often overlooked by previous work. Through comprehensive experiments across language modeling, machine translation, and text summarization, we show that equipping text generation models with ENT improves generation quality over standard training and previous soft and hard truncation methods. Furthermore, we show that our method improves the robustness of models against two of the most detrimental types of noise in machine translation, resulting in an increase of more than 2 BLEU points over the MLE baseline when up 50% of noise is added the data.
INTRODUCTION
Advances in neural text generation models have achieved remarkable success in various downstream tasks, which include but not limited to machine translation (Kalchbrenner Blunsom 2013). sum- marization (Rush al. 2015), question answering (Joshi al.) 2017) and story generation (Fan) jet al., 2018). The prevalent paradigm of training text generation models is estimation (MLE), which finds parameters that maximize the probability of each token from the training data conditioned on a given context.
The limitation of MLE is that the model is forced assign non-zero probability to all tokens that appear in the training data, regardless of their quality, making the model not robust to errors in the training data. Existing research has demonstrated that text generation models are vulnerable to
natural noise, such as misspelled and misordered words (Khayrallah & Koehn 2018) and adversarial
noise, such as poisoned training data (Wang et al. 2021a] Wallace al. [02]] Wan et 2023)
To overcome this limitation, previous studies have either explored options to find alternatives to the autoregressive MLE paradigm (Khandelwal al. 2021 Lewis al.] (2020b; An al.) (022)
or modify the MIE objective (Welleck et al. 2020 [i et al. 2020 Kang & Hashimoto [2200] Lin
jet al. 2021 Pang & He. 2021 | Xu al. 2022 al. 2023) Modifications of MLE estimate data
quality using the predicted probabilities of the ground truth token during training: high probability corresponds to a higher likelihood that the ground truth token is clean and vice versa. Therefore,
either directly remove data with high loss (Kang & Hashimoto, 2020 Goyal et al. 2022 Mohiuddin et al., 2022), or weigh data with low probability (Li et al.) 2021 Ji et al. 2023 at
each training iteration to improve robustness to data noise.
However, estimating data quality only using the predicted probability the target token ignores the distribution of the non-target tokens. For example, when model assigns a low probability

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Notation and Task Description. We consider an conditional text generation model Po (y)z)) Given context æ and target sequence (31,..., )r), the autoregressive framework models the prob- ability of the target sequence conditioned on the context pe Po x)) by factorizing to the sum of Published as a conference paper at ICLR 2024
Input to the Language Model
Predicted Probabilities
house
Context: live in
LM: Too many possible options...
hotel
Ground truth: house
buildling
mansion
answer
Context: am confused by the
LM: Never seen this before...
Ground truth: answer
zebra
Context: Capital of Maryland
LM: V
Baltimore
Ground truth: Baltimore
This should wrong!
Annapolis
Figure 1: An motivating example of using the enror norm for data quality estimation. All three examples have equal loss because they assign the probability the ground truth token. The skewness of the distribution of non-target tokens differentiates between the case when the context has high entropy with multiple possible continuations (cxample 1), when the model at the beginning of training and is incompetent in making a prediction (example 2) and the case when the data is an error (example 3). Truncating high loss removes all three examples whereas truncating high &a error norm only removes the third erroncous example.
specific token, it could be the case that the context is high-entropy with many viable continuations, leading to a diluted probability of the target token (first example in Figure Another possibility is that the model has not sufficiently converged and thus has not learned reasonable distribution for this token (second example in Figure D. In both cases, truncating this token or down-weighing the loss of this token could be harmful to model training.
To consider the predicted distribution non-target tokens when estimating data quality, we propose Error Norm Truncation (ENT). This modified objective uses the &2 norm of the difference between the model's predicted distribution and the one-hot vector of the ground truth to measure the quality of the data at each training iteration and truncate data with low quality. Intuitively, truncates tokens to which the model not only assigns a low probability but is very confident that it should be another token (third example in Figure T.. ENT improves robustness to data noise during training by accurately estimating data quality at the token level and removing noisy tokens.
method
To sum up. our contribution is threefold:
We propose Error Norm Truncation: data truncation method during training guided by more accurate data quality estimation method that considers the probability distribution of non-target tokens;
Through experiments under different tasks and setups, show Error Norm Truncation consistently outperforms the MLE baseline as well as strong baselines proposed by previ- methods in generation quality;
We directly validate that Error Norm Truncation improves the robustness of machine trans- lation models against two different types of noise: untranslated and randomly shuffled target sentences and outperforms all previous methods that truncate data.
BACKGROUND AND MOTIVATION
log-probabilities of individual tokens. The prediction for each time step is conditioned both on the context æ and the previous tokens y
log Pa (p((y)2) =
log pa (ye (y
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(XLSUM) Text: "Managers Alliance Boots said fall demand for products made for other companies meent
1. (Opus) Source: <2d>> Powerpoint, Adobe Acrobat, Microsoft Word, Microsoft Excel,
reduce capacity. Bosses the next two years and added they would make afforts to redeploy stoff. The division
said the posts will
Ta Target: Powerpoint, Pagemaker, Adobe Acrobat, Microsoft Word, Microsoft Excel,
involved, BCM, currently employs 1,200 people and will now focus own brand beauty and skincare products. Stephen Le Hane, an appreciate that many from the recession as most companies are in the UK. amount demand they for products in BCM has gone down and as there are quite high fixed costs in manufacturing, those adjustments in volume requirements the profilability and success of the BCM business."
compeny. said:
(INSLT Source: <2de> you get 5'8" and one tenth, and boom! 5'9".
customers are suffering
Target:sie sind gute 172 cm groß bumm!
175 cm.
have an impact
3. (MMT 17) Source: <en>
Bipak cayдa
corbicbi dip xak KaHa xeeiin алатьlH OMbHH eMec.
Summary: About 200 posts site in Nottinghami
the Boots
Target:
( P
#
Figure Examples of natural data noise that harms training. Left: summarization example from the XLSUM (Hasan al. (02T) dataset where details the summary (highlighted in red) camnot be inferred from the input text, which might cause the model to hallucinate facts in generating a summary. Right: Translation examples
from opus- 1oss-100 (Zhang et al. 2020). IWSLT 14 (Federico et al. (014) and WMT 17 (Bojar et al. [017). where details in the translation (highlighted in red) cannot be traced back to the source text (example I and 3), requires the model to perform metric conversion (example 3).
The context depends on the specific task: In machine translation, the context is the source sentence to be translated from. In summarization, the context æ is the article to be summarized. Standard language modeling
be seen as a special case where the context is empty.
MLE maximizes the probability of the target sequences from a training corpus D by minimizing the expectation of the negative log- leggliielihood over the training corpus:
T
Co y) = EynD
Σ
log pa byck,
However, the MLE objective is not robust to noise (Ji et al. 2023), which can observed by calculating the gradient of the MLE loss function with respect single token yr:
V Lo(x, Mr) Vpo(velyer..r)
When the data is incorrect and the predicted probability for the token yt (the denominator) is very small, the gradient norm La, (x)) would be very large, resulting in a large gradient update to an undesired direction.
The vulnerability of the MLE objective to noise cultivates research into truncating noisy data. trivial method of estimating data quality q(x; y) is use the predicted probability Po(y æ). Intuitively, if the model assigns low prediction probability to training instance, more likely that the training instance of low quality. However, practice, low prediction probability can also indicate high entropy context rather than data quality.
Previous Works.
natural way to mitigate this vulnerability to hard remove the noisy data: Loss Truncation (Kagg & Hashimoto 2020) directly removes fixed fraction of the training sentences with the highest loss by setting their loss to given fraction data c prune out. The loss function for Loss Truncation is:
Gr== = - log ( x)) (y)()) > Te.c);
where 1(:) is the indicator function and Tp.c the threshold calculated by the e-th percentile of losses over the training data. Note that the threshold depends on the model's current state since use the model to rank training data and prune out a given percentage with the highest loss (or lowest predicted probabilities.
Data truncation can also done a soft and fine- mne-grained way: TaiLr (Ji et al. 2023) up up-weighs individual tokens with higher predicted probabilities, smoothed by interpolation between the
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ground truth distribution and the predicted probability of the model. The loss function Craite is:
Po Po Mee 2)
log (o yerr
(1 - 7) (e
yec,
Standard Loss
Weighting Factor
where y is hyyper-parameee for the smoothing factor. To overcome the issue of the model assigning a very small probability to all target tokens uniformly during the initial stage of training, TaiLr sets lower threshold the weighting factor a hyperparameter. In our work, we consider Loss Truncation and Tail. the most important baselines to compare.
Motivation. We point out two limitations of estimating data quality only by training loss:
It is sensitive to the training iteration at which start to estimate data quality and remove or down-weigh low-quality data.
It ignores the rich information contained in the probability distribution of the incorrect (non-target) tokens, treating high and low entropy contexts equal.
The first limitation arises from the model, when trained from scratch, undergoes multi-rounds of memorizing and forgetting (Toneva et al. Jiang et. (022] examples. When certain example is memorized, the model would label it as high quality and vice versa. This leads high variance in measuring data quality throughout different stages of training- To overcome this issue, Loss Truncation first trains the model for pre-defined number of iterations and then uses it to do quality estimation. TaiLr uses a pre-defined lower bound on the weighting factor. However, these methods require extensive hyper-parae parameter tuning due to the high variance, especially when estimating quality within a mini- at an arbitrary training iteration.
al., a (202)) individual
The second limitation arises from negative log- likelihood loss ignores the skewness of the probability distribution over non-target tokens. For example, when the model assigns a low probability the ground truth token 'house", it might have distributed the majority amount of probability mass synonyms 'building', 'ho- tel' and 'mansion'. There exist multiple cor- rect predictions for a given context (Out et al.]
Value 10% of data minibatch
Emoo Narm
2018 Khayrallah et al., 2020), and only using the probability of one token to indicate quality leads to misjudgment.
ming
3 ERROR NORM TRUNCATION
Figure 3: The training dynamics of pre-training GPT2- large WikiText- 103. The plot shows the error norm for the largest 10% of data in each mini-batch. Initially, all error norms are close
Motivated by methods in dataset pruning (Paul al., (02T), we propose to estimate data qual- ity using the l2 norm of the difference vec- tor between the model's predicted distribution po(-1yee, æ) and the groundtruth one-hot dis- tribution (H(y:):
indicating the model uni- formly assigns tiny probabilitics to all target tokens. After the model is warmed up, it begins to detect data noise by assigning large error norms.
q(v1, æ) [|po(-lU
OH )l.
which we refer as the error norm. OH(}:) ) is a vector with all zeros except the entry at is one. At each training iteration, we set threshold an error norm above the threshold. The loss function for Error Norm Truncation (ENT) is
hyper-parameter and hard prune the tokens with
CENT Ey-Dl- log pe (g/z)) 1(g(v,, Tp.c)].
The f2 error norm presents a solution jointly to the two aforementioned limitations due to an obser- vation: the probability distribution of the incorrect tokens only becomes skewed after multiple
We provide PyTorch style pseudocode of Error Norm Truncation in Appendix D
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iterations of training. Initially, when the model does not have enough knowledge make a predic- tion, the error norm for all data is close to 1, indicating that our model uniformly assigns probabilities to all target tokens. After multiple iterations of training, when the model has enough knowledge, the error norm of data noise becomes significantly larger. Figure 3 illustrates the state transition of the
the model from warming up to being able make estimate of data quality, corresponding horizontal red line at around training iteration 500. Setting a threshold on error norm allows the model learn from all the data during the initial stage to make an educated estimate of data quality.
Theoretical Connections. As Kang & Hashimoto (2020) points out, a measurement of differ- ence between probability distributions that is more robust to noise than the standard -iveergeeece (KLD) Kullback & Leibler (1951) is the Total Variation Distance (TVD) (van Handel, (2166), de- fined by the supremum of difference assigned to the same event. Intuitively, TVD measures the distinguishabilityy between two distributions. Given two probability distributions p and over all possible sequence y, the TVD between them is:
TVD(p, 4) = sup (p(y) - (())]- yEY'
et al. (2023) factorizes the sequence level TVD to the token level and proves that the token level TVD is upper bound of the sequence level TVD, therefore minimizing the token-level TVD is able to make the model more robust to noise in the data. We show connections between error & norm, the token-level TVD and the KL--Dverrence P By Pinsker's Inequality, we have
1 lipe OH()))) 2
2 lIpe OH(W)ll2
sup ((()) OH():) V |LD(pp/(OH(()))
yEV Estimator of Token TVD
Error & Norm
We see that the error l2 norm is a lower bound of the estimator of token level TVD. Examples with high error norm indicate higher total variation distance, whereas examples with high loss (KLD) do not necessarily indicate high TVD since it is loose (Canonne) 2023) upper bound. Therefore, truncating examples with high error norms removes noisy data that has a higher TVD with the model's prediction learned from other instances.
4 CASE STUDIES
Error Norm clearly distinguishes between clean and noisy tokens. It is well established in robust statistics that &a error norm more sensitive to outliers (Hastie et al. (000)) than ε norm, so E norm is better in detecting outliers in data than ε norm. We prove the equivalency of using the error & norm and standard loss in ranking data quality at Appendix [A] To empirically show the superiority of using the & norm in distinguishing between clean and noisy tokens, we the dataset from Kang & Hashimoto 2020)) which contains 300 examples from the Gigaword text summarization dataset where each summary is annotated into two categories: 1) directly entailed and 2) contains facts that cannot be inferred from the context. We find the precise tokens that are not entailed by the input and label them as hallucinate and label all the other tokens as clean
We plot the normalized histograms of negative log-likelihood loss and error norm between clean and hallucinate tokens at figure 4a and 4b] evaluated by pre-trained BART-large model. The overlap between clean and noisy distributions of loss (shaded area figure 4a) is larger than the overlap of error norm (shaded area in figure 4b), indicating that error norm distinguishes between clean and noisy examples more clearly than negative likeliieldoood loss.
Error Norm provides more accurate measure of data quality. We directly verify that our method does provide a more accurate estimate of data quality. We plot out the BLEU scores of mul- tilingual machine translation of directions: En=(De. Fr, It, Es} with fixed fraction of sentences pruned out according to different metrics at Figure ENT was able to match the performance of the baseline at small pruning fractions (10%-20%) while having in the least drop of performance
yee, Foor simplicity, we rewrite the prohability distribution of predicted probabilities po( Po aasas po-
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clean-loss
clean erronnom
hallucinate-loss
hallucinate-errooeaan
0.20
0.15
denaity
deraltty
0.30
0.05
0.4
0.6
1.2
1.4
negatiine log-licelhood
(a) Normalized histograms of log-likelihood loss.
(b) Normalized histograms of error norm.
Figure Distributions of negative log-likee regliiklihood loss and emrrr & norm of clean and noisy data, evaluated by a pre-trained BART-large model. Error norm clearly distinguishes between clean and noisy data.
at high pruning fractions, outperforming randomly pruning for 2.43 BLEU and outperforming Loss Truncation by 0.88 BLEU when 60% of the data is pruned out. This shows that Error Norm provides more accurate estimate of data quality than negative log- oig-likelhhood loss.
EXPERIMENTS
In this section, we show that truncating tokens with high error norm improves generation quality across different tasks. We describe the setup for all of our experiments at $5.1. We validate that our methods improves robustness under synthetic noise at $5.2. We present our experiment results under the train-from-scratch setting at $5.3 and under the fine-tune setting at $5.4. We include results of both truncating fixed fraction of data (ENT-Fraction) and truncating according pre-defined threshold (ENT-Threshold), Detailed dataset statistics and hyper-parameters are at Appendix
5.1 SETUP
Robustness Experiments. To directly verify the ENT improves robustness, inject noise into 1M parallel sentences En-Fr data from the opus-100 dataset. We select two of the most harmful type of noise (Khayrallah & Koehn 2018: Untranslated Text where the source sentence is directly copied to the target side; Misordered Words where the words at the tar- side is randomly shuffled. We vary the amount of noise added the corpus (10% 20%, 30%, 40% 50%} of the size of the orig- inal clean corpus and report the BLEU scores of models trained MLE equipped with Loss Truncation, TaiLr and ENT-Fraction on the per- turbed datasets.
scores for different fractions
85
33.6
23.5
33.0
23.05
2217-..22277
65.
81.5
Random
BIT $a-gest)
21.05
31.0
BIT Smalless
Loss Tuuceation
20.02
Frune Praction
Figure 5: Average BLEU results of 4 translation di- rections En-(De, Fr. Es} from the opus-100 dataset with a fraction of sentences being truncated according to loss, crror norm, and randomly truncated. Truncat- ing high error norm sentences achieves the best per- formance at all truncation fractions.
Srain-from-Scratch. We evaluate our method machine translation and general language modeling. For multilingual translation, we train single model for eight directions en-
(es, fa. thh from the opus-100 cor-
pus Zhang al. 2020) using 1M parallel sentences for each direction.
We train on the fairseq (Ott et al. 2019) implementation of the standard Transformer (Vaswani et al., 2017 architecture for all of our machine translation experiments. For language modeling, we train
https///pus.nlll..u/oppp 100.php t rons iwslt t. de en

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GPT2-large (Radford et al. 2019) model the WikiText- 103 dataset (Merity al. 2017) for 5 epochs from scratch. We use the Huggingface (Wolf et al. 2020) implementation of GPT2- Garge.
Fine-Tuning, We validate our method on the text summarization CNN/Daily Mail (See al. 2017 Hermann et al. 2013) datasct on two different models: T5-small (Raffel et al. 2020) and BART-base (Lewis et al. 2020a) to validate our method generalizes across different pre-trained models. We use the Huggingface implementations of T5 and BART.
5.2 ROBUSTNESS RESULTS
Untranslated Text. Table I shows the BLEU results of machine translation models trained on corpus with different level of untranslated text injected. Since the corpus is high-quality data from the opus-100 training set, the difference between various methods that aim to improve robustness to noise is small when no noise is added.
The MLE baseline model's scores gradually decrease with increased injection, revealing the negative impact of untranslated sentences. Loss Truncation maintains similar BLEU scores. TaiLr exhibits modest gains in both metrics. Notably, Error Norm Truncation consistently improves performance with higher injection percentages. Outperforming the baseline 3.8 BLEU and outperforming the best of Loss Truncation and TaiLr 2.1 BLEU when 50% of noise is injected. These results emphasize the challenge of handling untranslated content, with the Error Norm Truncation proving exceptionally effective in mitigating this issue and enhancing translation quality.
Misordered Words. Table 2 shows the BLEU results of models when trained on data with misordered sen- tences injected at the target side. Our results echos with the results in Khayrallah & Koehn (2018), showing that randomly shuffling the target sen- tence is weaker type of noise com- pared to directly copying the source text to the target. Although Loss Truncation was able to improve upon the baseline when a small amount of noise is added (10-20%), it performs the same as standard MLE training at when larger amount of misor- dered sentences are added to the train- ing data. ENT is the most resilient method against misordered words at the target side, resulting in the largest BLEU scores improvement over the baseline in all noise levels. It outper- forms the baseline 0.9 BLEU when 50% of randomly shuffled sentences are injected and only underperforms 0.1 BLEU against the performance of standard training on clean data, in- dicating the resilience of the model against randomly shuffled target sen- tences when equipped with ENT.
Untranslated
0%
10%
20%
30%
40%
50%
MLE
36.5
34,9
33.2
30.6
31.0
28.6
Loss Trunc.
36.5
33.2
32.5
31.5
31.4
29.4
TaiLr
36.6
34.3
33.4
31.5
31.6
30.3
ENT-Fraction 36.7
33.3
33.8
33.3
33.1
32.4
Table 1: BLEU scores of models trained on opus- 100s-110 En-Fr data injected with the source sentence directly copied to the target side (Untranslated Text) ranging from 10% to 50% of the original clean data. Truncating with error norm the most robust method against untranslated sentence.
Misordered
0%
10%
20%
30%
40%
50%
MLE
36.5
36.1
36.1
36.2
35.8
35.5
Loss Trunc.
36.5
36.1
36.1
36.2
35.8
35.7
TaiL.
36.6
36.2
36.2
36.3
36.2
36.2
ENT-Fraction 36.7
36.3
36.7
36.7
36.5
36.4
Table 2: BLEU scores of models trained on opus-100 En-Fr data injected with parallel sentences randomly shuffled (Misordered Words) the target side ranging from 10% 50% of the orig- inal clean data. Truncating with error norm was able to improve upon the baseline the most compared to existing methods.
5.3 TRAIN-HROM-SCRATCH RESULTS
Language Modeling, We first evaluate our method on general language modeling. Table shows the results of the validation perplexity of pre-training a GPT-2 Large model on WikiText- 103 from scratch. Hard truncation methods (Loss Truncation and Error Norm Truncation) were able to lower the perplexity by more than 1 point compared to the MIE baseline. Truncating with error norm

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outperforms truncating with loss for fixed fraction. Truncating to a given threshold outperforms all existing methods by lowering 1.58 perplexity compared to the MLE baseline.
MLE
Loss Truncation
TaiLr
ENT-Fraction
ENT-Threshold
PPL. & 25.88
24.64
25.62
24.50
24.30
Table 3: Validation perplexity on WikiText- 103 of pre-training a GPT2-large model with different data trunca- tion methods. Truncating with enror outperforms the MLE baseline 1.38 perplexity while truncating a given threshold further improves the performance by 0.2 points perplexity.
To show that Error Norm Truncation less sen- sitive to the iteration from which soft or hard data truncation methods are applied, we vary this iteration {0, 100, 200, 500, 1000} pa- rameter updates and plot out the validation per- plexity Wiki'Text- 103 of different methods at Figure [ We see that ENT-Fraction is able to outperform previous methods while having the lowest variance and
25.5
25.5
25.4
Perplestty
baseline
25.2
Loss Truncation
ENT fraction
Tailr
further im- proves the performance over ENT-Fraction. We highlight that large-scale language model pre- training is too expensive to tryout a combinator- ically large number of hyper- parameters, there- fore our method is more scalable to large-scale pre-training tasks compared to other methods due to the low variance and high performance.
24.4
ENT threshold
ENT fraction
Loss Truncation
Figure 6: Validation perplexity, on Wiki 103 by varying the iteration to start using different methods. ENT exhibits the least variance and best perfor- mance.
Machine Translation. Table 4 shows the BLEU results on Multilingual Machine Translation, where IM parallel sentences for each language pair from set of linguistically diverse languages concatenated for training large model. We find that previous methods often underperform the MLE baseline due to not capturing the model's competency during truncating, while our method consistently outperforms the baseline. Our method also outperforms Loss Truncation in 6 out of 8 directions, given a fixed pruning threshold.
En-()
Es
Fa
It
Ko
Ru
Tr
Zh
Avg.
MLE
40.5
14.2
40.4
35.1
10.1
36.3
39.2
30.1
Loss Truncation
39.8
14.0
40.1
34.4
9.9
36.5
24.7
40.1
29.9
TaiLr
40.4
14.0
40.2
35.1
10.0
36.1
25.2
39.6
30.1
ENT-Fraction
41.1
14.8
40.3
35.2
10.3
36.4
25.0
39.6
30.3
ENT-Threshold
41.9
14.9
41
34.8
10.2
36.5
25.5
39.8
30.6
Table 4: BLEU results on linguistically diverse subset of the opus-100 100 dataset. Error Norm Truncation with threshold and fraction outperforms the baseline and Loss Truncation in out of 8 directions.
5.4 FINE-TUNING RESULTS
Summarization. Table shows the results of fine-tuning T5-small and BART-base on the CNN/- Daily Mail Summarization dataset. Since we can rely on the pre-trained model to make an estimate of the data quality, we do not need to pre-define a threshold for the model. Directly pruning out fraction of data produces the best result in this case. Again, we were able to observe that truncating with emror norm consistently outperforms all other methods in two different models.
6 RELATED WORKS
Modifications to MLE for Text Generation. As the MLE objective is not robust to noise, numerous work have proposed ways to modify the MLE objective. Welleck al. 2020) proposes
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T5-small
BART-base
R-1
R-2
R-L
R-1
R-2
R-L
MLE
42.19
19.69
39.04
43.50
20.59
40.36
Loss Truncation
42.22
19.68
39.05
43.22
20.66
40.44
TaiLr 41.53
19.22
38.33
42.20
19.66
39.07
ENT-Fraction 42.63
19.98
39.57
43,48
20.29
40.72
ENT-Threshold 42.37
19.80
39.27
43.35
20.30
40.54
Best validation rouge- 1/2/LSum results on fine-tuning T5-small and BART-base equipped with dif- ferent robust modifications to MLE on the CNN/Daily Mail dataset. ENT is able to outperform baselines on TS-small and match the performance of baselines on BART-base.
Table 5:
1ouge-1/2/LSum
to augment the MLE objective by penalizing the model for generating undesired outputs.
Xu
al. (2022) directly penalizes the model for generating repetitions. [Lin al. (2021) modifies
the gradient to encourage the model to generate diverse text. KKng & Hashimoto (2020) truncate
a given fraction of data with the highest loss to remove noise from the data. Pang He (2021)
reformulates text generation as an off-policy and offline reinforcement learning problem, assigning
weights to each token according to a pre-defined reward function. Similarly, Ji et al. (2023) also
reweighs each token from the training dataset by the prediction probability of the model, smoothed by interpolation between the one-hot probability vector and the predicted probability vector. Li et al. (2020) points out that the standard MLE objective treats all incorrect tokens as equal and proposes to learn a prior distribution over the tokens using the training data and smooth the one-hot ground truth distribution
Gaussian distribution itt al. (2023) proposes first to generate an intermediate output using MLE and iteratively refines the generation. To the best of our knowledge, our work the first to address the limitations of only relying on the output probabilities in estimating data utility.
tokens with similar embeddings. |Welleck]
Measuring Data Utility in NLP. Numerous works have proposed methods to estimate the contribu-
tion of each single data point in Natural Language Processing. For text generation tasks, the quality of data can be as simple as handcrafted heuristics such as word frequency and sequence length (Pla-)
tanios et al. 2019), the relative position of the word in sentence (Liang et al. | [022 Jia et a 2023))
the similarity to target domain (Moore & Lewis, 2010 Zhang al. 2019) Besides handcrafted heuristics, model generations (Wettig et al. 2024 Liu et al. (222)) and signals (loss, gradient, and representations) can also be utilized to measure data quality. Koh & Liang 2017) imports Influence Functions (Cook& & Weisberg 1975) from statistical theory to deep learning, measuring the utility of each training example by the difference between the parameters of the model trained with and without the particular training example. However, this estimation requires the computation of single sample gradients, which is impractical when the training dataset is large. Paul et al. (2021) shows that the influence on training loss of removing one particular training example is upper bounded by the gradient norm when trained on that example and proposes to approximate the single sample gradient norm by the error l norm. All of the above methods assume that the data utility is static. Our work differs in that our method takes into account the training dynamics while making quality estimations. For a comprehensive survey on data selection for NLP, we refer the readers to Albalak jet al. 2024). Additional related works on measuring data utility with model signals and discussions on Influence Functions are provided in Appendix B
CONCLUSION AND LIMITATIONS
Conclusion. Our work proposes Error Norm Truncation (ENT), a robust modification to the standard MLE objective in training text generation models. ENT measures the quality of each token by considering the skewness of the predicted distribution and truncates the noisy tokens during training. ENT demonstrates enhanced stability and superior performance over existing methods.
Limitations. We acknowledge that the improvements of our method result from the noisy distribu- tion of the training data, therefore the improvements on clean, curated data might not be as large. We leave more coarse-grate grained grouped data and dataset quality estimation for future work.

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Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, and Ping Li. Dataset pruning: Reduc- ing training data by examining generalization influence. In The Eleventh International Confer- ence Learning Representations, 2023. URL https:: http:s/s///peereeew...eee. net forum?id-
A AXD2
Yilin Yang, Akiko Eriguchi, Alexandre Muzio, Prasad Tadepalli, Stefan Lee, and Hany Hassan. Improving multilingual translation by representation and gradient regularization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp.
2021. URL wtt.httpps///tm htttp:://arxiv org abs /taps/2/199 21../.119..047
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning. arXiv preprint arXiv:2001.06782 2020. URL https:
Biao Zhang. Philip Williams, Ivan Titov, and Rico Sennrich. Improving massively multilin- gual neural machine translation and zero-shot translation. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pp. 1628-1639, Online, July 2020. Association for Computational Linguistics. doi: 10.18653/v1/20200 aac.mm0...lmmmiii....mmm 148. URL https:://aclanntooooo.oooot wttp:yy..orr/22..a1a.11..
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Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh. Curriculum learning for domain adaptation neural machine translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1903- 1915, Minneapolis, Minnesota, June 2019. Association for Computational Linguisties. doi: 10. 10.18653/11/119-1118)
A EQUIVALENCE OF LOss AND ERROR & NORM
Theorem: Given datapoints (æi, vi) and (æ,, 35). if Co(a,, v.) = Co yes Wi), then [.po yes. m.) OH(): [-po Vej. my) OH(
Where OH(-) is the one-hot vector.
Proof:
Lo (2;; Deii Vi) = Lo y<i,,:)
po (yal yeci, 2) po(ylyy<33 x)
2 - 2 pe (Mil yeir x)
= 2 2 po yej x)
po(uilB<cs,
po (y yeci ar)
1 po ).
(o リイく x)
Σ Evtto Ip(v/ ax))
Eyeoy Ipe (v)l
llpo(: Beii x)) OH(y))l |Ipo | x) OH(Y,))l
B ADDITIONAL RELATED WORKS
Measuring Data Utility. Influence Functions (Cook & Weisberg 1975: Koh & Liang] 2017)
measures the utility of data utilizing first and second order model signals (gradients and Hessian). Specifically, the score g assigned to each training data pair (ær, y), evaluated by model parameterized by is given by:
((., v) = -ol(( T,, H Vol(x (
where is the domain which you want evaluate your data utility. For standard training where you care about the influence on generalizability. 2g is the test set. For domain adaptation, 2g is data from the target domain. Ho² is the inverse Hessian.
Most of the work utilizing model signals for estimating data utility can be viewed as simplifications to Influence Functions. A line of work (Wang al. 2020a Yu et al. 2020 Wang al. 2020b Yang
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et al. 2021 Wang al. 2021b: Fan et al., 2024) drops the Hessian dependency and measures the data utility by the gradient similarity to the development set g(æ, y) - Vol 0) Vol(x, y; 9). gradient similarity to the dev set Since the test set distribution should be unknown and relying
risk overfitting to the dev set, another line of work (Pruthi et al. 2020; Paul et al., (02T) only uses the gradient norm (0x, y) = ( y; 0)] in estimating the utility of data. Our work also falls into this category by approximating the gradient norm with the error vector norm and treating data utility as adaptive to the model competence rather than fixed value.
Besides simplifying the Influence Function utility estimation, Basu et al. (202T) finds that the accu- racy of Influence Function heavily depends on inductive biases and can break if the neural network is too deep. Koh et al. (2019) and Yang et al. (2023) extends beyond quantifying data utility of single examples by considering the interaction when multiple training instances are collectively pruned. Ladhak et al. (2023) trains the same model for one iteration on clean data and on noise, and use the difference loss for finding errors in the training dataset, which can be seen as a realization of the gradient similarity between training clean and noisy examples, Grosse et al. (2023) approximates CooneckerFFactrnde
the inverse Hessian in the influence function using the Eigenvalue Approximate Curvature (EK-FAC) and batch similar queries together to overcome the bottleneck of computing single sample gradients. Besides truncating data to improve robustness, data utility mea- suring with Influence Functions also be applied to understanding model generalization (Grosse et al. 2023). explaining black-box predictions (Han et al. (020),, finding spurious correlations (Han & Tsvetkov 2021). and studying the impact of label errors on model disparity metries (Adebayo
ict al. (002)..
Active Learning and Uncertainty Sampling. Active learning aims to select the most informative data for labeling within a given annotation budget. Uncertainty sampling, as an active learning algorithm, targets datapoints where the model exhibits the highest uncertainty. The two simplest techniques for uncertainty sampling, as outlined by [Weng (2022). are:
* Loss: Selecting datapoints with the lowest predicted probabilities (0(
* Entropy: Selecting datapoints with high entropy -
-Ey Po (y/ xx) log Po (v/x).
Utilizing loss for data selection connected to Loss Truncation (Kang & Hashimoto/ 2020). The distinction lies in the fact that instead of truncating high-loss examples, uncertainty sampling opts to train on such challenging instances, allowing the model to focus on handling difficult cases.
The selection of high-entropy data is associated with employing the fg norm of the model's predic- tion probability vector [|pe x)) 2- Rényi (96II) establishes the equivalence between selecting data with high Rényi entropy and selecting data with low E norm of the predicted probability vector:
(-)((=
log (-1=))))))22)
ENT combines the benefits of both loss and entropy-based data selection by using the & norm of the error vector: [|pe(-]x)- (H())ll.. Data with high error &g norm comprises instances with low predicted probability and low entropy. Intuitively, ENT truncates data that the model is certain is incorrect.
C TASKS, MODEL SIZES, AND HYPER-PARAMETERR
Table Sshows the datasets, sizes and the evaluation metrics that we used in our paper.
Happer-arameetes. We use the official implementationf) of Loss Truncation and re-implement TaiLr ourselves. For fair comparison with Loss Truncation, we include results of both truncating a fixed fraction of data (ENTT-Fraction) and truncating according to a pre-defined threshold (ENT- Threshold). We fix the truncation fraction to be 0.1 for ENT-fraction and choose the best result among three truncation fractions {0.05, 0.1, 0.2) for Loss Truncation. For TaiLr, in addition to the recommended hyperparameter setting for machine translation and summarization in Ji et al. (2023). we additionally tuned x hyperparameter combinations: ? E {0.1, 0.5, 1.0} and lower threshold of the weighting factor among (0.1, 0.2, 0.3). We select the best results among three threshold values
hhans://githh..om//daaan//// dropper
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Datasct
Size
Metrie
Trained-from-eeratee
Billinuua MT Multilingual MT
ParaCrawl
En-Cs (SSM), En-Ru (50M). En-Zh (13M)
SacreBLEU
opus-100
1M sentences for all directions
SacreBLEU
Multilingual MT
opus-100
En-Gl (400K), En-Fr (IM)
SacreBLEU
Language Modeling
WikiText- 103
100M tokens
Perplexity
Fine-tuning
Summarization
CNN/Daily Mail
286K article-uummary summary pairs
Mouge-1/2/LL55
Robustmess
Bilingual MT
opus- 100
En-Fr (1M)
SacreBL.EU
Table Dataset statistics for experiments. translation experiments.
We report the number of parallel sentences for all machine
{1.35, 1.38, 1.4) for ENT-threshold 0 For of our Machine Translation experiments, we report SacreBLEU (Post 2018) result=" on the test set. For all of our experiments, report the average results of three runs with different random seeds.
D ALGORITHM PSEUDOCODE
Algorithm 1: Error Norm Truncation Fraction
Input:
logits: Sarch.Tensor the output logits from the LM.
shape of (batch size, seq length, vocab size) Forch.Tensof. the one-hot vector of target tokens shape of (batch size, seq length, vocab size)
labels:
fraction:
float, the fraction of tokens to prune.
Output:
Loss:
torch.Tensor
Compute binary mask probs = nn sonetioona..softma (logits, dim=-1)
labels dim=-1) sorted en = torch. (en. vin.view())) descendingsTruue) .valees threshold sorted_en [int (fraction len (sorted
en torch. norm
threshold < fixed number < sqrt (2) in ENT-Threshold
mask en threshold
Compute loss
loss fn = nn. NLLLoss (reduction- 'none')
loss loss fn (torch. log (probs), target)
loss = loss mean ()
E EXAMPLES
Table 7 shows examples from the opus-100 dataset where errors are found by large error norms.
"The threshold values was based on preliminary experiments: the maximum of emrrr la norm is / mz 1.414
BL EU]nrefs:1 case:mixed|Jeffno wtw.ffoorr200smmmothooo
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Source: <hey used forcast cereals, industrial and other crops.
Target: Elles sont utilisées pour les prévisions concernant les cultures céréalières, industrielles
Source: <2fr>> And make of the heirs of the garden of bliss.
Target: et fais de moi | un des héritiers du ardin des délices.
Source: < <2de>>Look for me in the end zone after this play.
Target: Red 7, Red 7, Red
Du find est mich nach dem Spiel in der End -Zone
Table Translation examples from the opus-100 dataset. Tokens with error norm larger than 1.0 are high- lighted in yellow and tokens with error norm larger than 1.3 are highlighted in red. The error norm helps us spot mistakes in the data. Instead of removing entire sentences, focusing on the highlighted tokens for truncation preserves the rest of the sentence, which can still hold valuable information.
F BILINGUAL MACHINE TRANSLATION RESULTS
For bilingual translation, train seperate models for the following three directions en-fcs.ru.zh from the ParaCrawl V9 corpus (Bañón et al. 2020) and report the BLEU (Papineni et al. 2002 results on the WMT22 test set (Koemi al. 2022).
Table 8 shows the BLEU scores of equipping MLE with error norm truncation compared with other soft and hard truncation baselines. ENT-fraction outperforms Loss Truncation in all three directions. ENT-Thrrehold is able to outperform all previous methods in directions En-Cs and En,Ru, only behind the best performance of En-Zh by 2 BLEU points.
En-Cs
En-Ru
En-Zh
MLE
25.2
24.6
12.5
Loss Truncation
25.2
25.3
12.8
TaiLr
25.1
25.4
13.2
25.3
25.5
13.1
ENT-Threshold
25.7
25.5
13
Table 8: Monolingual Machine Translation BLEU results trained on the ParaCrawl dataset and evaluated on WMT22 test set. Error Norm Truncation outperforms the baseline and other data truncation methods.
G MULTILINGUAL MACHINE TRANSLATION WITH MISMATCHED DATA SIZES
Tshoe the multilingual machine translation results when there is a mismatch data size. Er- norm truncation improves improvements on the high resource language in all temperature settings, indicating that removing noisy data can balance training in under mismatched multilingual setting, improving the perfor- mance on low-rew oowrresource languages without sacrificing performance on high- high-resource languages.
the low resource language pair En-Gl more compared the
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T=l
T=5
T=100
En-Gl
En-Fr
En-Gl
En-Fr
En-GI
En-Fi
MLE
27.4
38.1
27.1
37.2
27.9
37.2
Loss Truncation
27.4
37.9
27.3
37.0
27.6
37.1
TaiLr
27.7
38.0
27.5
37.1
28.2
37.5
ENT-Fraction 28.0
38.2
27.4
37.2
28.2
37.2
ENT-Threshold 28.1
38.2
27.5
37.3
28.5
37.2
Table 9: BLEU results of multilingual machine translation under different sampling temperatures. Our method was able to outperform the baseline and other truncation methods in 5 out of 6 setups. En-Gl is low resource with 400k parallel sentences and En-Fr is high resource with IM parallel sentences.
20


Reviews:
[{"review_id": "9RStRmL1RR", "reviewer": "Reviewer_CBUj", "summary": "This work proposes Error Norm Truncation (ENT), a method to clean low-quality data in training text generation models. The proposed method measures the distribution of each token between the predicted distribution and the ground-truth distribution, which is more robust than previous works of measearuing loss or probability.", "questions": "1. In Table 1, when the untranslated ration is 10%, MLE outputperforms all the methods with a relatively large margin. This is interesting and also confusing, comparing to other ratio. Do you have any explain or study on this? Moreover, in real scenoria, I think the untranslated ratio in the dataset is not as large as more than 20%, so does this result suggest that under common situation, using MLE is enough to beat other modifications?\n2. In Table 2, the advantages of ENT and also the Loss Trunc. and TaiLr are not significant against MLE when ratio <=30%. Also in real scenoria, the misordered ratio might not be that high, which also raieses the same question as above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The first half of the paper is well-organized and clearly written. I enjoy reading this part, which provides me a clear understanding of your motivation and method.\n2. The proposed method is technically sound, echoing your motivation, with theoritical proof and experimental studies.\n3. The experiments, overall, provide a good evidence of the effectivenss of the proposed method.", "weaknesses": "1. The last half of the paper is not well wrtitten with some temrs not clearly explained:\n1.1 In Figure 5, ENT (largest) and ENT (smallest) are not explained. I did not find where explains these two terms.\n1.2 In Figure 6, what and how are the ENT threshold and ENT fraction set?\n2. The authors claim that previous work would rely on hyper-parameter tuning (on fraction or threshold). But the proposed ENT method also requires these hyper-parameters. I am not sure the proposed method would be more efficient on hyper-parameter selecting.\n3. In $5.2, apart from the two settings, another setting \"Over translated\" (noted by the authros in Figure 2) should also be included.\n4. In $5.4, it is inadequate to draw such a conclusion on using which method for pre-trained model and using which method for from-sractch model, with only a summarization task. More downstream tasks should be conducted to draw such a conclusion.\n5. Title of Section 4 could be re-considered to like \"Analysis\", as case studies always provide study on a few specific \"cases\" from the dataset. But this section is analyzing on the whole dataset. However, this is just a suggestion, and does not affect my rating.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YS16XhSTXv", "reviewer": "Reviewer_FiyE", "summary": "This paper proposes a method called Error Norm Truncation (ENT) to enhance the robustness of text generation models against errors in the training data. The Error Norm Truncation (ENT) method is to truncate training data with high L2 error norm. The comprehensive experiments show that ENT improves the robustness of language models and machine translation models against various types of noise and outperforms previous methods.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper studies a fundamental issue in training neural models. \n- The proposed method is easy to implement and sounds appealing.\n- Experiments show that the proposed method can outperform previous methods on some tasks such as machine translation and language modeling.", "weaknesses": "- Even though the results have proven the effectiveness of ENT, I think the motivation of using error norm to estimate data quality can be further discussed and provide more insights about how do they correlate. \n - Theoretically, whether an example is helpful to train a model not only depends on the correctness of its label but also on the uncertainty (or entropy) of this example, according to the lessons from active learning. As uncertainty also takes into account of the prob of non-targets as ENT does. Therefore, it would be important to discuss the relationship between uncertainty and ENT in this paper.  \n- The proposed method achieves significant improvements on simulated training data with manually added noise (with two types of noise) but it only yields modest improvements on the standard benchmarks where training data may contain less noise. Therefore, it would be helpful if the proposed method works well on natuarally noisy benchmarks. Of course, it may be difficult to collect a large scale of training data and thus it is practical to apply the proposed method under the finetuning scenario, where a small scale of naturally noisy data is used for finetuning (for example, there is such a shared task in WMT).   \n\u000bp.s. I know there is no time for authors to add new experiments into the paper, because I am an emergency reviewer and submit the reviews just before the deadline. However, I would be happy to see more experiments from the dialog box in the openreview system a couple of days later.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "k9r1sdSmt5", "reviewer": "Reviewer_Fsrz", "summary": "This paper introduces a new method for noisy data truncation. It computes the l2 norm of the difference between the model's token distribution and the one-hot groundtruth. The error norm provides a measure of data quality. The Experiments on language modeling, machine translation, and summarization demonstrate the effectiveness of the proposed method.", "questions": "One motivation of the proposed method is that previous works treat all non-ground truth tokens as equally incorrect. However, I do not see how the proposed method solve this problem. In my opinion, all non-ground truth tokens are treated in the same way in the proposed method. can you explain?\n\nFollowing above, I think one potential improved version of the proposed method is to take into account the similarity between non-ground truth tokens and ground truth tokens when computing the l2 norm.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The proposed method is simple, effective, and robust.\n- The analysis experiments are insightful.\n- The experiment results on language modeling, machine translation, and summarization are comprehensive and the results are good.", "weaknesses": "- The improvements over existing methods seem marginal in some experiments.\n- The motivation of the proposed method is a bit unclear to me. See my question below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Q3hjPl2N4t", "reviewer": "Reviewer_p8xo", "summary": "This paper proposes a method for automatically identifying which data points are potentially noisy.\nSpecifically, this is done based on identifying points that have high L2 norms of the error and removing them from the loss function.", "questions": "1. How was the thereshold hyperparameter tuned in all experiments in the experimental section?\n2. I was confused by the second equation in section 3, should it be a \"less-than\" sign rather than a \"greater-than\" sign?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* Overall, I do really like the concept of the paper. It is simple and generally makes sense.\n* The method seems to have strong empirical results in synthetic settings, and reasonable experimental results in less synthetic settings.\n* Figure 4 is a nice analysis demonstrating that intuitively why the proposed method is better than log likelihood.", "weaknesses": "I have a few concerns about the paper:\n\n* It seems that some hyperparameter tuning (detailed in appendix C) was done for all of the loss modification methods, but none was done for MLE. Because of this, perhaps some of the gains over MLE can be attributed to randomness in training, rather than to inherent goodness of the method.\n* The results on real-world datasets (sections 5.3 and 5.4) are somewhat underwhelming. I see small gains (and perhaps small gains are a good result already given how simple the method is), but I'm also a little bit concerned whether these are interesting enough for practitioners to be excited and go back and implement/use this method. Overall, I feel like the paper lacks a big convincing results, such as  significant improvements to SOTA on a dataset that people care about.\n\nNote that I am not saying that the work is solid, it seems to be done reasonably well, I'm just not sure how much impact it will have on the community given the current empirical evidence.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (spotlight)