Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
Authors: Irina Bejan
Artem Sokolov
Katja Filippova
Venue: EMNLP 2023 Main (2023)
Keywords: data filtering
influence functions
self-influence
curriculum learning
noisy data
machine translation
question answering
TL;DR: Self-influence scores are stable and curriculum learning can use them to improve upon data filtering.

Abstract: Increasingly larger datasets have become a standard ingredient to advancing the state-of-the-art in NLP. However, data quality might have already become the bottleneck to unlock further gains. Given the diversity and the sizes of modern datasets, standard data filtering is not straight-forward to apply, because of the multifacetedness of the harmful data and elusiveness of filtering rules that would generalize across multiple tasks. We study the fitness of task-agnostic self-influence scores of training examples for data cleaning, analyze their efficacy in capturing naturally occurring outliers, and investigate to what extent self-influence based data cleaning can improve downstream performance in machine translation, question answering and text classification, building up on recent approaches to self-influence calculation and automated curriculum learning.

Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
Irina Bejan" and Artem Sokolov and Katja Filippova Google DeepMind
irinam, be (aanggmilll com, (artemsok, kat jaff ] @google..o
Abstract
1.1 Detecting Outliers
Defining outliers and how they may (harmfully) in- fluence model predictions in a task-agnostic way is hard and so, until recently, mostly task-dependent heuristics have been employed (Wang et al., 2018). More principled approaches define an impact of a training instance via the concept of influence func- tions (henceforth IFs) (Cook and Weisberg, 1980), which quantify the effect the loss on test point when removing individual training point x. For example, Koh and Liang (2017) used access gradients and their fast products with the loss Hes- sian (Pearlmutter, 1994) to approximate the loss change at æ that would occur had æ been infinites- imally upweighted in the training set. IFs have been used for debugging of machine learning mod- els (Han et al., 2020), data poisoning attacks (Koh et al., 2022) and detecting dataset errors (Schioppa et al., 2021; Kong al., 2022). There, it has been conjectured and empirically tested that filtering highly self-inftuential (2 x)) points, i.e., the ones that would cause a large loss delta on themselves (suggesting that they are "unsupported" by other data points and need to be memorized), does lead to improvements in synthetic and real scenarios. To deepen this line of work, we also operational- ize IFs detect outliers with self-influence scores, and formulate our first research question being:
Increasingly larger datasets have become stan- dard ingredient to advancing the state-of-the- art NLP. However, data quality might have already become the bottleneck unlock fur- ther gains. Given the diversity and the sizes modern datasets, standard data filtering is not straight- forward apply, because of the multifacetedness of the harmful data and clu- siveness of filtering rules that would generalize across multiple tasks. We study the fitness of lask-agnostic self-influence scores of training examples for data cleaning, analyze their effi cacy in capturing naturally occurring outliers, and investigate to what extent self-influence based data cleaning can improve downstream performance in machine translation, question answering and text classification, building up recent approaches to self-influence calcula- tion and automated curriculum learning.
1 Introduction
Deep learning on increasingly larger and diverse data sources brought impressive advances in natu- ral language processing (NLP), however, data qual- ity might be the major bottleneck to unlock further gains (Kumar al., 2020). NLP data are usually ac- quired via large-scale weakly-labelle data scraping or crowd-sourcing labels from non-expert human annotators, which are both error-prone (Bowman and Dahl, 2021). At the same time, ambiguous training data are also known to hurt models' per- formance through overfitting or memorization in overparameterizet networks (Zhang et al., 2017). Finally, not all data equally easy learn and overly complex instances may hinder learning as well. Below, we refer to all of those cases label noise, dut-of-distributinn, ambiguous or difficult- to-learn examples - by an umbrella term outliers. Two key questions of our work are: How can out- liers be detected and how should they be dealt with?
RQI: When are self-influence scores effective for detecting outliers?
The above improvements, however, contrast with the observations that IFs are sensitive to model and training hyperparameters in the general, 2 + x, case due to violation of the convexity assumption by IFs in deep learning: Basu et al. (2021) showed that depth and width of the network, its architecture, training regularization and the stochastic approx- imations inside IF have strong effects on the IF accuracy and stability (measured on retrieved influ- ential æs for a fixed 2), which are aggravated with
The work was done while interning at Google.

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the network size. K and Søgaard (2021) further found that IFs are sensitive to the parameter ini- tialization, ordering of the training data and batch size. Both papers thus doubted that IF scores of training instances would be reliable for practical purposes, and that retraining after removing or fix- ing the flagged instances would lead to improve- ments. Very recently Schioppa et al. (2023) gave theoretical perspective on IFs instability.
RQ3: Does AutoCL with self-influence scores bring gains compared filtering?
1.3 Contributions
We study the stability of self self-influence scores, which are task-agnostic and, if stable, would be an attractive candidate serve as the data clean- ing foundation. We further analyze the efficacy of capturing naturally occurring outliers by IFss and investigate to what extent self-influence can im- prove performance in NLP tasks with Transformer architectures, building up recent improvements in IF approximation accuracy and scalability with the Arnoldi iteration based IFs (ABIF) (Schioppa et al.. 2021), and AutoCL (Kreutzer et al., 2021). In more detail, our contributions are:
Since, at minimum, for self-influence point outliers in any objective and verifiable sense, they should exhibit certain stability, this leads to the second research question of our work:
RQ2: How stable are self-inftuence scores?
Unlike general influence, the self-influence stability has not been covered by previous studies.
Stability of self-influence scores. We start by measuring how stable self-influence scores are, since this is prerequisite for both successful data filtering and data scheduling. To this end, in $3, we study correlation and overlap of data rankings by self-influence scores across different model states, i.e., different final states the training converges to as a function of varying batch size, random seeds of data sampling, data ordering and IF calculation. We also explore the correlation be- tween model prediction stability (defined below as model churn) and IF's sensitivity to architec- ture. We find that, unlike the general (z + x) IF scores, the self-influence (= x) scores are stable with respect to training and model hyperpa- rameters, and across architecture variations, but care should be exercised in transferring findings between architectures of different capacity.
1.2 Dealing with Outliers
A standard approach to reducing the harms caused by outliers is to filter them out (Khayrallah and Koehn, 2018 8; Peskov et al., 2019). However, com- ing up with filtering rule that would generalize across multiple tasks is not straightforward. Most scalar-based (incl. self-influence) filtering schemes would prescribe setting a threshold cut-off value to delineate outliers from the rest data. This may be reasonable for datasets where the to- data portion has no apparent signal, however, in more realistic scenarios many outliers are at least somewhat useful. Applying threshold filtering in such situation may lead to performance decrease a portion of useful signal would be lost. For example, memorizing some outliers (e.g. under- represented training examples) can in fact improve accuracy (Feldman and Zhang. 2020).
Effectiveness of self-influence scores. In $4, we employ suite of different in-distribution and out-of-distrbbution (o.o.d.) evaluation setups and show that filtering out highly self-influential ex- amples more effective for the o.o.d. setup. We hypothesize that self-influence capturing general outliers prevents learning systematic noise pat- terns that would otherwise artificially inflate per- formance in the in-istribution evaluation setup, making it harder to improve upon with filtering. Furthermore, investigate what is captured by influence scores using both natural outliers and synthetic label noise, showing that natural data can be spread among high and low influen- tial samples, thus the common top-X% filtering strategies can be ineffective.
Motivated by this shortcoming, in this paper we explore alternatives to filtering where it is possible to make use of outliers and in particular consider automated curriculum learning (AutoCL.. AutoCL. covers range of algorithms, where not only the training data are presented to a neural network in a different order than random sampling, but also where this order is adapted alongside main training based on learning progress (Graves et al., 2017: Kreutzer et al., 2021). This particularly useful when dealing with outliers, as we can learn (via the self- inff-influence proxy) to ignore the outlying data samples and prioritize the most helpful ones, with- out having to choose apriori the cut-off threshold.
Thus, the final research question we address is:

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Data filtering automation. The fixed percent- age filtering can also be costly to tune or inaceu- rate, while attempts to automate it using abrupt changes at the top of the ranking have a low re- call rate (Lam et al., 2022). To remedy, in $5 we employ bandit AutoCL to dynamically detect, during training, the harmful or the useful train- ing data quantiles to feed from at each step. The possible bandit actions derived from the self- influence ranking and further split into a fixed number of discrete buckets. As a result, AutoCL adjusts on-the-fly fly the ratio of high or low influ- ence examples to train on. This is more gen- eral than threshold filtering, which is particular (static and weighweig case of general (dy- namic and soft-weighted) schedules.
QA:TriviaQA. This dataset includes 110k question-answer pairs authored by trivia enthu- siasts, who gathered evidence documents for an- swering questions drawn from Wikipedia and Bing search results (Joshi et al., 2017). This is a par- ticularly high quality supervised task, but is still difficult to learn: the input length is on average 10 times longer than in NQ, bringing additional chal- lenges such complex, compositional questions that require higher cross-sentence reasoning. We evaluate both on TriviaQA and on NQ using the Exact-Match (EM) and Fl scores.
Puasification:Wiippee Toxicity. The dataset contains 223k human annotated comments from Wikipedia talk page comments (Wulczyn et al., 2017). While the original dataset covers a variety of toxicity subtypes, we only consider a binary classification into toxic and non-toxic comments in (Ebert et al., 2022), and report accuracy and F1.
2 Tasks, Datasets, Models and Methods
Throughout this study, we investigate how self- influence methods perform and generalize across multiple NLP tasks, varying the tasks' nature, size, noise levels and model architectures (Table 1).
2.2 Models
We experiment with three different architectures: the standard Transformer-base on the MT task, two sizes of the state-of-the-art LongT's architecture for long inputs the QA tasks, and the classic BERT- base and T5 architectures on the text classification task. See $A for training details of each of those.
2.1 Tasks and Datasets
MT:Paracrawl. We consider the German- English translation task from the noisy Paracrawl corpus (Bañón et al., 2020), which consists of 100M sentence pairs obtained via web-crawling. We evaluate using BLEU after fixed number of steps on the newstest17 set from WMT17 to match the setup of Schioppa al. (2021), who also filtered Paracrawl with ABIF.
2.3 Methods
Influence functions an approximation to the loss change at the test point after infinitesimal upweighting of training point æ (Koh and Liang, 2017): I [xx =) = (V))(z), H ((x)) where (((x) ) is the gradient of the loss L at the points x or x, and H = V2 is the Hessian of the model at parameters O For deep learning, the Hessian is impractical to compute exactly, so Koh and Liang (2017) proposed approximate estimation proce- dure calculate [(ar, 2). Recently, Schioppa et al. (2021) proposed more accurate and stable method that uses Arnoldi iteration approximate the in- verse Hessian in subspaces spanned by H's largest eigenvectors. This enabled scaling up the computa- tion of influence scores to hundreds of millions of training points and model parameters. We use their released code to compute T [(z, =) (ABIF).
QA:Natural Questions. The NQ dataset consists real queries issued to the Google search engine, alongside Wikipedia fragments that could poten- tially contain an answer (K "Kwiatkowski et al., 2019). Each query can have short answer (incl. empty) and long answer, the latter requiring to predict spans from the fragments. Since we run our NQ experiments with a seq2seq model, we adopt the dataset version which only covers short answers from (Guo et al., 2022), who split the official train- ing set of 307k samples (90% for training, 10% as the dev set) for fine-tuming, and use the offi- cial dev set for testing. From the data quality per- spective, being real user queries, NQ is relatively clean but contains high degree of natural ambi- guity: about 33% of NQ annotations are debatable and 16% are wrong, meaning the Wikipedia frag- ment provides no evidence for the ground-truie truth answer (Kwiatkowski et al., 2019).
TracIn (Pruthi al., 2020) is gradient-only al- ternative influence definition that relies on C' checkpoints to approximate by how much x's gradi- ent changes model parameters and, in turn, the loss at z: (x, 2) = & D (Ve. L(x), Ve, [())).

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Dataset
Task
Noise
Model
Training
Architecture
Params
Train/Dev/Test
Parucraw]
MT
very high
ene-des
from scratch
Transoommrr base
60M
100M/3K/33
Natural Questions
QA
low
tuning
Li-se/abee
220M/77UM
TriviaQA
QA
low
ene-dee
line- dine-tuning
Long IS-base
220M
BSK/11K/11K
Wikipedia Toxicity
text-class.
high
enc( dec)
fine tuning
BERT base/TS base
110M/220M
144k/16kk/3k
Table 1: Dataset and model statistics.
The question of which layers are more effective for IFs still open, with recent work showing good results using the last or few last layers (Han et al., 2020; Barshan et al., 2020), but also using the first layers (Yeh et al., 2022). Therefore, we experiment with IF methods in three variants: first (the first layers of the encoder and decoder), last (last two layers of both) and all parameters and draw a comparison between them. For the Paracrawl experiments, following Schioppa et al. (2021), first and last only include the first two encoder and the last two decoder layers; and the ABIF cigenvectors were extracted from a model trained on WMTI7. 7. More details on self-influence computation in $B.
played the best-in-hindsight arm.
To quantify the learning progress, existing met- rics are looking at the loss (C) decrease the in- crease in model complexity (Graves et al., 2017). Among those, we use normalized prediction gain reward (pgnorm):
//0++)//C(0) and the co- sine similarity reward between the gradients of the training and the reward batches, where the reward batches are (re)sampled from development sets, fol- lowing (Kumar et al., 2019; Kreutzer et al., 2021).
3 Stability of
In this section, we evaluate the stability of self- influence scores with respect to model states, ar- chitecture and ABIF--pecific hyperparameters with Spearman rank correlation and the 90th percentile overlap (i.e. overlap between top-10% examples), suggested by K and Søgaard (2021) as an alterna- tive to global correlation since one normally cares only about highly self-influential examples.
Self-influence and outliers. For both influence definitions, the self-influence of a training point can be derived from them setting 2 x. It has been conjectured that high values of self-influence indicate data outliers (Koh and Liang, 2017; Pruthi et al., 2020); intuitively, if removing x deteriorates the loss value on itself, then should be different enough so that the prediction on æ could not be learned from the rest of the training data and had to be memorized. Grounding the influence definition in the loss magnitude covers many possible causes for being an outlier, such as mislabeling (i.e., true noise), ambiguity (i.e., multiple possible labels, depending on the information missing from the input), being out-of- dii-t---iittittiinn.. or being a difficult example (for the model) for other reasons.
It is important to understand whether self- influence ranking, given that it is thought to be predictive of data quality, is inherent data prop- erty or it is mainly rooted in the architecture. In this regard, we look the extent to which model stability and self-influence stability are intercon- nected, via the model churn metric (Cormier et al., 2016), which is the joint expected percentage of errors on a test distribution. For example, if model A is right on 9% of the examples that model B gets wrong, and B is right the 10% of the examples that gets wrong, the churn is 19%. While chang- ing weight initialization does not always impact accuracy, can result in net zero wins and losses, referred to as unnocessary churn.
Automated curriculum learning. We use the framing of curriculum learning as a multi-armed bandit problem (Kreutzer et al., 2021), where arms represent distinct subsets of the data that are con- sidered bandit actions and are played at each train- ing step 6. When an action a' is played (as man- dated by the EXP3 or EXP3S algorithms (Auer et al., 2002)), a uniformly sampled batch belong- ing to that data subset is fed to the model and the scalar reward feedback y' yt, received, where Yt would be an unknown reward vector of all possible actions. Through this, the bandit learns alongside the main task to minimize the re- gret, R= E E v'] max, E, y of not having
3.1 Dependence on model states
We investigate if the self-influence scores are sen- sitive to the model state, i.c., initialization of the model, data ordering or batch size. Previously, K and Søgaard (2021) showed instability of general IFs with regards these variables, while we turn attention to relf-influence stability, given its foun- dational role for data filtering.

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Layers Method
LS: NQ
Transformer: Paracrawl
In order for self-influence surface dataset er- ror/outliers, low degree of instability across such changes would be necessary to avoid misattributtio of self-influence stability to model architecture. We compare the self-influence scores resulted from:
90th
Spearman
90th 7
Spearman
ABIF
77.78
0.781
40.71
0.727
Trucin
80.49
0.938
63.47
0.872
ABIF
87.67
0.933
51.03
0.726
TracIn
86.95
0.949
64.83
0
a6
ABIF
78.03
0.804
44.58
0.771
Table 2: Stability of self-influence estimates to chang- ing model states (batch size, data ordering and model initialization), using ABIF and TracIn for LongT5-base on NQ and Transformer-bas base on Paracrawl.
LongT5-base vs. LongTT--arge: we fine-tune both models with the same hyperparameters analyze the sensitivity of self-influence model size that increases from 220M to 770M params.
Setup. To evaluate the sensitivity of self- influence to changes in model states, we fine-tune the same model twice: first we fix all hyperpa- rameters and second, we vary the batch size, data ordering seed and the model initialization, keeping the rest of the hyperparameters fixed between the two runs, to look into the worst case scenario out of the ones proposed in K and Søgaard (2021). For both runs, we compute the self-influence scores for the training set and compare the two resulting rankings for all three variants of ABIF (30 eigenvec- tors) and TracIn. We found it too slow to evaluate TracIn when using all layers, so we only report results obtained with ABIE, We run this analysis for two architecttures/tass:: LongT5-base on NQ and Transformer-base on Paracrawl.
Local vs. Transient-Global attention of LongT5: we fine-tune two L-ngTe--base models, each with a different attention, yet the same configuration of other hyperparameters, to analyze the sensitiv- ity to increased capability at same model size.
Results. From Table 3, changing the model ca- pacity (size or attention) has a negative effect on the stability of self-influence scores, with the size hyperparameter affecting it less. The first or all configurations make self-influence scores more sta- ble to large capacity changes than last layers, which were more robust to training parameter modifica- tions. We conclude, given the strong correlation between increase in churn and decrease in stabil- ity, that model instability is a contributor to the self-influence scores' instability. Importantly, self- influence scores appear to be particular sensitive to model's 's architecture capacity, and should be used with caution across differently-
Results. From Table 2. see that both meth- ods are considerably more stable changes in the model state, than in (K and Søgaard, 2021), where the maximum 90th percentile overlap was 32.77 for IFs and Spearman correlation below 0.07. Despite that the 90th percentile overlaps for Transformer are lower, we can see the ranking correlation is still high and believe that, because Paracrawl is very noisy dataset (>90% of it is noise, as we show below), the overlaps are less informative.
mod- cls. This is expected, as the architccture and model capacity, unlike training hyperparameters, define the loss landscape and its dynamics under training data perturbations. Below, we hence calculate and use self-influence scores for fixed architectures to minimize the chances of running into instabilities.
3.3 ABIF-pertinent instability
The choice of layers has a significant impact on the stability, the last layers being more stable com- pared to the first layers, which is consistent with previous work (Han et al., 2020; Barshan et al., 2020) where the last layer also yielded better re- sults. We believe that these results indicate that self-influence is robust enough to be relied on in detecting training outliers.
Finally, ABIF is a newly developed method, we inspect the effect of its hyperparameters on stability in SC and find that contributions pertaining to ABIF itself are not of concern.
Effectiveness of Self-Influence Scores
The impact of filtering highly self-influential exam- ples on the downstream performance and the recall of synthetically perturbed training samples, have been used to measure the correctness of IFs (Guo et al., 2021; Schioppa et al., 2021), given that the ground-truth estimate via leave-one-out is unfea- sible to compute even for medium-sized models. We ask whether filtering of highly ieff-influentil
3.2 Dependence on model architecture
Basu et al. (2021) found that network architecture, in particular its depth and width, impact the accu- racy of IFs. Here, we investigate to what extent self-influence is sensitive to a broader set of model changes that affect model capacity and capabilities.

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A
Model
Churn
Layer
90th
Spearman
LongTTShase
Long' Lo-gTS-
first 77.78
0.781
TGlobal attention
TGlobal attention
8.6%
all
78.03
0.804
=128, seedshu///naa
[B|=6,, seedaahas/im
87.67
LongTS-base T'-base
LongTS-targe
first 68.06
0.630
TGlobal attention
TGlobal attention
12.77%
67.89
0.621
B 1128, soodshef/haam))
[B|=128, socdstaf/innt
last 42.00
0.432
LongTS--base
LongTS-base
first 61.19
0.591
TGlobal attention |B|= sesd
Local attention
13.54%
all 60.05
0.591
[B|=128, seedskuf/ima=0
last 41.92
0.242
Table 3: Relation between model stability and its architecture, capacity training hyperparameters the NQ dataset. Bold marks differences between models A and B. The first group of ABIF results is from Table 2.
examples more helpful for o.o.d. evaluation (by removing true outliers) or if it can also improve performance on test sets distributed similarly to training data (and containing the same error types).
Weot whe
Worg roul
Weong input
0.3-
Setup. To evaluate the performance of filtering, we calculated the self-influence scores using all three layer settings of ABIF, sorted them to retrieve the highly self-influential examples, and experi- mented with different thresholds given that the ra- tio of (harmful) outliers in each dataset is unknown. Then we retrained on the filtered data, kept the best result across the layers choices and reported nearby percentages to illustrate the performance trend.
0.1.
0.0
Quart buosd rillaraa stare (see tagh!
Figure 1: Distribution of the correct and incorrect exam- ples annotated by expert annotators in 5 equally-sized bins (quantiles) computed based on the self-influence ranking (last layers), and ordered from low (0) to high influence (4). The annotations include wrong input (con- text), wrong label, or both wrong.
We consider three tasks with same distribution evaluation (on NQ, TriviaQA and Toxicity) and three o.o.d. setups (NQ, Paracrawl and Toxicity): I) Training Transformer- using the same setup as above, and evaluating on the newstest17 dataset. 2) Fine-tuning the LongT5-base on NQ as before, but evaluating on the TriviaQA dataset to make it o.o.d. To align the task definitions, we only keep the normalized answer from TriviaQA's 's answers list, whereas usu- ally the metrics are computed against each of the given answers and the maximum score is reported. Fine-tuning on Wikipedia Toxicity, but evaluat- ing on the o.o.d. Civil Comments (CivilC) develop- ment set of 97k comments (Borkan et al., 2019).
improvements on Toxicity, which is expected to be very noisy. This shows that the common heuristic of filtering highly self-influential examples might not be the best fit for the in-distribution tasks and we develop further why.
model on Paracrawl
4.1 Noise captured by high self-influence scores
Here we study how a naturally occurring noise, as annotated by human experts, is partitioned by the self- self-influence ranking. Additionally, we compare to synthetic noise, tested previously to be accu- rately retrieved by high self-influence in (Schioppa et al.. 2021), see if significant differences occur.
Results. From Table 4, we see that self-influence filtering using ABIF brings higher improvements for the o.o.d. evaluation setup: up to +9 BLEU points on Parucrawi-newsstsstl 7 and +3 Fl points on NQ-TriviaQA setup, with negligible improve- ment in the Toxicity-Civill case, which shows that training on cleaner datasets improves performance on an o.o.d. test set. In the in-distribution setup, TriviaQA and NQ trained on full data always out- perform filtering, which is not surprising given both are high-quality datasets, but also brings very small
Setup. We use the 5-way annotations by hu- man experts on a set of 205 uniformly sampled NQ examples and released with the original pa- per (Kwiatkowski et al., 2019). The examples were annotated as correct, debatable and wrong, but we treat the debatable entries to be in the correct bucket as there is no strong evidence that suggests they are wrong. We compute the self-influence scores on the first and the last layers and analyze how the natural noise being ranked.
For comparison, we looked at the ability to

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Train
Eval
% Used
EM
F1
BLEU
100%
63.97
NQ
NQ
90%
58.49
63.06
Troin
Eval
% Used
Acc
F1
100%
78.08
80.17
chassineaddd
100%
92.61
95.80
harzbas
TrivioQA
TrivisQA
98%
77.49
79.72
Tuxicily Tuxicity
95%
93.15
96.13
9092
74.64
76.96
95.97
100%
20.55
100%
95,28
97.51
NQ
TrivisQA
95%
18.96
23.52
Toxicity
CivilC
95.42
97.59
9056
17.52
21.85
100%
21.36
Paracraw]
Dowstesl17
10%
30.45
Table 4: Performance of percentile filtering of highly self-influential examples as per ABIF self-influence in- and out-of-distribution test sets, for seq2seq and classification tasks. We report the maximum over all, first, and last settings. For the seq2seq tasks, the metries that don't 't apply are marked with dashes.
retrieve synthetic noise via the self-influential examples and compute the recall in the top- 10%/20%/30%% of self-influence scores. We alter the original data by uniformly sampling 10% of the dataset for which we shuffle the labels, ensuring all labels were changed from its initial value. This is important because a significant amount of ques- tions have no answer (they are not answerable), because they are natural search queries.
datasets. To rectify, Lam et al. (2022) attempted clustering and identifying abrupt changes in the magnitude of the sorted self-influence scores, but this resulted in a low recall rate. Hence, we move to automated curriculum learning to dynamically identify the outlying parts of data based on the buckets of self-influence scores without commit- ting to completely remove any of them.
First, we validate how different the quality sig- nal is from each individual self-influence bucket NQ. We found (Figure 4 in $F) that EM of the high- est self-influence bucket is much lower (although not zero) compared the rest of buckets, which are in turn difficult to separate, possibly, because they contain data of mixed usefulness. A positive aspect of AutoCL is that, due to exploration, the model regularly visits all buckets, and may dynami- cally up- or down-weight buckets depending the model's current needs, overcoming the rigidness of fixed threshold filtering.
The synthetic noise retrieval confirms previous findings with high recall, as 29% of the synthetic noise lands in the top-20% ranks and 94% in the top-30%, when using last layers (vs. 84% in top-30% for first layers). We hypothesize that the synthetic noise is not predominantly in top-10% because other forms of outliers are already present in the dataset that are more harmful to the model.
Results.
The behaviour of natural noise is considerably different. It barely comes up among highly self- influential buckets as we can see in Figure 1, but is distributed predominantly among the lowest and mid-influential buckets. Examples annotated as having wrong labels are absent from the top-20% Additionally, we see that input noise does not affect the model as much as label noise, given that exam- ples with wrong input are almost evenly distributed in the lower 80% of the data. These results suggest that manual tuning of the filtering threshold of self- influence rankings may not find percentile range which, if removed, improves performance.
Setup. We verify the feasibility of AutoCL with two definitions of the self-influence, given by ABIF and by TracIn, and check that the findings are con- sistent across three datasets (NQ, Paracrawl and Toxicity T5). Bandit actions are mapped equal-sized data buckets corresponding fined number of percentile ranges of self-influence scores. We first explore 10 buckets, which should allow the bandit to at least match the performance of filtering, and then consider more granular setups (20 and 25 buckets) which would be infeasible to manually test against filtering. We expect the ban- dit not to use much of the high self-influential buck- ets, nor the lowest bucket, which prior work found to be less useful because of its simplicity (Feldmman and Zhang, 2020; Schioppa et al., 2021). Follow- ing (Schioppa et al., 2021), we report BLEU for Paracrawl at 10k and 200k steps. As baselines
prede-
5 Automated Filtering of Outliers
As showed above, using self-influence to filter noisy training data has the drawback of the diffi- culty of choosing a right cut-off threshold. Yet, trdd--rnd search for fixed threshold based on the downstream performance remains popular, which is costly and does not generalize across

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Config
Method
BLEU
BLEU 200K
model initially uses the mid-influence buckets (top- 50%/80%), followed by the high-influence outlier buckets (top-80%) which are quickly dropped and continues alternating between the lowest buckets (in bottom-20%) For Toxicity, the policy is not interpretable (though the high influence bucket is heavily used at all stages) but still brings more gains compared the filtering, As TriviaQA is very clean human-curated dataset, filtering does not improve over baseline, and AutoCL brings only nominal insignificant gains (see $D).
Baseline
13:75
21.36
Fittered 90%
ABIF
26.8
30,45
Panicranl
Filtered 90%
TraCI((])
22.10
27.87
ABIF
21.45
27.48
AutoCL- bins
ABIF
25.50
30,45
AutoCL- 25 bins
ABIF
24.13
31,38
AutoCL bins
TraIn(I))
18.60
29.33
Config
Method
EM
F1
Natural Questions
Raseline
49.05
63,97
Filtered 10%
ABIF
58.49
63.06
Filiered 10%
Tuela(3)
$8.09
62.75
AutoCI.-
ARIF
59.72
64.33
AutoCI. bins
ABIF
59.20
64.01
AutoCL 25 bizs
Trela(3)
59.59
64.32
Config
Method
Acc
F1
Finally, one might wonder if AutoCL improve- ments are due to self-influence or due to the in- creased data scheduling flexibility, i.e., if sim- pler example difficulty signal would suffice tain similar gains. In $E, run AutoCL on NQ/TriviaQA buckets defined via difficulty sig- nals based on domain knowledge (context length, word rarity, context-question lexical overlap and question type) and find that self-influence is indeed crucial for the observed gains.
Toxseiy
Baseline
91.73
67.37
Filtered
ABIF
91 80.8
67.22
AutoCL- bins
ABIF
93.61
70.56
AutoCL 25 bins
ABIF
92.09
67.58
Table 5: Performance of threshold filtering and AutoCI, on top of self-influence scores by ABIF or TracIn (num- ber of checkpoints C' brackets).
we use the filtering methods from $4. See $G for AutoCL hyperparameters of reported results.
Related Work
Results. From Table 5, we see that AutoCL strongly outperforms filtering methods when given enough bucket granularity, for very noisy Paracrawl, noisy Toxicity and low noise NQ, and regardless of the task and IF definition. We improve over filtering on Paracrawl by +1 BLEU point, on Toxicity by +3.2 Fl points, and on NQ by +1.2 F! points. In addition, on Paracrawl at 10k steps fil- tering with good threshold outperforms AutoCL which requires time to learn which are the useful buckets. Given that ABIF and TracIn showed simi- lar results for NQ and Paracrawl, only look ABIF for Toxicity.
Dataset error detection. Deep learning models' performance can be sensitive to noisy datasets (Ku- mar et al., 2020; Zhang et al., 2017: Khayrallah and Koehn, 2018), and various instance- based scor- ing methods have been proposed, alongside with sorting and thresholding to filter out noisy training example, including bilingual cross-entropy differ- ence rank parallel corpora (Axelrod et al., 2011). "forgetting events", where an individual training example transitions from being classified correctly to incorrectly over the course of learning (Toneva et al., 2019), or area-under margin (Pleiss et al.. 2020), computed as the difference between the log- its value in classification tasks. II's were used to de- tect dataset errors (Koh and Liang, 2017: Schioppa et al., 2021), by looking self-influence how much training point influences its own loss. How- ever, these methods led to various degrees of suc- cess: in the case of IFs, Koh and Liang (2017) and Guo et al. (2021) improve performance by di- rectly fixing/augmenting the mislabeled highly self- influential training examples, but filtering lowest or highest influential examples out did not outperform training on full data in other studies (Guo et al., 2021; Kocijan and Bowman, 2020). At the same time, it brought consistent performance gains on an o.o.d. task for MT (Schioppa et al., 2021), raising the question whether filtering helps for improving
We check if the multi-armed bandit's learned policy (probabilitiee of choosing data bucket) is interpretable in Figure 2. In general, there is no incentive for the policy to be interpretable as it targets loss improvements only and may under- take redundant switches between neighboring buck- ets in setups with high bucket granularity with the same end performance. That said, for Paracrawl, the model quickly learns to drop the top-92% of the data as ranked by self-influence, which almost matches our best filtering threshold of 90%, in- stead training 2/3 of time on the bottom-4%/88% and only V/3 of time from the top-4% which cor- responds the lowest influence and is known to be less useful to the model (Schioppa et al., 2021). For NQ, there is more of bucket switching, and the

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8.005
0.00
00
0.200
1.00 1204
200K
1996
12x
10:300
15:0
200K
3.02
1.10
332
(a) Paracrawl (25 bins); very high noise.
(b) NQ (10 bins); low noise data.
(c) Toxicity (10 bins); high noise data.
Figure 2: Learned AutoCL. policies, showing the probabilities attributed to each bucket (where BO the lowest influence bin). The policy extracted from Paracrawl (a) used 25 bins (with only BO-B3 in the legend), and the ones from NQ (b) and Toxicity (c) used 10 bins.
performance in-distribution test sets is more effective for o.o.d. generalization.
noise and do online data selection to train gradu- ally noise-reduced data batches, resembling active learning. Similarly, Nguyen al. (2020) uses a self-ensemble for label filtering during training, by gradually allowing supervision from clean labels and stopping learning on the filtered noisy labels. Jiang et al. (2020) develops method that use cur- riculum learning and vicinal risk minimization to handle both real and synthetic noise. We note that curriculun-based methods have been more effec- tive than filtering, but also have inherent complica- tions, such as defining "easy" and "hard" or design- ing an effective training schedule following these definitions. To overcome this limitation, we used automated curriculum learning (Graves et al., 2017; Kreutzer et al., 2021) that employ multi-armed bandit to learn the most effective training schedule.
Harmful vs. useful outliers. A shortcoming of filtering/data selection is that outliers are not al- ways harmful. Prior work has shown that training with noise, or with injected artificial noise, can in- crease the stability of the models vis-à-vis noisy data (Vaibhav et al., 2019; Belinkov and Bisk, 2018; Heigold et al., 2018), which can be caused by dif- ferences in noise types and their interaction with the target task. Al Sharou et al. (2021) noted that only "harmful noise" that affects the performance of the system or does not carry the intended mean- ing should be removed, while "useful noise" should be kept or even added the training data it nat- urally occurs at test time (Rolnick et al., 2017). Moreover, the performance of the noise detection methods were evaluated, due to a lack of suitable datasets, on synthetic noise, but Jiang et al. (2020) found synthetic noise to affect much more DNN's capability to generalize than real noise. We are par- ticularly interested in whether that holds true for IFs and analyze what kinds of outliers are captured by highly self celf-influcntial examples.
Conclusion
We proposed a general method for improving model performance in the presence of noisy train- ing data based on self-influence and bandit cur- riculum learning, and without relying on thresh- old filtering. We showed that Arnoli iteration based self-influence scores of training instances are stable with respect varying training hyper- parameters such as batch size, random seeds, and the iteration's hyperparameters, and thus can be a reliable foundation for data cleaning methods. We further demonstrated that not all data outliers (as per human annotation) receive similarly-valued self-influence scores, what necessitates generaliz- ing threshold filtering to a dynamic data reweigh- ing. Finally, showed that dynamically reweigh- ing based on multi-armed bandits, which pick self- influence bins to sample batches from, outperforms threshold filtering on noisy and clean datasets.
Dynamic data schedules. Following this limita- tion of filtering methods, different training sched- ules that account for the training dynamics have been developed inspired by Bengio et al. (2009) and van der Wees et al. (2017). Swayamdipta et al. (2020) proposed a "easy-to-hard" training sched- ules based the mean (confidence) and standard deviation (variability) of the gold label probabil- ities over the training epochs, where hard points to samples with low confidence and low variabil- ity. Wang et al. (2018) proposes using a small amount of trusted data to help the model measure

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8 Limitations
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009. Curriculum learning. In ICMI..
A potential limitation of approach is the re- quirement to precompute self-influence scores pretrained model and retrain again using these scores. While recomputing the self-influence scores during training may make them reflect the influence w.r.t. the current state of the evolving model better, doing this efficiently would be non- trivial engineering problem. Additional unexplored factors are the choice of the training data for the scores-producing model (i.e. trained on clean data, in (Schioppa et al., 2021) and in our Paracrawl experiments, or on the to-be-cleaned data in our QA and toxicity experiments) and the trade-offs bandit rewards which influence the overhead of bandit learning alongside the main training (from negligible for the cosine similarity reward to an additional forward pass for the pgnorm reward). Finally, left exploration of the impact of natural noise on performance, tusk-specific rewards and exploiting other signals for filtering future work.
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2019. Nuanced metrics for measuring unintended hias with real data for text classification. In wwW.
Samuel R. Bowman and George Dahl. 2021. What will it take to fix benchmarking in natural language understanding? In NAACL.
R. Dennis Cook and Sanford Weisberg. 1980. Char- acterizations of an empirical influence function for detecting influential cases in regression. Technomet- rics, 2254):495-508
Quentin Cormier, Mahdi Milani Fard, Kevin Canini, and Maya Gupta. 2016. Launch and iterate: Reducing prediction churn. In NIPS.
Jacob Devlin, Ming- Wei Chang. Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In NAACI.
Sebastian Ebert, Alice Shoshana Jakobovits, and Katja Filippova. 2022. Understanding text classification data and models using aggregated input salience. In R2IIAAI workshop at AAAI.
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A Model training details
Layers 90th O Spearman
number of eigenvectors
We experiment with three different architectures: the standard Transformer-base on the MT task, two sizes of the state-of-the-ar LongT5 architecture for long inputs on the QA tasks, and the classic BERT- base and T5 architectures for text classification.
first
96.79
0.99
last all 93.90
96.74
0.99
0.99
initialization
first
96.21
0.99
last
96.92
all 97.08
0.99
Transformer. For the MT task, we implement the standard, 6-layer, Transformer-bas architec- ture (Vaswani et al., 2017) trained for 200k steps with batch size of 128 using T5X (Roberts et al., 2022) on 16-core TPUv2 and fixed input length of 128 on Paracrawl dataset. For training, we use Adafactor with a learning rate 2.0 and the rsqrt decay (Vaswani et al., 2017) of 0.8, a dropout rate of 0.1 and a 32k SentencePiece model.
Table 6: Stability of self-influence estimates with re-
spect to ABIF hyperparameters using LongT5 on NQ.
number of top eigenvalues to use, and the num- ber of Arnoldi iterations. We use 30 eigenvalues and 60 iterations for computing the self-influence scores, and compare it, resp., to 100 and 200 an ablation.
Lo For our QA tasks, we use the state-of- the-art LongTT-base and LongT'-larre architec- ture (Guo al., 2022)' implemented in T5X, using 32k SentencePiece model, batch size of 128 and AdaFactor as the original work. We fine- tune the models on NQ for 261k steps on 16- core TPUv2 to convergence, with a learning rate of 0,001, dropout rate of 0.1 and a fixed input length of 4,096. We employ the same setup for TriviaQA, except we double the input length, to account for TriviaQA's much longer contexts than in NQ.
TracIn self-influence. We employ TracIn as baseline for NQ self-influence scoring, and use fixed projection size of 1,024 and three check- points: from the beginning of the training (60k steps), middle of training (140k) and the final checkpoint (260k). We could not scale TracIn to 100M Paracrawl examples, so we use its variant by (Schioppa et al., 2021), who reduce gradient dimensionality to 30 using Gaussian matrices for the last checkpoint 100k steps.
C ABIF-pertinent instability
For the text toxicity classification, we periment with BERT-base (Devlin et al.. 2019), and fine-tune for 35k steps with early stopping, batch size of 32, learning rate of 10-6, weight decay of 5. 10-6, imput length of 128 and dropout of 0.5.
BERT.
Finally, given that ABIF is newly developed method, we inspect if different choices of hyper- parameters for ABIF can contribute to the instabil- ity, including layers choice, number of top eigen- vectors (30 vs. 100) and the initialization seed, by recomputing the self-influence scores with vari- ous configurations using the fixed hyperparameters variant of the LongT5- fine-tuned earlier.
T5. To use our T5X AutoCL. implementation, we reframed the toxicity classification task as a seq2seq task in T5 (Raffel et al., 2019) by pre- dicting two tokens: toxic and safe, treating other output tokens misclassifications, and report F1 for the toxic class. The T5-base model is trained for 120k steps, with input length of 128, batch size 64, dropout rate of 0.1, using Adafactor, with a learning rate of 0.01 and decay rate of O.
Results. From Table we can see that contribu- tions that pertain to ABIF itself are not of concern since there are largely unaffected by the different choice of its parameters. The 90th percentile over- lap is lower, despite the almost perfect correlation, because the overlap is sensitive insignificant movements in the vicinity of the cut-off value.
B Self-influence calculation
ABIF self-influence. To trade-off between mem- ory/speed for accuracy, ABIF introduces several hyperparameters that may impact its accuracy, in- cluding the choice of layer's gradients to use, the
D AutoCL on clean dataset: TriviaQA
In Table 4, we saw that filtering for the in- distribution task for TriviaQA proves unsuccess- ful, attributable to TriviaQA being a very clean human-curate dataset. Regardless, we attempted
tgit wtt.:////..oolleeeeeerrrrcc//aaa

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LongTT5-base as in earlier sections, with the excep- tion of training on NQ for 200k steps. The signals we employ are:
84 80
0.3-
Context length: (Platamios al., 2019) proposed using sentence length proxy for translation difficulty. We consider the length of the context because longer context would require more cross- sentence reasoning to find the answers.
9.4-
Figure 3: Policy learned by the model during AutoCL, showing the soft-max weights attributed to each bin (where BO is the lowest influence bucket). This policy leads same downstream quality the full data and training with uniform sampling.
Another layer of difficulty can be added by very specific domain-questions (thus words that rarely appear in training). According to Platanios et al. (2019), low frequency words make it difficult to learn robust representation of the words, but also make the gradients of the rare word embeddings to have high variance. Therefore, Platanios et al. (2019) proposed us- ing the likelihood of the unigram probabilities to aggregate the word frequencies in a difficulty heuristic. Given corpus of sentences, sii=1 the relative word frequencies are defined as:
Word rarity:
to run the AutoCL on it and found that the method reaches the same quality as the model trained on the full data (Table 7), despite not using uniformly random samples. Figure shows the learned pol- icy for self-influence buckets, where we see that the model uses all buckets, initially with more emphasis on harder buckets (top 30% of highly influential examples) and follows up with using more the top 10% and most of the lowest influence bucket. Commencing with highly influential exam- ples is reasonable because, given the lack of noise, they are presumably difficult examples with high impact on accuracy, which need be memorized.
M
M
M
p(w;)
Dnt myy?
(1)
Niotal
where indexes unique words in the corpus. These are aggregated using the logarithm of word probabilities to prevent numerical errors:
Config
Method
EM
Fl
N
drarity (siity((s)))))))
log pl (wi)
(2)
Baseline Filtered 10%
78.08
80.17
ABIF
77.49
79.72
AutoCL bins
ABIF
78.13
80.29
Context-question lexical overlap: Sugawara al. (2018) showed that questions with low lexical overlap with the context tend to require reasoning skills compared to superficial word matching, and showed the models have worse performance on the subset where the answer was not present in the most similar sentence to the question. We propose of tokens between context and question after re- moving the stop words and normalizing by the number of remaining tokens:
Table 7: Filtering performance and AutoCI. on TriviaQA (very clean dataset) using ABIF self-influence scores.
E Other difficulty signals for AutoCL
In $5, AutoCL delivered important gains NQ (+1.2 driven by self-influence ranking as sig- nal. However, one might wonder if the improve- ments could be attainable by AutoCL with much simpler example difficulty proxies for splitting the data into buckets.
metric the overlap
Ano-sw Ne
d(qno-sw c)
(3)
Setup. We propose suite of heuristics, inspired by prior work, split the training data in fixed number of buckets, as we did with the self- influence ranking, and explore a variety of hyper- parameters configurations, using the LongT5-base model on NQ and TriviaQA. We train the same
Qno-sw
where Ino-sw denotes question from which stop words have been removed.
Question type: Gardner et al. (2019) argued that question answering encompasses a broad range

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of conceptual tasks which are posed as questions (i.e. fitting the question format), but do not resem- ble a cohesive task. Rogers et al. (2023) grouped these tasks into three categories, with an increas- ing degree of difficulty based on easiness to re- place the questions in dataset with content-free identifiers: classification (What is the sentiment of (X]?), slot-fillingge Aitiiilling/teemplae with no meaning- ful question understanding (When was [Person] bom?? and open-ended questions.
H30
66
2008
200K
Figure 4: LongTS-base performance trained individu- ally on each self-influence bin, from B1 (bottom-105% to B10 (top-10%)) of the NQ dataset.
We capitalize on that distinction for automation purposes and adapt it to the NQ-specific query types. We consider the top-N type of questions with a minimum representation of 5% of the data given by the first token of each sentence ('who', "what', 'when', 'where", 'how", 'which', 'will") and place remaining training examples in the category other, which should be highly diverse and contain many redee--epresenned examples.
and may dynamically up- down-weigh buckets depending on the model's current needs, overcom- ing the rigidness of fixed threshold filtering.
G Hyperparameters of AutoCL runs
Here, we report the hyperparameters of the bandit- based AutoCL in Table 9. All experiments used the fixed bandit learning rate of 0.001 and the explo- ration rate of 0.01. We tuned between two bandit algorithms (EXP3 and EXP3S) and two rewards function (pgnorm vs. cosine similarity between gra- dients of the train and the reward batch).
Signal
Algorithm backets Reward EM
F1
Narunal Questions
Baseline
58.10
62.65
Length
EXP3
5
Pgncer
57.79
62.88
Longth
EXP3S
10
cosine
58.03
62.69
Word rarity
EXP3
5
Pgnorr
57.82
62.52
Word rarit
EXP3S
57.56 62.65
Lexical overlap
EXP3
5
Pgnorr
58.10
62.82
Question type
EXPS
penoor
58.02
62.66
TriviIQA
Baseline
78,08 80.17
We found that for Paracrawl (very noisy dataset), the EXP3's design goal of minimizing the regret over the whole history by finding a single best arm (Auer et al., 2002) biases the bandit to commit fully to playing the current best arms (lowest influ- ence buckets in Figure 2) and it does not reconsider noisy buckets which may decrease performance. However, for datasets where the highly influential buckets prove valuable and generally have a lower level of noise, saw that EXP3's piecewise sta- tionary behavior makes better use of all data and brings more consistent gains.
Length
EXP3
cosine 76.14 78.30
Word rarity
EXPS
77.92 80.00
Lexical overlap
EXP3
pgrcrr 77.37 80.01
Table 8: Results on LongT5-base using AutoCL on a variety feature splits on NQ and TriviaQA.
Results. In Table regardless of the signal cho- sen for the split, none of the methods strongly im- proves on the base model performance. This shows that self-influence scores are not only a generaliz- able metric to be used in junction with curriculum learning methods, but are also more informative than trivial, NLP-specific, difficulty metrics.
Hyperparameters
Run
Method
Algorithm
Reward
F Training on different buckets in isolation
Pusscraw
AutoCL. 5 bins
ABIF
EXP3
cosine
AutoCL- - 10 bins
ABIF
EXP3
cosine
AutoCL- - 25 bins
ABIF
EXP3
pgnorm
Here, we validate how different the quality signal is from each individual self-influence bucket. In Figure 4, we see that the performance of the highest self-influence bucket is much lower (although not zero) compared the rest of buckets, which are in turn more difficult separate, possibly, because they contain data of different grades of usefulness. A positive aspect of AutoCL is that, due to explo- ration, the model will regularly visit all buckets,
AutoCL- - 25 bins Traclo(1)
EXP3
pgnorm
AutoCI bins
ABIF
EXP3S
pgnarn
AutaCI- bins
ABIF
EXP3S
Pgnarn
AutoCL bins
TracIn(3)
EXP3S
pgnorm
AutoCI. 10 hins
ABIF ABIF
EXP3S EXP3S
cuxine
AutoCI - 25 bins
cosine
Table 9: Hyperparameters for the reported AutoCL re- sults in Table 5 in $55 found with grid search. The num- ber of checkpoints, C, for TracIn is in brackets.


Reviews:
[{"review_id": "6wH6pSCrmE", "reviewer": "Reviewer_Bjvq", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your comments. \n\n1. Please note that w.r.t. the stability analysis, our contribution is that, to the best of our knowledge, we are the first to do such an analysis for self-influence scores, despite the IFs being used for filtering since 2017. As the results in Table 2 demonstrate, self-influence scores, unlike influence scores ([K & Sogaard, 2021](https://arxiv.org/abs/2111.04683)), are much more stable when the commonly modified *training* hyperparameters (batch size, data order, initialization) are used. However, when it comes to *model* parameters (Table 3), there is a substantial drop in stability. These are novel results and we believe they shed light on how to actually interpret highly self-influential examples: given these results, simply calling them “noise”, like done in prior work, is insufficient as they are apparently related to model type and capacity. \n\n2. This is a serious misunderstanding: the missing values are either not possible to calculate for task/metric combinations in question or are not used in prior work (e.g. BLEU is not defined for toxicity classification etc.; likewise, F1 is not defined on Paracrawl and so on). For the given tasks and metrics, we provide all the results that apply and are required for comparison with prior work. We had to lump them into one table to save space and will split Table 4 into multiple tables to avoid the impression that the values were omitted.\n\n3. Thank you for the pointers. Please note that we do cite two of the works mentioned – AUM (Pleiss et al. 2020) and dataset cartography (Swayamdipta et al., 2020) in the Related Work section as prior developments for dataset error detection that use training data dynamics. However, those methods are designed primarily for *classification* tasks, while in our work we mostly focus on seq2seq tasks (except for toxicity). Seq2seq tasks require beam search that would complicate calculation and require an adaptation of the mentioned methods. We thus opt for a task-agnostic method that can generalize across NLP tasks and investigate its properties as a prerequisite to use it effectively with AutoCL.  \nEven more importantly, please note that we are not proposing a new *filtering* method – we fill the gaps in the analysis of an existing method and propose to go beyond filtering with AutoCL. That is, our main point should stand regardless of the filtering baseline: AutoCL should improve by using useful bits of information from the to-be-discarded examples and, in the least favorable case, AutoCL (given access to the buckets based on the same filtering scores) should agree with the threshold-based filtering.\n\n4. Thank you, we will do our best to revise the style and content order.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The idea of using a self-influence score to identify outliers or harmful samples is inspiring. If removing a sample deteriorates the loss value on itself, then this sample should be different enough from the rest of the training data. Such samples could be mislabeled, ambiguous, or difficult samples, or out-of-distribution samples.\n2. Automated filtering using bandit curriculum learning seems effective and avoids manually tuning the hyperparameters of the previous fixed threshold.\n3. Both the stability and efficacy of self-influence are analyzed on diverse datasets, tasks, and models, demonstrating the generality of the proposed method.", "reasons_to_reject": "1. The claim that the proposed self-influence score is stable with respect to training and model hyperparameters across architecture variations is not actually supported by the evidence in Table 2 and Table 3. It can be observed that the self-influence score varies greatly for different model architectures and training hyperparameters.\n2. The effectiveness of the proposed self-influence score is doubtful. When demonstrating the efficacy of the self-influence score in Table 4, nearly half of the results are missing and replaced with '-'. This may raise doubts that only favorable results are picked, especially for the out-of-distribution test. It would be appreciated if the author could provide those missing results and justify why they were missed in the original table.\n3. There is no comparison with other data filtering techniques, such as AUM, Data Cartography, or PVI. How does the performance compare with those existing data filtering techniques?\n4. I agree with the other two reviewers that the paper is hard to read. A better presentation style and more clarity would be appreciated. Specific suggestions were well given by the other two reviewers.\n\nR[1] Pleiss, Geoff, Tianyi Zhang, Ethan Elenberg, and Kilian Q. Weinberger. \"Identifying mislabeled data using the area under the margin ranking.\" *Advances in Neural Information Processing Systems* 33 (2020): 17044-17056.\n\nR[2] Swayamdipta, Swabha, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. \"Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics.\" In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9275-9293. 2020.\n\nR[3] Ethayarajh, Kawin, Yejin Choi, and Swabha Swayamdipta. \"Understanding Dataset Difficulty with $\\mathcalV $-Usable Information.\" In International Conference on Machine Learning, pp. 5988-6008. PMLR, 2022.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper focuses on identifying noisy data/outliers and filtering out harmful instances to improve model performance. The author proposes using self-influence scores for data filtering and incorporating bandit curriculum learning to automate the filtering process instead of using a fixed filtering threshold. The stability and effectiveness of the self-influence scores are analyzed.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Hosf0hdEW6", "reviewer": "Reviewer_SJBj", "summary": "", "questions": "* Do you think high self-influence data instances always harmful for models? I am thinking of large language models --- one would need LLMs to memorize certain facts to give correct answers; in this case, such memorization/high self-influence can become an advantage? \n* Do you have any hypothesis why it is this case, that first/all are less sensitive to size/attention, but more sensitive to model states?\n* What are the fundamental difference between synthetic and natural noises that leads to the successful filtering only on synthetic data?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments and questions.\n\n1. Experiments:  \n    * Thank you for pointing out the confusing line, we will reword this part to be unambiguous that in this experiment we modified only the three mentioned hyperparameters (batch size, data ordering seed and initialization seed) and the rest of hyperparameters remain the same between the two runs. The specific choice of the three hyperparameters was driven by prior work and them being the most susceptible to modification during retraining after a round of data cleaning.   \n    * In all our tasks, the filtering performance decreased monotonically outside the optimal threshold thus we approached the search for it similarly to binary search, with a granularity of minimum 5%, and reported the nearby percentages to illustrate the performance trend and its decrease speed.\n\n2. Background:\n    * You are right, the HVP method was proposed by [Pearlmutter (1984)](https://core.ac.uk/download/pdf/297017473.pdf). [Koh & Liang (2017)](https://arxiv.org/abs/1703.04730) proposed to use it and LISSA for their IF approximation. We will add the Pearlmutter citation to the text.\n    * Thanks for pointing this out - we will add more examples/explanations earlier.\n    * Thanks again - we will explain missing notation and terms, which fell prey to our draft shortening. The gradient and rewards batches are two regular SGD mini-batches used to calculate, respectively, the network update gradient and bandit rewards. In principle, they could be the same but ([Kreutzer et al., 2021](https://arxiv.org/abs/2110.06997)) found that drawing the reward batch from a held out set works better.\n\n3. Results: Please note that the natural noise experiment is done on NQ which is a low noise, human curated, dataset. There, indeed, self-influence fails to cleanly capture the natural (~human) noise and improvements from threshold filtering are absent (Table 4, first line). AutoCL though, because it’s able to use all the data, managed to slightly improve over that. In contrast, datasets like Paracrawl and Toxicity are crawled and labeled with heuristics having different (and likely systematic) noise patterns than the “human noise”, and hence are easier tasks for any filtering and even more so for AutoCL. Overall, we believe our analysis and the conclusions we draw from it will make the present discussion around data noise and the role of filtering more fine-grained and as a result more insightful. \n\nQuestions:\n1. We agree, and in fact that was one of the motivations for AutoCL – throwing away a datum just because it looks like an outlier doesn’t sound right and the network should be given a chance to learn from it, in particular because it is likely to come from an underrepresented population. AutoCL detects, from our experiments, what data has most utility at each training step - and in a few cases, using the high self-influence examples provided the most utility, while low self-influence ones were likely learned very quickly.\n\n2. While this is true, it is good to note that the sensitivity to model states across all layer settings is significantly smaller than to size/architecture (relative change in 90th $\\cap$ is 12% vs. >30%). We haven’t explored the differences between the layers in detail, but we believe the situation you describe could be due to the “cancellation effects” ([Yeh et al., 2022](https://arxiv.org/abs/2202.11844)), where different examples share logic in the last layers and lead to a weaker discriminative power of the influence scores calculated via them. In contrast, the first/all layers cover low-level processing and word embeddings, permitting examples to reflect their own unique logic in the IF scores and be less susceptible to the cancellation effect. The bedrock changes in size or architecture could amplify the cancellation effect when first layers are not included and hence the overlap drops relatively by over 30% when comparing to first/all. The less foundational changes in model states, and in this case aided by the low prediction churn, happen to have an opposite effect but the difference is only ~12%.\n\n3. We think the bulk of the difference in filtering performance could be explained by the ease of detecting some of synthetic noise – e.g. unrelated target or distantly wrong label – while natural noise is more subtle and ambiguous even for human annotators. For NQ, which is a well-curated dataset, due to such subtleties, the agreement between expert’s annotations of non-null answers and the consensus reached by the 5-way annotations were low: Figure 4 in the [NQ paper](https://aclanthology.org/Q19-1026) shows that debatable answers feature prominently in every annotator bin; according to Table 2 there, only <60% of answers are unambiguously correct after reannotation; finally, their Table 3 evaluates short-answer agreement precision/recall at only 63.4/52.6. In summary (and eyeballing the [released 5-way annotations](https://ai.google.com/research/NaturalQuestions/download)), many noisy examples are narrowly missing the correct answers (e.g. in terms of minimal correction to them) which requires common sense, cultural understanding and a certain degree of pedantism to spot.", "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": "* It is an interesting idea to use self-influence score to filter data. \n* Understanding instability is important for such filtering to put into practice. \n* Using AutoCL instead of filtering is intriging, and the performance shown is encouraging.", "reasons_to_reject": "* The experiment setups are not well-explained, and thus not reproducible/convincing\n    * Lines 360-362 should be more specific: what are the specific choices of hyper-parameters (for reproducibility)? How would that change performance (to better ground the stability)? \n    * In table 4, how did the authors choose different percentage of data to filter out? \n\n\n* The writing of the background/methods is hard to follow, important points include: \n    * Is the IF approximation method proposed by Koh and Liang (2017)? I think the HVP method was proposed quite early.\n    * Without reading Section 7, it is hard to understand what are the different subsets of data in AutoCL\n    * Math notation system in AutoCL is used without introduction: what are y/Y? Also, in pgnorm (I think \\mathcal L is loss?), on which data the loss should be computed; and what are the gradient and reward batch?  \n* The results of synthetic noises vs. natural noises detection confuse me: on the one hand, data filtering based on self-influence improved OOD performance (Table 4); however, on the other hand, such filtering failed to filter out natural noises (Figure 1) — what are the implications then? Reading the full results, I feel some pieces of evidence are missing here: self-influence scores can help filter out noisy data and help with AutoCL, however, it cannot really detect natural noises, which is a bit puzzling.", "questions_to_authors": "* Do you think high self-influence data instances always harmful for models? I am thinking of large language models --- one would need LLMs to memorize certain facts to give correct answers; in this case, such memorization/high self-influence can become an advantage? \n* Do you have any hypothesis why it is this case, that first/all are less sensitive to size/attention, but more sensitive to model states?\n* What are the fundamental difference between synthetic and natural noises that leads to the successful filtering only on synthetic data?", "paper_topic_and_main_contributions": "The paper studied whether self-influence scores based on Arnoldi-based Inﬂuence Function (ABIF) can be used to filter out low-quality data (outliers, e.g. noises) so as to improve the test performance. Specifically, the authors explored 1) the stability of computed self-influence scores against different a) model states (i.e. initialization, data ordering, and batch size) and b) model size and architecture, 2) whether self-influence scores can be used to select noises, and 3) whether one can use self-influence scores to divide datasets into different subsets, and then use Automated Curriculum Learning (AutoCL) to improve training performance. The results show that 1) self-influence scores are more robust against model states than sizes and architectures, 2) self-influence scores are better at capturing synthetic data noises than natural noises, and 3) AutoCL help learning better than filtering, based on self-influence scores.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Second-Order Stochastic Optimization for Machine Learning in Linear Time, Agarwal et al., 2017.", "Typos_Grammar_Style_and_Presentation_Improvements": "A bit strange to mention a section (5.3) and put everything in the appendix. It would be better give some more information in the main text for the purpose of being self-contained."}, "extra_text": {}}, {"review_id": "oqVy0moOXf", "reviewer": "Reviewer_2t6t", "summary": "", "questions": "Question 1: Are you going to release the experimental setup to the public?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your high evaluation of the work and thank you for the constructive suggestions! \n\n1. Statistical test: Yes, experiments require costly full retraining plus rerunning the IF analysis on the full training data. On top of that, the stability metrics – 90th overlap and Spearman correlation – are easily applicable only to pairs of runs. Time permitting, we will generate more pairs and report the variance of the results.  \n2. Content arrangement: We will try to rearrange the introduction, explain IF earlier, and move the related work to the main body of the paper if it’s accepted. Thank you!\n3. Open-sourcing: Yes, we are looking into it, although cleaning up and figuring out licensing may delay it past the conference date.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The contribution seems novel, the experiments are (for the most part) well executed and motivated, the research questions are clear and the results support the findings.\n\nI appreciate the fact that some of the less well-known techniques, metrics, etc by examples.", "reasons_to_reject": "I have two concerns with this paper.\n\nFirstly, I don't think the results regarding the stability of influence functions would withstand a statistical test - only two results are being compared here (although for different settings). Perhaps the chosen different initialisation/model happened to produce a similar result by chance. I believe for more robust conclusions, the sample set of observations should be more than a pair. I appreciate that these experiments are computationally expensive, perhaps a bigger sample could be investigated for a subset of the investigated settings (e.g. 'first' only in Table 2)\n\nSecondly, I find the paper's content arrangement rather odd, see my comments in the presentation section. But this is only a minor concern and could be addressed by an additional page of content, should the paper be accepted.", "questions_to_authors": "Question 1: Are you going to release the experimental setup to the public?", "paper_topic_and_main_contributions": "This paper investigates the impact of influence-functions in the context of identifying self-influential examples (i.e. examples that are difficult to predict from other examples and are thus hypothesised to be memorised). The paper investigates properties of such self-influential examples, suggesting that they are properties of data rather than dependant on model architecture and initialisation. Finally, the paper shows how knowledge about such examples can be used to improve training and out-of-distribution generalisation of models.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None that I'm aware of."}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "I find it odd to move the related work section into the appendix, while spending a whole page on Sections 2 and 3 and 4.1, which are essentially background. Sections 3 and 4.1 could definitely be moved to the appendix, since they're already summarised in Table 1.\n\nFurthermore, the introduction set's the scene rather awkwardly. I believe the concept of self-influence should be (at least intuitively) introduced earlier and followed up by the research questions this paper is addressing. I am not sure why the paragraphs in lines 64 to 96 are so prominent."}}]

Author rebuttal:

Meta-review: This paper studies the stability of self-influence scores, which determine memorizability and are task-agnostic, to training and model hyperparameters. Not only does self-influence have intrinsic properties that makes it desirable, the paper also aims to determine its capability towards cleaning noisy NLP datasets by removing outliers that have high self-influence.

The reviewers like the novelty of the idea and found it interesting, the application strongly motivated. They also commended that the AutoCL method is great.

There were concerns about the empirical evidence, raised by the most critical reviewers. I believe the authors’ rebuttal adequately addressed the concerns. There were some other questions about synthetic / natural noise which also seem addressed. The most critical reviewer also was convinced by the author rebuttal, which clarified some of the misunderstanding about the empirical evidence. Overall, this idea seems to be fairly unique and novel and exciting, as reflected in the reviews.
Decision: Accept-Main