Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets (2023)

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.

## introduction deep learning on increasingly larger and diverse data sources brought impressive advances in natural language processing (nlp), however, data quality might be the major bottleneck to unlock further gains @xcite . nlp data are usually acquired via large-scale weakly-labeled data scraping or crowd-sourcing labels from non-expert human annotators, which are both error-prone @xcite . at the same time, ambiguous training data are also known to hurt models' performance through overfitting or memorization in overparameterized networks @xcite . finally, not all data are equally easy to learn and overly complex instances may hinder learning as well. below, we refer to all of those cases -label noise, out-of-distribution, ambiguous or difficultto-learn examples -by an umbrella term outliers. two key questions of our work are: how can outliers be detected and how should they be dealt with? * the work was done while interning at google. ## stability of self-influence in this section, we evaluate the stability of selfinfluence scores with respect to model states, architecture and abif-specific 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 alternative to global correlation since one normally cares only about highly self-influential examples. it is important to understand whether selfinfluence ranking, given that it is thought to be predictive of data quality, is an inherent data property or it is mainly rooted in the architecture. in this regard, we look at the extent to which model stability and self-influence stability are interconnected, via the model churn metric @xcite , 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 on the 10% of the examples that a gets wrong, the churn is 19%. while changing weight initialization does not always impact accuracy, it can result in a net zero wins and losses, referred to as unnecessary churn. ## effectiveness of self-influence scores the impact of filtering highly self-influential examples on the downstream performance and the recall of synthetically perturbed training samples, have been used to measure the correctness of ifs @xcite , given that the ground-truth estimate via leave-one-out is unfeasible to compute even for medium-sized models. we ask whether filtering of highly self-influential ## automated filtering of outliers as we showed above, using self-influence to filter noisy training data has the drawback of the difficulty of choosing a right cut-off threshold. yet, trial-and-error search for a fixed threshold based on the downstream performance remains popular, which is costly and does not generalize across 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 committing to completely remove any of them. first, we validate how different the quality signal is from each individual self-influence bucket on nq. we found (figure 4 in §f) that em of the highest self-influence bucket is much lower (although not zero) compared to 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 dynamically up-or down-weight buckets depending on the model's current needs, overcoming the rigidness of a fixed threshold filtering. 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 consistent across three datasets (nq, paracrawl and toxicity on t5). bandit actions are mapped to equal-sized data buckets corresponding to a predefined 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 bandit not to use much of the high self-influential buckets, nor the lowest bucket, which prior work found to be less useful because of its simplicity @xcite . following @xcite , we report bleu for paracrawl at 10k and 200k steps. as baselines we use the filtering methods from §4. see §g for autocl hyperparameters of reported results. results. from we check if the multi-armed bandit's learned policy (probabilities of choosing a 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 undertake redundant switches between neighboring buckets 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%, instead training on 2 /3 of time on the bottom-4%/8% and only 1 /3 of time from the top-4% which corresponds to the lowest influence and is known to be less useful to the model @xcite . for nq, there is more of bucket switching, and the 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 to the filtering. as triviaqa is a very clean human-curated dataset, filtering does not improve over baseline, and autocl brings only nominal insignificant gains (see §d). finally, one might wonder if autocl improvements are due to self-influence or due to the increased data scheduling flexibility, i.e., if a simpler example difficulty signal would suffice to attain similar gains. in §e, we run autocl on nq/triviaqa buckets defined via difficulty signals 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. ## conclusion we proposed a general method for improving model performance in the presence of noisy training data based on self-influence and bandit curriculum learning, and without relying on threshold filtering. we showed that arnoldi iteration based self-influence scores of training instances are stable with respect to varying training hyperparameters 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 generalizing threshold filtering to a dynamic data reweighing. finally, we showed that dynamically reweighing based on multi-armed bandits, which pick selfinfluence bins to sample batches from, outperforms threshold filtering on noisy and clean datasets.