Difficulty Estimation in Natural Language Tasks with Action Scores (2025)

Abstract: This study investigates the effectiveness of the action score, a metric originally developed for computer vision tasks, in estimating sample difficulty across various natural language processing (NLP) tasks. Using transformer-based models, the action score is applied to sentiment analysis, natural language inference, and abstractive text summarization. The results demonstrate that the action score can effectively identify challenging samples in sentiment analysis and natural language inference, often capturing difficult instances that are missed by more established metrics like entropy. However, the effectiveness of the action score appears to be task-dependent, as evidenced by its performance in the abstractive text summarization task, where it exhibits a nearly linear relationship with entropy. The findings suggest that the action score can provide valuable insights into the characteristics of challenging samples in NLP tasks, particularly in classification settings. However, its application should be carefully considered in the context of each specific task and in light of emerging research on the potential value of hard samples in machine learning.

## introduction while contemporary artificial intelligence (ai) algorithms can be successfully applied to a range of tasks, they need vast amounts of data to be trained on. one of the inevitable problems with such ai systems is the biases @xcite they often exhibit. large datasets can contain inherent biases that get amplified when used to train ai models. they often stem from skewed or unrepresentative training data and can result in models misinterpreting or struggling with certain samples. another substantial issue when dealing with large amounts of data is the expense and sometimes the inability to fact-check the correctness of every data sample @xcite , which amplifies the tendency of large-scale datasets to have hard sample, @xmath0.06, @xmath1.01 i feel that he was being overshadowed by the supporting characters easy sample, @xmath2.01, @xmath3.005 i feel reassured that if something happened to me my guests would be able to easily get the help they need a significant portion of their examples wrongly labeled. one way of observing both biased and incongruous samples is by analyzing whether they comply with the optimization dynamics. therefore, it is crucial to have a systematized, robust, and model-agnostic way to pinpoint such samples and observe how exactly they influence the model's performance. in this regard, the metric that this study will explore is called action score, which can be categorized as part of the tools available for understanding model dynamics through the lens of individual samples. this metric has been extensively studied and applied across various computer vision tasks, demonstrating its relevance in assessing model behavior @xcite . however, its potential usefulness in the domain of natural language processing (nlp) has yet to be explored. this study aims to fill this gap by applying the metric to sentiment analysis, natural language inference and abstractive text summarization. tables 2a , 2b and 2c show examples of difficult and easy samples (high and low action scores accordingly) from each of the tasks. the main research question in this paper is: can the action score measure difficulty in natural language processing tasks? text label action score hard i as representative of everything thats wrong with corporate america and feel that sending him to washington is a ludicrous idea surprise 29.0019 easy i feel reassured when i listen to waldmans songs joy 0.0048 (a) sentiment analysis premise hypothesis label action score hard i read everything i could get my hands on. there was nothing i couldn't get my hands on. neutral 41.1284 easy she remembered poems that she had learned when she was in high school. she didn't remember any poems. contra 0.0166 (b) natural language inference dialogue label summary action score hard sophie: whats for dinner mom?olivia: tacos and burritos sophie: wowwww! my favorite please keep it ready will be home in 20 mins olivia: all is ready dear!! sophie is coming home in 20 minutes for the dinner olivia, her mother, prepared. 19.8895 easy mattie: will you call me when dad is at home? ross: sure mattie: ty :* ross will call mattie when dad is at home. 0.5993 (c) abstractive text summarization the existing metrics are either model or taskspecific. furthermore, the majority of those metrics are too computationally expensive and/or require model architecture modifications. the action score is calculated as the accumulated loss over all epochs for each individual sample. the metric utilizes a single parameter that can be relatively easily obtained. in essence, the main contribution of the action score lies in its effective application to a variety of different architectures with minimal additional modifications. the contributions of this paper are the experimental validation that the action score measures sample difficulty in three nlp tasks: sentiment analysis, natural language inference, and abstractive text summarization. this study hypothesizes that the action score, originally developed for computer vision tasks, can be effectively applied to nlp tasks to measure the difficulty of individual samples. it is expected that samples with higher action scores will correspond to more challenging linguistic inputs, such as complex sentence structures, rare words, or ambiguous meanings, while lower action scores will be associated with simpler, more straightforward language samples. the effectiveness of the action score in nlp will be evaluated by comparing it to established difficulty metrics like the predicted entropy and through qualitative analysis of high and lowscoring samples. ## state of the art an important aspect of model evaluation that is often overlooked is the varying difficulty of samples within a dataset. an interesting work @xcite on dataset cartography, identifying three distinct regions in datasets: easyto-learn samples, hard-to-learn samples, and am-biguous samples. this categorization reveals that the difficulty of samples is not binary but exists on a continuum. traditional evaluation metrics often fail to capture these nuances, potentially leading to an incomplete understanding of model performance. an intriguing work @xcite has utilized the insights we can obtain from the model's training dynamics -in a broad sense, this is the model's behavior during the training process -as a potential avenue for developing more generalizable metrics. the area under the margin (aum) metric @xcite shows promise in identifying mislabeled instances in classification datasets, but its effectiveness in filtering nlp datasets has been questioned @xcite . although the aum metric can successfully find mislabeled samples, it also removes a significant amount of correctly labeled samples, which results in the loss of a large amount of relevant information. interpreting model behavior through the lens of training dynamics allows us to gain insights into both the nature of the dataset and the model's learning process. samples that are consistently classified correctly with high confidence throughout training likely represent "easy" instances, while samples where the model's predictions fluctuate greatly may represent ambiguous or challenging instances. samples that are consistently misclassified, even late in training, may represent very difficult instances or potentially mislabeled data. several difficulty metrics have been studied for particular nlp tasks. @xcite ) performed a large-scale evaluation of summarization datasets, introducing 5 intrinsic metrics and applying them to 10 popular datasets. their findings highlight that data usage in recent summarization research is sometimes inconsistent with the underlying properties of the datasets employed. they also discovered that their metrics can serve as inexpensive heuristics for detecting generically low-quality examples. in the context of text classification, a study @xcite identifies difficult samples by analyzing data inputs in the semantic embedding space. the method proves to be an effective way to find difficult samples in 13 datasets. by removing them, trained models achieve better f1 scores (up to 9%). despite these efforts, the ai research community has yet to develop a truly universal metric that can be applied without major model modifications, that is model-and task-agnostic and that does not add a significant computational overhead. ## action score for nlp tasks the action score @xcite is a novel metric designed to quantify the difficulty of individual samples in machine learning tasks. it is based on the principle that samples that do not conform to the optimization dynamics of a model can be considered unnatural or difficult. the action score is calculated by accumulating the loss of each sample overall validation (or training) epochs, resulting in a single scalar value that represents the sample's difficulty. this approach is model-agnostic and can be applied to a wide range of tasks without requiring modifications to the underlying model architecture. the action score is defined as ## experimental setup to investigate the effectiveness of the action score in estimating sample difficulty in natural language processing (nlp) tasks, a systematic methodology was employed, involving the following steps: bert calculate the cross-entropy loss every epoch | h sadness anger fear surprise joy love target class distribution | p* predicted class distribution | p sadness anger fear surprise joy love validation split sample tokenizer i got a very nasty electrical shock when i was tampering with some electrical appliances 1 } 2 3 4 5 6 3.4713 3.5401 6.5898 34.7912 7.7355 7.1971 7.2575 calculate the action score as the accumulated loss every epoch in complexity, allowing for a comprehensive evaluation of the action score's performance. ## conclusions and future work this study has explored the effectiveness of the action score, a metric originally developed for computer vision tasks, in estimating sample difficulty across various natural language processing tasks. the results demonstrate that the action score can provide valuable insights into the characteristics of challenging samples in sentiment analysis and natural language inference, often identifying difficult instances that are missed by other metrics like entropy. however, the effectiveness of the action score appears to be task-dependent, as evidenced by its performance in the abstractive text summarization task. this finding underscores the importance of considering the unique properties of each nlp task when applying difficulty estimation metrics.