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Time Waits for No One! Analysis and Challenges of Temporal Misalignment

Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Karishma Mandyam, Noah A. Smith

arXiv:2111.07408v2cs.CL

TL;DR

Temporal misalignment between training and evaluation data can degrade NLP performance, but its effects across tasks and domains remain insufficiently understood. The paper benchmarks eight timestamped tasks across four domains and evaluates pretrained models with optional temporal adaptation and task finetuning. It finds stronger and more variable degradation than previously reported, while continued pretraining helps less than finetuning on temporally aligned labeled data.

  • Problem

    The paper addresses limited understanding of how temporal misalignment affects long-term NLP system performance across tasks and domains.

  • Method

    The study evaluates eight tasks across four domains and uses the TD score to summarize performance degradation as training and evaluation periods diverge.

  • Results

    Temporal degradation varies substantially by task, reaching 40 F1 points over five years for political affiliation and only 1 point for Yelp ratings.

  • Takeaways & Limitations

    Temporal misalignment should be considered in NLP benchmarks and monitored in deployed systems, while temporal adaptation should not substitute for temporally aligned labeled data.

  • Takeaways & Limitations

    The study selects periods with sufficient and consistent training-data sizes, although domain and task data amounts and language-change rates may fluctuate over time.

Abstract

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When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment. Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domain-specific pretraining, followed by task-specific finetuning. We establish a suite of tasks across multiple domains to study temporal misalignment in modern NLP systems. We find stronger effects of temporal misalignment on task performance than have been previously reported. We also find that, while temporal adaptation through continued pretraining can help, these gains are small compared to task-specific finetuning on data from the target time period. Our findings motivate continued research to improve temporal robustness of NLP models.

1 Introduction

Temporal misalignment between training and evaluation data is understudied despite changing language use, and this paper measures its effects across diverse NLP tasks and domains. The results show substantial, task- and domain-dependent degradation, while temporal adaptation offers limited benefit relative to temporally aligned task finetuning.

  • Over five years, classifier F1 deteriorates by as much as 40 points for political affiliation on Twitter and as little as 1 point for Yelp review ratings.
  • Temporal misalignment affects both language-model generalization and downstream task performance, with degradation varying considerably across domains and tasks.
  • Temporal adaptation through continued pretraining provides limited benefit compared with finetuning on labeled data from the test period.
  • The benchmark motivates greater attention to temporal misalignment in NLP evaluation and further research on temporal robustness.
  • The paper compiles eight tasks across four domains and introduces an interpretable metric summarizing how task performance degrades with time.

2 Methodology Overview

The study evaluates temporal misalignment in a pretrained-model pipeline using timestamped tasks across multiple domains, controlling training-period data size and summarizing degradation with the TD score. It covers eight tasks and four domains, including social media, scientific articles, news, and reviews.

  • 2.1 Learning Pipeline: The pipeline pretrains a language model, optionally applies domain-adaptive continued training, and then finetunes it for a downstream task.
  • 2.1 Learning Pipeline: Temporal misalignment can arise from task shift in annotated data or language shift affecting the pretrained model, and the study does not disentangle these effects.
  • 2.2 Experimental Design: For each task, the study fixes a test period, varies the training period, and keeps training partitions roughly equal in size to estimate performance regret from temporal misalignment.
  • 2.3 Quantifying Temporal Degradation: The TD score is the average slope from regressing modified performance differences against training-time displacement, with higher scores indicating faster deterioration.
  • 2.4 Domains, Tasks, and Datasets: Three of the eight tasks are newly defined and all tasks require nontrivial postprocessing.
  • 2.4 Domains, Tasks, and Datasets: The benchmark spans eight tasks across four domains, including publisher and review classification, Twitter tasks, and scientific and news datasets.

3 Empirical Results and Analysis

Across eight tasks, temporal misalignment substantially affects performance, but its severity varies by task, domain, and direction of time shift. Temporal adaptation offers limited relief, whereas finetuning on temporally updated labeled data is more effective.

  • 3.1 Temporal Misalignment in Tasks: Label distributions themselves drift over time, including a reversal from Republican-majority to Democratic-majority tweets in POLIAFF between 2015 and 2020.Notable label-distribution drift was also detected for AIC and MFC.
  • 3.1 Temporal Misalignment in Tasks: Temporal misalignment substantially degrades task performance, with the largest effects for POLIAFF (TD=7.72) and PUBCLS (TD=5.45).Half of the tasks lose at least 1 point per period of training–evaluation divergence.
  • 3.1 Temporal Misalignment in Tasks: Temporal performance loss occurs in both directions, affecting models trained on newer data when evaluated on older texts as well as models trained on older data.The deterioration can emerge after only a few years rather than only across decades or centuries.
  • 3.1 Temporal Misalignment in Tasks: Performance degradation varies sharply across tasks: POLIAFF and TWIERC have TD scores of 7.72 and 0.96, while YELPCLS is among the most robust at 0.26.Tasks in the same domain can therefore exhibit very different temporal sensitivity.
  • 3.2 Temporal Misalignment in LMs: Language-model degradation differs by domain: Twitter changes most rapidly, whereas food reviews change much more slowly.The experiments report considerable variation in temporal sensitivity across text domains.
  • 3.2 Temporal Misalignment in LMs: Temporal domain adaptation provides only small gains and can sometimes hurt, while finetuning on temporally updated labeled data is more effective.Adjusting the language model alone does not sufficiently mitigate misalignment from labeled data.

4 Limitations and Future Work

The study’s controlled setup has important scope and data-selection limitations, while several future directions could improve temporal robustness and reduce annotation costs.

  • Limitations: The benchmark’s controlled setup may not reflect models trained on data accumulated across multiple time periods, which the authors expect could perform well.This is identified as a drawback of the study’s setup.
  • Limitations: Time periods were selected for sufficient, consistent training sizes, although real domains and tasks fluctuate in data volume and language-change rates.The authors recommend considering both factors when selecting periods.
  • Future Work: Temporal misalignment may also affect annotation efforts, making labels anachronistic when time-sensitive constructs such as controversy or social norms change.The authors identify this as an unstudied extension of their findings.
  • Future Work: Future work could study sudden changes from events such as COVID-19 or political shifts, which may influence tasks including question answering.These settings are outside the paper’s current exploration.
  • Future Work: More sophisticated domain modeling and continual-learning methods could seek stronger temporal robustness than straightforward adaptation provides.The paper points to domain mixtures, hierarchical domains, and task-oriented continual learning as directions.
  • Future Work: Because updated labeled data can be expensive, future dataset maintenance could balance annotation costs against expected performance gains.The authors also suggest characterizing or detecting task changes to update datasets efficiently.

5 Conclusion

The paper shows that language change over time affects NLP performance and contributes a benchmark and TD score for studying temporal misalignment. Temporal degradation varies substantially across tasks, while continued language-model training on aligned data has limited effect compared with the need for more effective adaptation methods.

  • Conclusion: The paper compiles timestamped datasets into a benchmark and introduces TD score to compare models by their temporal misalignment.These resources summarize and facilitate evaluation of temporal robustness.
  • Conclusion: Temporal degradation varies considerably across tasks, more than previously reported, motivating temporal-misalignment checks in benchmarks and monitoring of live systems.The authors emphasize practitioner vigilance as well as continued study across NLP applications.
  • Conclusion: Continued training of language models on temporally aligned data has little effect, motivating less costly temporal adaptation methods than ongoing labeled-data collection.The conclusion contrasts limited temporal adaptation gains with the expense of maintaining annotated datasets.

A A Metric for Temporal Degradation

The paper introduces temporal degradation (TD), an interpretable metric that summarizes how task performance changes when training and evaluation timestamps differ. TD fits performance differences across training timestamps for each evaluation timestamp, then averages the resulting slopes across evaluation years.

  • A A Metric for Temporal Degradation: The modified difference D increases when performance deteriorates regardless of whether training data precedes or follows evaluation data.This transformation makes temporal performance changes easier to compare in a monotone form.
  • A A Metric for Temporal Degradation: TD measures the average rate of performance deterioration per time period as training and evaluation timestamps diverge.The metric is designed to make temporal misalignment comparable across tasks.
  • A A Metric for Temporal Degradation: For each evaluation timestamp, TD is the slope of a line fitted to modified performance differences from models trained at multiple timestamps.The final TD score averages these slopes across all evaluation years.
  • A A Metric for Temporal Degradation: Examples from POLIAFF and YELPCLS illustrate both temporal sensitivity and cases without substantial sensitivity, with most deterioration trends approximated linearly.The examples support using a line-based rate of degradation in practice.

B Details of Model Development

The experiments use GPT-2 implementations for temporal adaptation and task finetuning, with hyperparameters documented separately for domains and tasks. Additional implementation details include the training hardware and a cited figure for timestamp examples.

  • B Details of Model Development: Temporal adaptation trains GPT-2 separately across each domain and timestamp for k steps.The implementation uses Huggingface’s GPT-2 implementation, with hyperparameters listed in Table 4.
  • B Details of Model Development: Examples illustrating timestamp choices are referenced in Figure 4.The supplied passage provides the reference but not the figure’s content.
  • B Details of Model Development: Temporal finetuning uses Huggingface’s GPT-2 implementation for classification and summarization tasks.Hyperparameters for the eight tasks are listed in Table 5.
  • B Details of Model Development: The finetuning experiments were run on Quadro RTX 800 GPUs.The passage identifies the hardware but does not provide further training settings.

C Data Collection

The data collection process combines task-specific filtering, manual annotation, and temporal consistency checks across social media, scientific articles, news, and review-related examples. The section also points to example TD calculations from POLIAFF and YELPCLS.

  • C Data Collection: The paper notes that released data are intended for non-commercial use and provides additional postprocessing and collection details.The supplied passages do not specify those additional procedures.
  • C Data Collection: POLIAFF uses manually annotated U.S. politician Twitter accounts, excluding party switchers, independents, and suspended accounts.One volunteer double-checked the annotations for correctness.
  • C Data Collection: Figure 7 presents three TD calculations, with one example from POLIAFF and two from YELPCLS.The annotated values are raw evaluation scores, while plotted markers represent modified performance differences.
  • C Data Collection: AIC samples Semantic Scholar science documents restricted to ICML or AAAI computer-science papers with abstracts of at least 50 tokens.The filtering criteria define the scientific-article subset used in the experiments.
  • C Data Collection: Newsroom articles for PUBCLS and NEWSUM are retained only when the metadata year appears in the text and no future year is mentioned.These checks are applied to both supervised temporal finetuning and unsupervised temporal adaptation.
  • C Data Collection: PUBCLS balances Fox News, New York Times, and Washington Post labels across years through uniform-random downsampling.The procedure is intended to equalize label distributions over time.

D Extended Results

Extended results examine word-overlap correlations, full finetuning outcomes, and combinations of temporal adaptation with finetuning. The reported patterns show that temporal effects vary by task, while finetuning on similar time periods produces similar performance despite differing adaptation conditions.

  • D Extended Results: The study reports correlations between word overlap and each task’s performance in Table 6.The table contains Pearson r correlation coefficients for the relationship between vocabulary overlap and performance.
  • D Extended Results: The full finetuning results without domain adaptation are presented in Figure 8.These results correspond to the downstream-task experiments described in Section 3.1.
  • D Extended Results: The full results for finetuning with temporal domain adaptation are presented in Figure 7.The supplied passages identify the figure and section but do not provide additional cell values.
  • D Extended Results: Temporal finetuning can cause massive performance drops, but degradation varies by task: YELPCLS, MFC, and TWIERC show minimal degradation, whereas POLIAFF and NEWSUM deteriorate substantially over time.Figure 8 shades each column by score, with higher scores represented by darker orange cells.
  • D Extended Results: Models finetuned on similar time periods performed similarly regardless of how their DAPT conditions differed.Table 7 compares the default pretrained model with combinations of temporal adaptation and finetuning.
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