Time Waits for No One! Analysis and Challenges of Temporal Misalignment (2111.07408v2)

Abstract: 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.

• The paper introduces a new Temporal Degradation (TD) metric to quantify performance loss over time in various NLP tasks.
• Empirical analysis shows varied temporal drift, with political affiliation and publisher classification being notably sensitive.
• Temporal adaptation via continued pretraining yields limited gains, underscoring the need for task-specific data updates.

Temporal Drift in LLMs: Analysis and Implications

The paper "Time Waits for No One! Analysis and Challenges of Temporal Misalignment" presents an empirical investigation into the effects of temporal drift on LLMs (LMs) and their downstream applications in NLP. Temporal drift refers to the phenomenon where LLM performance degrades due to a mismatch between the time period of the training data and that of the evaluation data. This paper is crucial given the dynamic nature of language, and the increasing reliance on pre-trained LMs in NLP systems.

Methodology Overview

The authors explore the impact of temporal misalignment across eight tasks spanning multiple domains: social media, scientific papers, news articles, and reviews. The tasks include political affiliation classification, entity typing, mention type classification, venue classification, media frame classification, publisher classification, summarization, and review rating classification. Each task is evaluated over several time periods, and the degree of performance degradation is quantified using a metric termed Temporal Degradation (TD) score. This metric captures the rate of performance deterioration as a function of time, allowing comparison across different tasks.

Key Findings

1. Temporal Drift Across Tasks: The paper reveals significant variation in the extent to which temporal misalignment affects task performance. TD scores indicate that political affiliation classification and publisher classification are markedly sensitive to such misalignment, showing substantial degradation. Conversely, entity typing and media frame classification demonstrate robustness, exhibiting minimal performance drops.
2. Domain-Specific Effects: The degree of drift varies considerably by domain. For instance, Twitter data showed rapid language changes, leading to high temporal drift, whereas food reviews displayed stability over time. This suggests that language use dynamics inherent to specific genres or domains can influence how much contextual drift will affect NLP performance.
3. Limited Mitigation via Temporal Adaptation: Continued pretraining of LMs on temporally aligned data (temporal domain adaptation) yielded small improvements and was often inadequate compared to task-specific finetuning on data from the target year. This highlights the importance of updating labeled datasets alongside LMs to mitigate the effects of temporal drift effectively.

Implications and Future Directions

The findings emphasize the necessity for researchers and practitioners to account for temporal drift when designing NLP systems and benchmarks. Models trained on temporally misaligned data risk performance degradation, which can have real-world consequences in applications like text classification, sentiment analysis, and entity recognition.

Future research should explore more sophisticated temporal adaptation techniques that leverage emerging corpora more effectively and consider the broader impacts of semantic shifts. Continual learning approaches that enable LMs to adapt progressively to new data streams could serve as potential solutions. Additionally, incorporating knowledge from studies on lexical semantic change might offer insights into managing temporal misalignment. Understanding how sensitive specific tasks and domains are to language dynamics will guide the strategic collection and annotation of new data, optimizing costs and enhancing model robustness.

Indeed, the paper presents robust evidence that temporal drift over relatively short timeframes can affect LLMs and downstream tasks significantly. This work underscores the importance of ongoing adaptations to both the LMs themselves and the labeled datasets they rely on, paving the way for more temporally resilient NLP applications.