Mind the Gap: Assessing Temporal Generalization in Neural Language Models

Abstract: Our world is open-ended, non-stationary, and constantly evolving; thus what we talk about and how we talk about it change over time. This inherent dynamic nature of language contrasts with the current static language modelling paradigm, which trains and evaluates models on utterances from overlapping time periods. Despite impressive recent progress, we demonstrate that Transformer-XL LLMs perform worse in the realistic setup of predicting future utterances from beyond their training period, and that model performance becomes increasingly worse with time. We find that, while increasing model size alone -- a key driver behind recent progress -- does not solve this problem, having models that continually update their knowledge with new information can indeed mitigate this performance degradation over time. Hence, given the compilation of ever-larger language modelling datasets, combined with the growing list of language-model-based NLP applications that require up-to-date factual knowledge about the world, we argue that now is the right time to rethink the static way in which we currently train and evaluate our LLMs, and develop adaptive LLMs that can remain up-to-date with respect to our ever-changing and non-stationary world. We publicly release our dynamic, streaming language modelling benchmarks for WMT and arXiv to facilitate LLM evaluation that takes temporal dynamics into account.

• The paper reveals that Transformer language models can experience up to a 16% increase in relative perplexity when predicting data beyond their training period.
• The paper demonstrates that increasing model size does not prevent temporal degradation, with smaller, current models outperforming larger, outdated ones.
• The paper shows that implementing dynamic evaluation significantly mitigates performance drops, enhancing predictions for time-sensitive tasks.

Analysis of Temporal Generalization in Neural LLMs

The paper "Mind the Gap: Assessing Temporal Generalization in Neural LLMs" provides a detailed examination of how neural LLMs, specifically Transformer-based models, generalize over time. The study identifies a critical gap in the current language modeling paradigm, which largely relies on static evaluations using datasets that overlap temporally with the training data. The authors contend that this practice might overestimate the models' performance, given the dynamic and non-stationary nature of real-world language.

Key Findings

1. Temporal Degradation of Model Performance: The study finds that Transformer-XL models perform significantly worse when predicting future utterances that fall outside their training period. The performance degradation worsens over time, with up to a 16% increase in relative perplexity observed when attempting to predict articles published two years beyond the training data.
2. Model Size Does Not Mitigate Temporal Issues: Increasing model size, a principal driver of recent advancements in language modeling, does not solve the temporal degradation problem. A smaller, more up-to-date model can outperform a larger, outdated model, indicating that temporal adaptation is necessary for maintaining performance.
3. Impact on Downstream Tasks: The temporal degradation adversely affects downstream tasks that require current factual knowledge, such as answering questions about recent events. However, tasks where the context is provided, such as reading comprehension, are less affected by outdated LLMs.
4. Dynamic Evaluation as a Mitigation Strategy: The authors propose dynamic evaluation as an online learning method to continually update model parameters with new information, thereby slowing the degradation rate. This approach, when applied to fast-changing words (e.g., named entities), leads to significant perplexity improvements.

Implications and Future Directions

The findings highlight the necessity for adaptive language modeling approaches that can address the challenge of non-stationary data. Theoretical implications include reassessing the way we build and evaluate LLMs, taking into account the temporal dynamics of language. Practically, the study suggests that to deployment systems such as conversational agents, machine translation, and news summarization, should integrate mechanisms for continuous learning to remain effective over time.

The research opens avenues for further exploration into continual and lifelong learning in NLP systems. The release of dynamic, streaming language modeling benchmarks by the authors aims to foster advancements in adaptive LLMs.

Conclusion

By scrutinizing the temporal generalization ability of neural LLMs, the paper underscores the gap between static training paradigms and the inherently dynamic nature of language. The work advocates for the integration of temporal awareness in LLM evaluations and trainings, pushing for models that can better adapt to future linguistic environments. This study is pivotal for advancing the understanding and development of temporally resilient NLP applications, ensuring their relevance and accuracy in a constantly evolving world.