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Mind the Gap: Assessing Temporal Generalization in Neural Language Models

NeurIPS 2021 · Large language models › Retrieval and knowledge · approach: Knowledge editing in LLMs · 24 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
train on data overlapping the test period (CONTROL) instead of time-stratified past dataTransformer-XL 287M · CUSTOMNEWSperplexity: 21.33 -> 18.38Helped
train on data overlapping the test period (CONTROL) instead of time-stratified past dataTransformer-XL 287M · WMTperplexity: 22.45 -> 21.11Helped
train on data overlapping the test period (CONTROL) instead of time-stratified past dataTransformer-XL 287M · arXivperplexity: 23.07 -> 21.38Helped
increase model size from 287M to 448M parameterstime-stratified Transformer-XL · WMT and CUSTOMNEWS temporal degradationrelative perplexity slope over time: 5.5% lower absolute perplexityNo clear effect
add dynamic evaluation (update all parameters on test stream)time-stratified Transformer-XL · WMTperplexity: 22.45 -> 22.17Helped
add dynamic evaluation (update all parameters on test stream)time-stratified Transformer-XL · arXivperplexity: 23.07 -> 20.98Helped
add dynamic evaluation (update all parameters)time-stratified Transformer-XL · WMT new/shifting wordsperplexity: 109.73 -> 66.26Helped
update only bias terms instead of all parameters in dynamic evaluation (ablation)time-stratified Transformer-XL · CUSTOMNEWSperplexity: 20.72 -> 20.96No clear effect
update only embeddings instead of all parameters in dynamic evaluation (ablation)time-stratified Transformer-XL · arXivperplexity: 20.98 -> 22.27Hurt
train LM on data until 2009 rather than 2008Transformer-XL · NewsQA 2009 questionsF1: 0.47 -> 0.47No clear effect

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