Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

Abstract

Natural language understanding (NLU) has made massive progress driven by large benchmarks, but benchmarks often leave a long tail of infrequent phenomena underrepresented. We reflect on the question: Have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail? We conceptualize the long tail using macro-level dimensions (underrepresented genres, topics, etc.), and perform a qualitative meta-analysis of 100 representative papers on transfer learning research for NLU. Our analysis asks three questions: (i) Which long tail dimensions do transfer learning studies target? (ii) Which properties of adaptation methods help improve performance on the long tail? (iii) Which methodological gaps have greatest negative impact on long tail performance? Our answers highlight major avenues for future research in transfer learning for the long tail. Lastly, using our meta-analysis framework, we perform a case study comparing the performance of various adaptation methods on clinical narratives, which provides interesting insights that may enable us to make progress along these future avenues.

Benchmarking Long-tail Generalization with Likelihood Splits

“…While we do not test whether models can memorize long-tail knowledge, we instead test whether models can process long-tail sentences. Naik et al (2022) note that it is challenging to catalogue and evaluate generalization along micro-level dimensions and instead propose benchmarks that vary along macro-level dimensions (such as the language and domain) as a proxy. We hypothesize that LMs learn which micro-level phenomena are rare, as this would improve their overall language modeling objective.…”