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Language Modelling as a Multi-Task Problem (2101.11287v1)

Published 27 Jan 2021 in cs.CL and cs.LG

Abstract: In this paper, we propose to study LLMling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether LLMs adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of LLMs as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of LLMling.We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.

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Authors (4)
  1. Lucas Weber (11 papers)
  2. Jaap Jumelet (25 papers)
  3. Elia Bruni (32 papers)
  4. Dieuwke Hupkes (49 papers)
Citations (13)

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