UL2: UNIFYING LANGUAGE LEARNING PARADIGMS

Abstract

Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives -two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pretraining objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups. Finally, by scaling our model up to 20B parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on oneshot summarization. Finally, we show that UL2 20B works well with chain-ofthought prompting and reasoning tasks, making it an appealing choice for research into reasoning at a small to medium scale of 20B parameters. We publicly release Flax-based T5X model checkpoints for the 20B model.

1. INTRODUCTION

Note: This is a static copy of this paper as of the ICLR submission. Please use the arxiv version for future updates : https://arxiv.org/abs/2205.05131. TYVM. There is a wide spectrum of pre-trained model options for NLP researchers and practitioners these days (Devlin et al., 2018; Brown et al., 2020; Raffel et al., 2019; Radford et al., 2019; Liu et al., 2019; Yang et al., 2019; Thoppilan et al., 2022; Fedus et al., 2021; Du et al., 2021; Chowdhery et al., 2022) . When faced with the question of what model should one use, the answer is often it depends, followed by on what task? Answering this can be overwhelming, comprising of a number of fine-grained follow-up questions like, 'encoder-only or encoder-decoder?', 'span corruption or language model?'. Pressing further, the answer always seems to depend on the target downstream task. This paper questions and rethinks this thought process, specifically answering the questions of why should the choice of the pre-trained LM depend on the downstream task? and how can we pre-train models that work universally well across many tasks?. This paper proposes a step towards making a universally applicable language model possible. We present a framework for Unifying Language Learning Paradigms or UL2 in short, that is consistently 1

