Boosting Natural Language Generation from Instructions with Meta-Learning

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6792 - 6808 December 7-11, 2022 ©2022 Association for Computational Linguistics Boosting Natural Language Generation from Instructions with Meta-Learning Budhaditya Deb, Guoqing Zheng, Ahmed Hassan Awadallah Microsoft Research {budeb, zheng, hassanam}@microsoft.com Abstract Recent work has shown that language mod- els (LMs) trained with multi-task instructional learning (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved per- formance compared to prompt tuning. MTIL illustrates that LMs can extract and use infor- mation about the task from instructions beyond the surface patterns of the inputs and outputs. This suggests that meta-learning may further enhance the utilization of instructions for effec- tive task transfer. In this paper we investigate whether meta-learning applied to MTIL can further improve generalization to unseen tasks in a zero-shot setting. Specifically, we pro- pose to adapt meta-learning to MTIL in three directions: 1) Model Agnostic Meta Learn- ing (MAML), 2) Hyper-Network (HNet) based adaptation to generate task specific parameters conditioned on instructions, and 3) an approach combining HNet and MAML. Through exten- sive experiments on the large scale Natural In- structions V2 dataset, we show that our pro- posed approaches significantly improve over strong baselines in zero-shot settings. In partic- ular, meta-learning improves the effectiveness of instructions and is most impactful when the test tasks are strictly zero-shot (i.e. no similar tasks in the training set) and are "hard" for LMs, illustrating the potential of meta-learning for MTIL for out-of-distribution tasks. 1 Introduction Given some basic instructions and a few demon- strations, humans are capable of conducting di- verse tasks without any supervision. Can language models perform similarly on unseen tasks when trained with instructions? Specifically can such an approach work on complex generation tasks with relatively smaller language models (LMs)? The recent advances in large LMs have shown tremendous potential in diverse AI applications and have the capability to change the way model developers and users interact with intelligent sys- tems. The inherent representative power of such models has shown that diverse NLP tasks can be solved purely by appending prompts or demonstra- tions in context before a test input (Radford et al., 2019; Brown et al., 2020). This has led to the rise of prompt-based training (Liu et al., 2021) where even much smaller models trained on a large set of tasks in a multi-task setting with prompts, can behave similarly (Schick and Schütze, 2021). A natural extension of the prompt tuning concept involves adding instructions about the task along with the demonstrations. Instructions are more in- formative than prompts and aid the language mod- els to solve unseen tasks better. Instructions can have different forms, for example to convey a short task specific statement (e.g. "Provide a short sum- mary for the following input") (Schick and Schütze, 2021b), or a natural language question ("How would you rephrase that in a few words?") (Sanh et al., 2022; Wei et al., 2022; Bach et al., 2022). However, for complex generation …

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