Muppet: Massive Multi-task Representations with Pre-Finetuning (2101.11038v1)

Abstract: We propose pre-finetuning, an additional large-scale learning stage between LLM pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks. We show that pre-finetuning consistently improves performance for pretrained discriminators (e.g.~RoBERTa) and generation models (e.g.~BART) on a wide range of tasks (sentence prediction, commonsense reasoning, MRC, etc.), while also significantly improving sample efficiency during fine-tuning. We also show that large-scale multi-tasking is crucial; pre-finetuning can hurt performance when few tasks are used up until a critical point (usually above 15) after which performance improves linearly in the number of tasks.

• The paper proposes a pre-finetuning framework that leverages massive multi-task learning to improve language model representations.
• Authors introduce a novel training scheme with loss scaling and task-heterogeneous batches to balance gradient contributions across tasks.
• Empirical results demonstrate state-of-the-art performance on benchmarks like RTE and HellaSWAG, achieving better sample efficiency with fewer labeled examples.

Massive Multi-task Representations with Pre-Finetuning

The paper "Muppet: Massive Multi-task Representations with Pre-Finetuning" discusses a novel approach to enhancing the performance of pre-trained LLMs through a method called pre-finetuning. This method introduces a large-scale multi-task learning stage between the initial pre-training and the subsequent fine-tuning phases, involving approximately 50 diverse datasets with over 4.8 million labeled examples. The primary hypothesis is that representations gleaned through this intermediate stage will generalize better across various downstream tasks, such as sentence prediction, commonsense reasoning, and machine reading comprehension (MRC).

Core Contributions

1. Pre-Finetuning Framework: The paper presents pre-finetuning as a significant stage built on top of traditional pre-training and fine-tuning schemes, enhancing the learning process by incorporating massively multi-task learning. This approach leverages a wide range of tasks to train models such as RoBERTa and BART systematically.
2. New Training Scheme: The paper introduces a novel multi-task training scheme emphasising loss scaling and task-heterogeneous batches. This scheme aims to stabilize training by ensuring that gradient steps are uniformly distributed across varying tasks, reducing imbalance issues commonly faced in standard multi-task learning approaches.
3. Impact of Scale: By conducting extensive experiments, the authors highlight the crucial role of scale in multi-task learning. They find that representations improve linearly beyond a certain threshold of tasks, pinpointed at around 15. Below this threshold, however, performance tends to degrade.
4. Sample Efficiency: Pre-finetuned models exhibit improved sample efficiency, requiring fewer labeled examples during fine-tuning phases. Particularly in low-resource settings, these models achieve superior results compared to their traditionally pre-trained counterparts.

Empirical Findings

The research provides empirical evidence that pre-finetuning enhances model performance across various tasks. Notably, it achieves new state-of-the-art results in RTE and HellaSWAG datasets. The findings suggest that models improved through pre-finetuning can significantly outperform standard pre-trained models across a diverse set of benchmarks.

Through a careful analysis of multi-task learning strategies, the paper identifies critical points in the multi-tasking process where the number of tasks impacts representation quality. This addresses prior concerns observed in works like T5, where additional multi-task learning layers did not yield similar benefits on larger models.

Discussion and Future Implications

The methodology proposed brings forth multiple implications for the field of natural language processing and machine learning. By effectively leveraging massive and diverse datasets through pre-finetuning, the paper paves a path for developing more robust and generalizable language representations. As the complexity of tasks in AI continues to grow, these methods provide a scalable solution to constructing models capable of tackling a wide array of real-world applications.

Future directions might explore automating the pre-finetuning procedure across different domains or identifying potentially beneficial task sequences based on transfer learning insights. In addition, expanding the model's capabilities to handle multi-modal data could further generalize the applicability of pre-finetuning strategies, thus broadening the scope beyond language-specific tasks.

In summary, the innovative use of pre-finetuning and the emphasis on multi-task learning scale introduced in this paper provide a meaningful advancement in leveraging pre-trained LLMs. These findings emphasize the importance of task diversity and the potential of pre-finetuning to strengthen the quality of learned representations, which could lead to more effective and efficient deployment of AI systems in various complex domains.