Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models (2010.05874v1)

Abstract: Massively multilingual models subsuming tens or even hundreds of languages pose great challenges to multi-task optimization. While it is a common practice to apply a language-agnostic procedure optimizing a joint multilingual task objective, how to properly characterize and take advantage of its underlying problem structure for improving optimization efficiency remains under-explored. In this paper, we attempt to peek into the black-box of multilingual optimization through the lens of loss function geometry. We find that gradient similarity measured along the optimization trajectory is an important signal, which correlates well with not only language proximity but also the overall model performance. Such observation helps us to identify a critical limitation of existing gradient-based multi-task learning methods, and thus we derive a simple and scalable optimization procedure, named Gradient Vaccine, which encourages more geometrically aligned parameter updates for close tasks. Empirically, our method obtains significant model performance gains on multilingual machine translation and XTREME benchmark tasks for multilingual LLMs. Our work reveals the importance of properly measuring and utilizing language proximity in multilingual optimization, and has broader implications for multi-task learning beyond multilingual modeling.

Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

The paper addresses the significant challenges associated with optimizing multilingual models that encompass dozens or even hundreds of languages. While employing a language-agnostic methodology to optimize a combined multilingual task objective is a common practice, the effective characterization and utilization of the underlying problem structure to enhance optimization efficiency are not thoroughly explored. This research peeks into the black-box of multilingual optimization by analyzing loss function geometry with a primary focus on the alignment of gradient trajectories.

The authors identify that gradient similarity along optimization trajectories provides a critical signal correlating strongly with both language proximity and overall model performance. This observation unveils a major limitation in existing gradient-based multi-task learning (MTL) methods and paves the way for a novel optimization procedure named Gradient Vaccine. This method promotes geometrically aligned parameter updates for closely related tasks, fundamentally enhancing the optimization process.

Core Contributions and Findings

1. Characterization of Multilingual Optimization: The paper explores how typologically similar languages experience more similar loss geometries during the training of multilingual models. This relationship aids in uncovering the cross-lingual transfer mechanisms, which are not apparent when traditionally employing a monolithic training objective.
2. Gradient Similarity and Task Performance: Empirical investigations reveal that tasks with higher gradient similarities typically demonstrate better joint training performance. This insight into gradient dynamics emphasizes the potential for negative interference when language proximities are ignored, impacting model efficacy negatively.
3. Proposed Methodology – Gradient Vaccine: The Gradient Vaccine method innovatively adapts optimization processes by leveraging task relatedness and engaging in preemptive gradient adjustment to align with desired similarity objectives. This scalable optimization method effectively reduces detrimental gradient interference and fosters positive cross-lingual task transfer.
4. Empirical Validation: Through experiments on multilingual neural machine translation tasks and XTREME benchmarks, the proposed Gradient Vaccine demonstrates significant improvements over baseline models. Specifically, it shows notable performance gains on both high-resource and low-resource language pairs, indicating versatile applicability for various multilingual scenarios.

Broader Implications

The implications of this research extend beyond multilingual NLP applications, suggesting advancements for general multi-task learning problems. By better understanding and utilizing the intrinsic loss geometry, models can be trained more efficiently, potentially leading to improved performance across diverse machine learning tasks. This work also advocates for a closer examination of language proximities in multilingual contexts, which could catalyze new techniques in model adaptation and data sampling strategies.

Future Directions

There is substantial potential for future developments based on these findings. First, extending this approach to other architectures and beyond language tasks could yield valuable insights. Furthermore, exploring how these principles might assist in environments with heavily unbalanced datasets remains an intriguing area for future research. As the complexity and scale of machine learning models continue to grow, techniques derived from the Gradient Vaccine could support the optimization of expansive multi-task systems involving numerous interacting tasks and objectives.