CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2104.08835v2)

Abstract: Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks. We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols. To instantiate different seen/unseen task partitions in CrossFit and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from open-access NLP datasets and converted to a unified text-to-text format. Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks. We also observe that the selection of upstream learning tasks can significantly influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability.

• The paper introduces the CROSSFIT framework and NLP Few-shot Gym to systematically evaluate cross-task generalization in NLP.
• It demonstrates that multi-task learning can outperform meta-learning, achieving up to a 35.06% improvement in few-shot performance on unseen tasks.
• Experimental results reveal that increasing upstream data volume does not proportionately boost generalization, emphasizing the importance of strategic task selection.

Overview of CROSSFIT Y: A Few-shot Learning Challenge for Cross-task Generalization in NLP

The paper "CROSSFIT Y: A Few-shot Learning Challenge for Cross-task Generalization in NLP" introduces and examines a structured methodology for enhancing few-shot learning capabilities in NLP tasks through cross-task generalization. This research responds to the challenge of efficiently extending the learned knowledge from prior tasks to novel ones, reflecting humans' linguistic adaptability when confronted with scarce data in novel contexts. The work presents two primary contributions: the CROSSFIT Challenge and the NLP Few-shot Gym, forming a basis for exploring cross-task generalization within diverse NLP contexts.

Key Contributions and Methodologies

CROSSFIT Framework: The CROSSFIT framework establishes a comprehensive approach to paper cross-task generalization by defining standardized seen/unseen task partitions, controlled data access during learning phases, and specific evaluation protocols. It particularly addresses the issue of unseen task performance enhancement through observed tasks in upstream learning stages.

NLP Few-shot Gym: A notable establishment accompanying the CROSSFIT framework is the NLP Few-shot Gym, a collection of 160 widely diverse NLP tasks formatted uniformly in a text-to-text style. This repository serves as the substrate for inspecting the efficacy of cross-task generalization methods across variably structured tasks and domains.

Experimental Approach: The paper utilizes multi-task learning (MTL) and meta-learning techniques such as MAML, first-order MAML, and Reptile to analyze their impact on cross-task generalization across diverse partitions. Through meticulous empirical evaluations, the paper discusses the performance variance based on task similarity, dataset augmentation, and possible reductions in performance due to pre-trained knowledge erosion.

Numerical Results and Observations

One of the significant findings is that simple multi-task learning frequently outperforms meta-learning methods in achieving few-shot efficacy on unseen tasks. Multi-task learning showed an average relative performance gain (ARG) of up to 35.06% based on the random partition strategy, contrasting with lower ARGs observed in meta-learning techniques. Additionally, the results pointed out the intricate role task selection plays during upstream learning, influencing the outcomes across unseen tasks.

Another insightful observation concerns data volume for upstream tasks. Enlarging the data size does not proportionately enhance cross-task generalization; experimental results with increased data (~8x) during upstream learning did not lead to substantial performance improvements.

Theoretical Implications and Future Directions

The research brings to light several theoretical implications regarding cross-task generalization. It posits that selecting upstream learning tasks based on surface-level task format and goals might be suboptimal, as deeper task similarity measures could be crucial for improved generalization.

For future discourse, the paper suggests avenues such as refining meta-learning algorithms to cater specifically to text-to-text transformer architectures and further exploration into automated task selection mechanisms based on task similarity dimensions distinct from format or goal categorizations.

Conclusively, this paper invites further exploration into the systematic understanding and strategic enhancement of cross-task generalization—a pursuit intending to advance towards constructing models embodying general linguistic intelligence similar to human faculties. The CROSSFIT Challenge and the NLP Few-shot Gym are posited as foundational tools for continuing such academic endeavors.