Exploring and Predicting Transferability across NLP Tasks (2005.00770v2)

Abstract: Recent advances in NLP demonstrate the effectiveness of training large-scale LLMs and transferring them to downstream tasks. Can fine-tuning these models on tasks other than LLMing further improve performance? In this paper, we conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems (text classification, question answering, and sequence labeling). Our results show that transfer learning is more beneficial than previously thought, especially when target task data is scarce, and can improve performance even when the source task is small or differs substantially from the target task (e.g., part-of-speech tagging transfers well to the DROP QA dataset). We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size. Overall, our experiments reveal that factors such as source data size, task and domain similarity, and task complexity all play a role in determining transferability.

• The paper introduces a scalable empirical analysis showing effective transfer even with limited data sizes.
• The paper demonstrates that task and domain similarity are critical factors for successful NLP transfer learning.
• The paper presents novel task embeddings leveraging cosine similarity to predict the best source tasks for transfer.

Exploring and Predicting Transferability across NLP Tasks

The paper "Exploring and Predicting Transferability across NLP Tasks" by Tu Vu et al. investigates the nuances of task transferability within the field of NLP. This work is particularly relevant given the increasing prominence of transfer learning strategies in AI research, where models trained on large datasets are adapted to perform well on related tasks with less available data.

Study Framework and Methodology

The authors conduct an in-depth empirical analysis of transfer learning across 33 distinct NLP tasks, categorized into three classes: text classification/regression, question answering, and sequence labeling. Their research design encompasses over 3,000 task and data regime combinations, a scale that surpasses previous works on the subject. The data regimes are methodically divided into:

• Full source task dataset to full target dataset,
• Full source dataset to limited target dataset,
• Limited source dataset to limited target dataset.

In this context, 'full' implies utilizing the entire dataset, while 'limited' refers to a subset comprising 1,000 samples. The primary goal is to understand under what conditions transfer learning is most beneficial, especially when target task data is sparse.

Key Findings

1. Transfer Viability Across Data Sizes: The paper reveals that task transferability is often viable across varying data sizes, challenging earlier assumptions that effective transfer necessitates large source datasets. Interestingly, transfer remains beneficial even when both source and target datasets are limited, underscoring the adaptability of NLP models.
2. Importance of Task Similarity: The work identifies that task and domain similarity, rather than solely dataset size, are pivotal in determining transfer success. Particularly in data-scarce scenarios, aligning source and target task types can significantly influence performance gains.
3. Unintuitive Transfer Instances: A number of out-of-class transfers demonstrated unexpected positive results, such as successful transfer from part-of-speech tagging tasks to question answering datasets like DROP, which were initially assumed to be unlikely based on their apparent disparity.

Predictive Modeling of Task Transferability

Beyond empirical analysis, the authors introduce a method to predict which tasks are best suited for transfer to enhance a given target task. They propose two types of task embeddings:

• TextEmb: Derived from the domain's token representations, aligning the semantic and syntactic similarities among tasks.
• TaskEmb: Informed by the Fisher Information Matrix, it captures the task-specific attribute relevant for transfer—a novel approach borrowed from computer vision meta-learning paradigms.

Their predictive models leverage cosine similarity within these embedding spaces to rank the source tasks for transfer potential, demonstrating superior performance to the heuristic of dataset size. The model effectively identifies which tasks to leverage for improving performance on a new target task, especially beneficial in resource-limited environments.

Implications and Speculations

The findings suggest that designing NLP systems can greatly benefit from strategic task selection guided by learned embeddings, rather than relying on heuristic approaches. This has practical implications for AI deployment where data scarcity is a chronic constraint, such as dialect recognition and underrepresented language tasks.

In future research, the methodologies explored here, particularly the utilization of task embeddings for transfer learning, may extend to real-time adaptation mechanisms or multi-task learning efficiencies across a broader scope of AI applications. This trajectory could see AI models becoming more flexible, adaptive learners, capable of seamlessly transferring acquired knowledge to novel scenarios without incurring significant additional training costs.

Overall, this paper contributes robust empirical evidence supporting nuanced task selection in transfer learning and proposes innovative methods to predict task synergies, paving the way for more intelligent and resource-efficient NLP models.