Bridging Human and Machine Learning: Meta Prompt Tuning and Lifelong Few-Shot Language Models

Motivation

Modern language models (LMs) achieve strong performance on many NLP benchmarks but differ from humans in two key ways: they require large amounts of labeled data to generalize (few‑shot learning) and they suffer catastrophic forgetting when trained sequentially on new tasks (continual/lifelong learning). This work addresses both challenges by integrating recent advances in meta‑learning, prompt tuning, and dynamic architecture adaptation.

Meta Prompt Tuning (MPT)

MPT applies meta‑learning to prompt‑tuning. Instead of learning a prompt from scratch for each downstream task, MPT learns a shared initialization of prompt embeddings across a set of related source tasks. During meta‑training, the algorithm optimizes the initialization so that a small number of gradient steps on a new task’s few‑shot examples yields strong performance. The procedure can be described as:

Sample a batch of tasks from the meta‑training set.

For each task, perform k gradient updates on its few‑shot data starting from the shared prompt initialization.

Compute the loss on a held‑out validation set for each task and back‑propagate to update the shared initialization.

Experiments on multiple source‑target configurations show that MPT consistently outperforms standard prompt tuning and fine‑tuning when only a handful of labeled examples are available. Limitations include sensitivity to the choice of source tasks and the need for a sufficiently diverse meta‑training corpus.

Lifelong Sequence Generation (LSG) and Dynamic Module Expansion & Adaptation (DMEA)

LSG concerns the continual training of a generative LM on a sequence of generation tasks (e.g., translation, summarization) while preserving knowledge of earlier tasks. DMEA addresses LSG by dynamically expanding the model’s architecture based on inter‑task relatedness:

Task similarity assessment: before training on a new task, DMEA computes a similarity score between the new task and previously seen tasks using embedding‑based metrics.

Module allocation: if the new task is sufficiently dissimilar, DMEA adds a new lightweight module (e.g., a feed‑forward block or adapter) initialized from a meta‑learned prior.

Knowledge reuse: for similar tasks, DMEA re‑uses existing modules and optionally fine‑tunes them, reducing parameter growth.

Regularization: a distillation loss aligns the outputs of the expanded model with those of the previous version on stored exemplars, mitigating forgetting.

This dynamic expansion enables the model to acquire new capabilities without overwriting earlier knowledge, and empirical results demonstrate lower forgetting rates compared with static‑architecture baselines.

Continual Few‑Shot Relation Learning (CFRL) and ERDA

Relation extraction is a fundamental NLP task often required in downstream applications. CFRL studies the scenario where a model must learn new relation types from a few examples while the task sequence evolves. The proposed solution, Embedding space Regularization and Data Augmentation (ERDA), consists of two components:

Embedding space regularization: a contrastive loss keeps embeddings of previously learned relations well‑separated from those of new relations, preserving a stable representation space.

Data augmentation: synthetic examples are generated by swapping entity mentions and applying paraphrase models, effectively increasing the few‑shot support set for each new relation.

Combined, ERDA reduces catastrophic forgetting and improves few‑shot accuracy on benchmark relation‑extraction streams.

Lifelong Few‑Shot Language Learning (LFLL) and LFPT5

LFLL defines a unified paradigm where a single LM continuously encounters tasks of varying types (classification, generation, extraction) and domains, each presented with only a few labeled instances. To operationalize LFLL, the authors introduce LFPT5, a prompt‑tuning framework built on the T5 architecture:

All tasks share a common prompt encoder that is meta‑trained across a diverse task pool.

When a new task arrives, LFPT5 performs a few gradient steps on the task‑specific prompt while keeping the base LM weights frozen.

Historical prompts are stored and periodically revisited using a replay buffer, enabling the model to retain prior knowledge.

Task‑type adapters are optionally inserted to handle modality differences (e.g., encoder‑only for classification vs. encoder‑decoder for generation).

Empirical evaluation shows that LFPT5 maintains high performance on earlier tasks while quickly adapting to new ones, outperforming naïve fine‑tuning and static prompt‑tuning baselines.

Conclusion

The presented methods collectively advance language models toward more human‑like learning: efficient few‑shot adaptation via meta‑initialized prompts, continual acquisition of new skills through dynamic architecture growth, and robust retention of prior knowledge using regularization and replay. These techniques enable LMs to operate effectively in data‑scarce, evolving real‑world environments.