MetaPrompting: Learning to Learn Better Prompts (2209.11486v4)

Abstract: Prompting method is regarded as one of the crucial progress for few-shot nature language processing. Recent research on prompting moves from discrete tokens based hard prompts'' to continuoussoft prompts'', which employ learnable vectors as pseudo prompt tokens and achieve better performance. Though showing promising prospects, these soft-prompting methods are observed to rely heavily on good initialization to take effect. Unfortunately, obtaining a perfect initialization for soft prompts requires understanding of inner LLMs working and elaborate design, which is no easy task and has to restart from scratch for each new task. To remedy this, we propose a generalized soft prompting method called MetaPrompting, which adopts the well-recognized model-agnostic meta-learning algorithm to automatically find better prompt initialization that facilitates fast adaptation to new prompting tasks.Extensive experiments show MetaPrompting tackles soft prompt initialization problem and brings significant improvement on four different datasets (over 6 points improvement in accuracy for 1-shot setting), achieving new state-of-the-art performance.

MetaPrompting: Advancing Prompt Initialization for Few-Shot Learning in NLP

The paper "MetaPrompting: Learning to Learn Better Prompts" addresses significant challenges in the field of few-shot learning, particularly with soft-prompting methods in NLP. Building on the established role of prompting in aligning pre-trained LLMs (PLMs) with downstream tasks, this research shifts the focus from conventional prompt design to enhancing the initialization of soft prompts through meta-learning strategies. The proposed MetaPrompting framework introduces an optimization-centered meta-learning technique aimed at improving the adaptability and performance of PLMs when facing novel few-shot tasks, achieving noteworthy accuracy improvements across various benchmark datasets.

Soft vs. Hard Prompting in Few-Shot Learning

Prompting strategies in NLP facilitate the conversion of tasks to forms reminiscent of the models' pre-training conditions. The paper scrutinizes the evolution from discrete token-based "hard prompts" to continuous "soft prompts." While soft prompts demonstrate enhanced potential by operating in a continuous space, they also entail notably increased sensitivity to initialization, unlike their discrete counterparts. The initialization challenge emerges as a significant bottleneck, requiring a sophisticated understanding of LLMs' internal dynamics and often hampering performance gains due to task-specific constraints.

The MetaPrompting Framework

The core innovation of the paper is the introduction of MetaPrompting, which leverages model-agnostic meta-learning algorithms like MAML and Reptile to refine prompt initialization. The framework seeks to abstract meta-knowledge from source domain tasks, equipping PLMs with a more effective initialization that supports rapid adaptation to new tasks. This process is particularly relevant considering the vast solution space of soft prompts, where optimal initialization can substantially influence the success of few-shot learning.

Empirical Validation and Results

Empirical validation of MetaPrompting was conducted across four datasets: 20 Newsgroups, Amazon, HuffPost, and Reuters, reflecting diverse domains and text characteristics. MetaPrompting consistently outperformed existing state-of-the-art methods, including strong meta-learning baselines, with over seven points' accuracy enhancement in 1-shot settings. These results underscore its efficacy in both single and few-shot scenarios, as well as its ability to maintain robustness against variations in prompt forms.

Theoretical and Practical Implications

The research notably expands our understanding of meta-learning's applicability to NLP, especially in addressing initialization problems endemic to soft prompts. The potential reduction in manual prompt engineering workload and the increased flexibility in transferring learned initializations across different tasks mark profound practical benefits. Furthermore, these advancements could lead to broader applications beyond NLP, fostering meta-learning methodologies across various machine learning subfields.

Speculation on Future Developments

Future work may explore integrating MetaPrompting with metric-based learning methods or model-based meta-learning to further enhance few-shot learning methodologies. Additionally, investigating the potential alignment of meta-learned initializations with external knowledge sources could amplify the contextual adaptability of the LLMs. The broader adaptation of MetaPrompting could herald new paradigms in dynamic task adaptation and real-time model refinement, especially in applications demanding high interaction with continuously evolving data contexts.

This paper presents a significant step towards overcoming limitations in prompt-based fine-tuning and suggests intriguing pathways for future research in both theoretical and applied dimensions of AI and NLP.