Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2106.13353v2)

Abstract: Prompting LLMs (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. In this work, we show that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering. In fact, one can use null prompts, prompts that contain neither task-specific templates nor training examples, and achieve competitive accuracy to manually-tuned prompts across a wide range of tasks. While finetuning LMs does introduce new parameters for each downstream task, we show that this memory overhead can be substantially reduced: finetuning only the bias terms can achieve comparable or better accuracy than standard finetuning while only updating 0.1% of the parameters. All in all, we recommend finetuning LMs for few-shot learning as it is more accurate, robust to different prompts, and can be made nearly as efficient as using frozen LMs.

An Analysis of Few-Shot Learning through Simple Prompting and Parameter Fine-Tuning in LLMs

The paper "Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with LLMs" explores methodologies intended to optimize the use of large pre-trained LLMs (LMs) in few-shot learning scenarios. Few-shot learning—the capacity to generalize information from a minimal set of examples—presents considerable challenges in both academic and applied contexts. This research scrutinizes the mechanisms of prompt design and parameter tuning to enhance minimal data learning, particularly in masked LMs such as RoBERTa and ALBERT.

Key Findings and Methodologies

Prompt Engineering Simplification:

A significant aspect of the paper emphasizes that rigorous prompt engineering—a process thought to heavily influence few-shot learning efficacy—is not as crucial when employing prompt-based finetuning methods. The authors propose the use of "null prompts," which omit specific task templates and training examples, yet achieve comparable performance to manually crafted prompts in diverse NLP tasks. These null prompts consist of the direct concatenation of input fields with a [MASK] token, minimizing complexities associated with prompt design.

Parameter-Efficient Finetuning:

Conventional methods of LLM adaptation typically involve tuning extensive parameter sets, introducing memory drawbacks for each new application. This paper demonstrates that tuning can be restricted to merely the model's bias terms— a methodology termed "BitFit"—without loss of performance. Notably, BitFit requires the update of only about 0.1% of the full parameter set, showcasing an efficiency gain without significant trade-offs in accuracy.

Comparison and Evaluation:

By rigorously comparing various methods, including in-context learning and standard parameters finetuning, the authors draw attention to the limitations of leaving LM weights unchanged, which tend to demand heavily optimized prompts. On the contrary, prompt-based finetuning with lightweight adjustments to certain parameters not only improves accuracy but simplifies the design requirements. This observation underscores the adaptability of masked LMs when minimal prompt engineering is combined with selective parameter tuning.

Implications and Future Directions

The practical implications of this research are substantial, particularly for developers aiming to deploy ML in scenarios with constrained data availability. Adopting null prompts drastically reduces the need for trial-and-error in prompt designing, and deploying BitFit ensures that using large LMs remains computationally feasible. These findings suggest a shift towards simpler, more efficient architectures that reduce overhead in model adaptation practices.

The paper advocates for a reevaluation of the contributions of prompt patterns and verbalizers to few-shot learning success, thereby encouraging further exploration into architectures capable of retaining high efficacy with diminished complexity in both prompt engineering and parameter tuning. Future research might explore the scalability of these findings to diverse model types, including left-to-right generative models like GPT-3, and their applications on broader machine learning tasks.

In conclusion, this paper establishes valuable insights into the development of more efficient NLP models for few-shot learning, promoting greater accessibility and functionality in AI-driven language processing applications. Understanding and leveraging the findings can lead to the advancement of LMs that require minimal computational resources while providing robust performance across a variety of tasks.