Reframing Instructional Prompts to GPTk's Language (2109.07830v3)

Abstract: What kinds of instructional prompts are easier to follow for LLMs (LMs)? We study this question by conducting extensive empirical analysis that shed light on important features of successful instructional prompts. Specifically, we study several classes of reframing techniques for manual reformulation of prompts into more effective ones. Some examples include decomposing a complex task instruction into multiple simpler tasks or itemizing instructions into sequential steps. Our experiments compare the zero-shot and few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks across 6 categories. Compared with original instructions, our reframed instructions lead to significant improvements across LMs with different sizes. For example, the same reframed prompts boost few-shot performance of GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all tasks. Furthermore, reframed instructions reduce the number of examples required to prompt LMs in the few-shot setting. We hope these empirically-driven techniques will pave the way towards more effective future prompting algorithms.

Analysis of LLM Sensitivity to Prompt Framing

The paper "Reframing Instructional Prompts to GPT's Language" addresses a pivotal aspect of interacting with LLMs such as GPT-3: the sensitivity of these models to the specific framing of input prompts. The key objective is to explore ways to transform task descriptions into effective prompts that enhance the performance of LLMs, focusing extensively on GPT-3. The authors propose and empirically validate a series of "prompt-reframing" techniques. These techniques are designed to improve LLM performance by modifying prompt characteristics.

Summary of Findings and Methodology

Through empirical investigation, the authors identify crucial features that determine the effectiveness of instructional prompts. By reframing complex tasks into simpler, component-specific prompts, GPT-3's few-shot learning performance improves by an average of 14%, while reducing the sample complexity compared to existing few-shot baselines. The research spans 12 diverse NLP tasks, including question generation and classification, demonstrating cross-model applicability of the reframing guidelines beyond just GPT-3.

Reframing Techniques

The paper introduces several distinct reframing strategies that can be used by model designers:

1. Pattern Reframing: This involves the use of low-level patterns instead of abstract concepts, enabling the model to parse task-specific details more effectively.
2. Itemizing Reframing: Long and complex instructions are converted into concise bulleted lists that delineate steps and requirements clearly.
3. Decomposition Reframing: This breaks down a multifaceted task into multiple manageable subtasks, each addressed individually.
4. Restraining Reframing: Some tasks may deviate from the model’s pre-training objectives, so adding constraints on output generation helps align model responses with desired outputs.
5. Specialization Reframing: Task instructions are refined to directly address the task-specific operations required, without reliance on generic directives.

These approaches collectively improve the model's performance by aligning its input processing with its interpretative capabilities, ensuring that the instructions are well-understood.

Implications and Future Research Directions

The paper demonstrates empirical gains in various NLP benchmarks, reinforcing that prompt engineering is critical for maximizing model efficacy and efficiency. Beyond immediate performance enhancements, prompt-reframing offers practical benefits in deploying LLMs at scale without the computational overhead of model tuning and retraining.

Key implications for future AI development include:

• Model Generalizability: The cross-architecture effectiveness of reframed prompts underscores the potential for developing universally applicable prompt engineering strategies.
• Sustainability in Model Training: Reframing reduces the necessity for extensive task-specific data and fine-tuning, thus offering a sustainable alternative to resource-intensive training processes.
• Adaptation to Large Models: As larger models become more prevalent, the computational efficiency offered by prompt-reframing could be significantly advantageous.

Further research could explore automated prompt-reframing methodologies to streamline the task definition process. Additionally, evaluating the impact of prompt-reframing across a broader spectrum of model architectures and applications could validate its scalability and robustness in real-world deployments.

In conclusion, "Reframing Task Definitions to GPT-3's Language" provides a structured and empirically-supported approach to maximize the efficacy of LLMs through thoughtful prompt engineering. This line of research holds the promise of advancing the performance boundaries of LLMs while advocating sustainable and adaptable AI methodologies.