Do Prompt-Based Models Really Understand the Meaning of their Prompts? (2109.01247v2)

Abstract: Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to learn faster in the same way that humans learn faster when provided with task instructions expressed in natural language. In this study, we experiment with over 30 prompt templates manually written for natural language inference (NLI). We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively "good" prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on hundreds of prompts (Sanh et al., 2022). That is, instruction-tuned models often produce good predictions with irrelevant and misleading prompts even at zero shots. In sum, notwithstanding prompt-based models' impressive improvement, we find evidence of serious limitations that question the degree to which such improvement is derived from models understanding task instructions in ways analogous to humans' use of task instructions.

An Examination of Prompt Semantics in LLMs

The paper "Do Prompt-Based Models Really Understand the Meaning of Their Prompts?" by Webson and Pavlick presents an investigation into whether prompt-based LLMs truly comprehend the semantics of the prompts they utilize for task-solving, particularly when compared to human understanding of task instructions. This research is timely given the burgeoning interest in prompt-based models in the field of NLP, especially in zero-shot and few-shot learning contexts.

Study Overview

The paper employs a comprehensive set of experiments involving more than 30 manually written prompt templates across various categories, including instructive, irrelevant, misleading, and null prompts. These experiments are conducted on diverse models, including ALBERT, T5, and T0, with model sizes ranging up to 175 billion parameters, providing a broad perspective on model performance across different scales. The focus of the empirical analysis is Natural Language Inference (NLI), a cornerstone task in NLP. The research investigates the impact of both the content of the templates and the choice of target words on the performance of these models.

Key Findings

One of the central findings of the paper is that the models' performances are not as sensitive to the semantic content of the prompts as one might expect. Interestingly, models often perform equally well with irrelevant or misleading prompts as they do with thoughtfully constructed instructive ones, even in few-shot settings. This suggests a limited level of semantic understanding inherent in these models despite their impressive zero- and few-shot achievements.

Moreover, the choice of the target words used in the prompts significantly affects model performance. Models demonstrated better accuracy with intuitive target words like “yes” and “no” compared to arbitrary or semantically equivalent words. This indicates a possible reliance on surface-level heuristic features rather than deeper semantic understanding.

Implications

The results raise questions about the true nature of the improvements seen in prompt-based models and challenge the common assumption that these models understand prompts in a human-like way. They emphasize the distinction between LLMs' pattern recognition capabilities versus genuine understanding of natural language semantics.

Theoretically, these findings suggest that advancements in prompt engineering may not require as much semantic expertise as previously thought. Practically, they point towards potential limitations in deploying these models in real-world applications where nuanced understanding is critical.

Future Directions

The paper sets the stage for various avenues of future research. Improving the semantic sensitivity of LLMs, perhaps through refined training strategies or hybrid models, emerges as a crucial goal. Furthermore, there is potential for exploring the interaction between model architecture and prompt design to enhance model robustness and performance reliability.

In conclusion, while prompt-based models represent a significant stride forward in NLP, they do not yet embody a form of understanding akin to human comprehension of task instructions. By foregrounding these limitations, the paper by Webson and Pavlick provides a valuable lens for interpreting prompted learning and calls for strategic advancements in model training and prompt construction.