Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts (2112.08348v2)

Abstract: Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous prompts that is faithful to the problem they solve. In practice, we observe a "wayward" behavior between the task solved by continuous prompts and their nearest neighbor discrete projections: We can find continuous prompts that solve a task while being projected to an arbitrary text (e.g., definition of a different or even a contradictory task), while being within a very small (2%) margin of the best continuous prompt of the same size for the task. We provide intuitions behind this odd and surprising behavior, as well as extensive empirical analyses quantifying the effect of various parameters. For instance, for larger model sizes we observe higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop in accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tasks, providing guidance for future progress in prompting LLMs.

• The paper demonstrates that continuous prompts can achieve near-equivalent task performance despite projecting onto arbitrary, semantically unrelated discrete texts.
• It shows that larger models and longer prompts exacerbate prompt waywardness, challenging the reliability of current discrete interpretations.
• The study highlights potential risks for adversarial exploitation and underscores the need for robust methods in discrete prompt extraction.

Analysis of Discretized Interpretations in Continuous Prompts

The paper Prompt Waywardness: Challenges of Discretized Interpretation of Continuous Prompts examines the perplexing behavior of continuous prompts when interpreted as discrete text within the context of LLMs (LMs). The authors investigate the feasibility of deriving meaningful discrete interpretations from continuous prompts that retain the effectiveness of prompt tuning, a method that tunes a prompt for target tasks by adjusting only a small set of parameters instead of the entire model.

Key Insights and Findings

The paper presents a hypothesis termed "Prompt Waywardness," suggesting that there is often a surprising disconnect between the functionalities solved by continuous prompts and their nearest-neighbor discrete representations. The authors propose that for any discrete prompt, it is possible to identify a continuous prompt that achieves near-equivalent task performance while its projection maps onto an arbitrary text, regardless of the semantic relevance of that text to the task. This hypothesis is critical as it implies that continuous prompts might solve a task effectively without their discrete form providing a truthful or relevant representation of what the prompt is accomplishing.

Empirical Evidence

The paper provides empirical support for the Prompt Waywardness hypothesis using multiple classification datasets. The experiment results show that continuous prompts can indeed be constrained to project onto arbitrary, and often semantically unrelated, textual phrases—achieving high prompt F1 scores with minimal drops in task accuracy. Across various model sizes, the phenomenon persists, suggesting that the complexity and expressive capacity of larger models exacerbate this disconnect.

Furthermore, the paper finds that larger models and longer prompts exhibit a higher degree of waywardness. This observation aligns with the understanding that deeper networks imbue initial layers with vast expressive power, enabling a wide range of solutions that adhere to a given discrete projection.

Implications for Future Research

The implications of prompt waywardness challenge the interpretation and trustworthiness of continuous prompt tuning, particularly in applications where semantic transparency is crucial. Key takeaways include:

• Challenges to Discrete Interpretability: The findings suggest inherent difficulties in deriving faithful discrete interpretations from continuous prompts using nearest-neighbor approaches. This disconnect raises concerns about the reliability of using such interpretations for trust in AI systems.
• Potential for Adversarial Use: The propensity of continuous prompts to harbor misleading discrete projections could be exploited for adversarial strategies, posing a threat when systems rely on these projections for decision-making transparency.
• Difficulties in Optimizing for Discrete Prompt Discovery: Current approaches leveraging differentiable pathways toward discrete human-readable forms may be inherently unstable due to the degeneracy introduced by the wayward nature of the underlying continuous representations.

Conclusion

The paper presented in this paper underscores a fundamental issue within prompt tuning paradigms, providing a basis for future work aimed at both understanding and addressing the limitations of continuous prompt interpretations. The onus is now on the AI research community to innovate solutions that either bridge this gap or reimagine the architecture or methodology in which prompts are used and interpreted in LMs. The implications extend to a broader ambit, potentially influencing the design and ethical considerations of NLP applications.