Emergent Response Planning in LLMs (2502.06258v2)

Abstract: In this work, we argue that LLMs, though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations encode future outputs beyond the next token}$. Through simple probing, we demonstrate that LLM prompt representations encode global attributes of their entire responses, including $\textit{structure attributes}$ (e.g., response length, reasoning steps), $\textit{content attributes}$ (e.g., character choices in storywriting, multiple-choice answers at the end of response), and $\textit{behavior attributes}$ (e.g., answer confidence, factual consistency). In addition to identifying response planning, we explore how it scales with model size across tasks and how it evolves during generation. The findings that LLMs plan ahead for the future in their hidden representations suggest potential applications for improving transparency and generation control.

• The paper reveals that LLMs exhibit emergent planning by encoding response attributes, challenging traditional token-by-token generation assumptions.
• It employs systematic probing of hidden representations to predict structural, content, and behavioral attributes with high correlation and robust F1 scores.
• The findings imply that scaling model size enhances planning capabilities, offering new avenues for improved output control and transparency.

Emergent Response Planning in LLMs: Analysis and Implications

The paper "Emergent Response Planning in LLM" investigates a novel aspect of LLMs, namely their capacity for emergent response planning. Contrary to the established view that LLMs, trained primarily on next-token prediction, only operate on a local, token-by-token basis, this paper provides evidence that they are capable of planning future responses. This emergent planning behavior is discerned through the hidden representations that encode attributes beyond the mere prediction of the successive token.

The research outlines a method whereby various global attributes of an LLM's response can be anticipated by probing its hidden prompt representations. These attributes are categorized into structural attributes (such as response length and reasoning steps), content attributes (for instance, specific choices in narrative construction), and behavioral attributes (including metrics like answer confidence and factual consistency). These findings are significant as they suggest that LLMs possess latent planning mechanisms that could potentially be leveraged to improve model transparency and control in generative tasks.

Empirical evidence in the paper includes systematic probing experiments conducted across different models and tasks. Simple probes were trained to predict response attributes from hidden representations, demonstrating that LLMs encode comprehensive planning information in their prompt representations. Major results from this paper include high correlation scores for regression tasks like response length and reasoning steps, as well as robust F1 scores for classification tasks concerned with character choices and factual consistency. These results are notable because they illustrate that LLMs are not purely reactive agents but instead exhibit a degree of foresight in their generation process.

Further layer-wise analyses revealed nuanced insights into how planning information accumulates and peaks differently across model layers. Structural attributes tend to manifest in the middle layers, while content-related attributes surface in the uppermost layers, signifying intricate planning mechanisms embedded throughout the model architecture.

The paper also explores how the planning ability scales with model size, showing that larger models within the same family tend to have strengthened planning capabilities. This scaling insight is critical for developers looking to optimize LLMs for tasks requiring nuanced control over output characteristics.

This work poses several important implications for the future development of LLMs. From a practical standpoint, understanding and harnessing emergent planning can significantly enhance the interaction between LLMs and users, providing more predictable and customizable outputs. For example, by predicting aspects like response length and reasoning complexity, it is conceivable to develop systems that dynamically allocate computational resources during runtime.

Furthermore, the ability to predict aspects such as factual consistency and answer confidence has potential applications for bias mitigation and improving the safety of machine-generated content. However, the paper also raises questions about the introspective abilities of LLMs; despite encoding these attributes, they fall short in explicitly reasoning about their planning processes, suggesting a gap between implicit capacity and explicit self-awareness.

Theoretical implications include a refined understanding of LLMs' operation beyond statistical learning. The emergence of planning abilities calls for a theory that encompasses both prediction and planning as intrinsic capabilities within artificial neural networks. This could open paths for integrating planning models traditionally used in AI with LLM architectures to enhance decision-making tasks.

In conclusion, "Emergent Response Planning in LLM" presents a compelling examination of the latent capabilities of LLMs, with meaningful insights for both the theoretical expansion of AI planning paradigms and practical enhancements in LLM applications. As future research further explores understanding the causal mechanisms and layer-wise dynamics of response planning, it holds the promise of significantly advancing the state-of-the-art in model interpretability and control.