Emergent Response Planning in LLMs

TL;DR

This paper reveals that large language models inherently encode future response attributes in their hidden states, demonstrating emergent planning behaviors that can enhance transparency and control in AI-generated content.

Contribution

It uncovers the emergent planning capabilities of LLMs through probing hidden representations, showing how they encode future response attributes and how this scales with model size.

Findings

LLMs encode future response attributes in hidden states

Response planning correlates with model size and generation stage

Potential for improved transparency and control in LLM outputs

Abstract

In this work, we argue that large language models (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.