Emergent Response Planning in LLM

2025-02-10

AI Summary

This work investigates whether large language models (LLMs), trained solely on next-token prediction, implicitly encode global response attributes—such as structure, content, and behavioral properties—in their hidden representations, thereby exhibiting emergent planning capabilities. Method: We propose lightweight linear probes to systematically decode cross-temporal representations of response attributes—including length, reasoning steps, role selection, answer choice, confidence, and factual consistency—from intermediate-layer activations across multiple LLM scales. Contribution/Results: Empirical results show that these attributes are decodable with high accuracy early in generation—significantly surpassing random baselines—and that planning capability strengthens with model scale and evolves dynamically across deeper layers. This study provides the first empirical evidence that LLMs possess implicit, global, and cross-temporal response planning capacity, offering a novel perspective on their internal mechanistic behavior.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $ extbf{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 $ extit{structural attributes}$ (response length, reasoning steps), $ extit{content attributes}$ (character choices in storywriting, multiple-choice answers at the end of response), and $ extit{behavioral attributes}$ (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 suggests potential applications for improving transparency and generation control.

Problem

• LLMs encode future outputs in hidden representations
• Probe LLM representations for global response attributes
• Explore scaling and evolution of LLM response planning

Innovation

• LLMs encode future outputs
• Probing reveals global attributes
• Scales with model size