AI Safety Papers

Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models

Authors

Tianyi Men, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao

Abstract

Planning, as the core module of agents, is crucial in various fields such as embodied agents, web navigation, and tool using. With the development of large language models (LLMs), some researchers treat large language models as intelligent agents to stimulate and evaluate their planning capabilities. However, the planning mechanism is still unclear. In this work, we focus on exploring the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations. First, we study how planning is done internally by analyzing the multi-layer perception (MLP) and multi-head self-attention (MHSA) components at the last token. We find that the output of MHSA in the middle layers at the last token can directly decode the decision to some extent. Based on this discovery, we further trace the source of MHSA by information flow, and we reveal that MHSA extracts information from spans of the goal states and recent steps. According to information flow, we continue to study what information is encoded within it. Specifically, we explore whether future decisions have been considered in advance in the representation of flow. We demonstrate that the middle and upper layers encode a few short-term future decisions. Overall, our research analyzes the look-ahead planning mechanisms of LLMs, facilitating future research on LLMs performing planning tasks.

Publication Details

  • Published: Unknown
  • Added to AI Safety Papers: 2024-11-11
  • Original Paper: Link

Summary

This paper investigates how large language models (LLMs) perform look-ahead planning, focusing on their internal mechanisms and ability to consider future steps when making decisions. The research provides important insights into whether LLMs plan greedily (one step at a time) or can look ahead multiple steps. Key Contributions:

  1. First comprehensive study of planning interpretability mechanisms in LLMs

  2. Demonstration of the “Look-Ahead Planning Decisions Existence Hypothesis”

  3. Analysis of how internal representations encode future decisions

  4. Investigation of information flow patterns during planning tasks

Figure 1: An example of greedy and look-ahead planning.
Figure 1: An example of greedy and look-ahead planning.

Figure 1 The researchers use the Blocksworld environment as their primary testing ground, where models must manipulate colored blocks to achieve target configurations. The paper introduces a clear distinction between greedy planning (considering only the next step) and look-ahead planning (considering multiple future steps). Methodology: The study employs a two-stage analysis approach:

  1. Information Flow Analysis: - Examines how planning information moves through the model - Studies Multi-Head Self-Attention (MHSA) and Multi-Layer Perceptron (MLP) components - Shows that middle-layer MHSA can partially decode correct decisions
Figure 3: Extraction rate of different components in Llama-2-7b-chat-hf.
Figure 3: Extraction rate of different components in Llama-2-7b-chat-hf.

Figure 3

  1. Internal Representation Analysis: - Probes different layers to understand encoded information - Investigates both current state and future decision encoding - Demonstrates that models can encode short-term future decisions
Figure 10: Future action linear probe in Llama-2-7b-chat-hf.
Figure 10: Future action linear probe in Llama-2-7b-chat-hf.

Figure 10 Key Findings:

  1. LLMs do encode future decisions in their internal representations

  2. The accuracy of future predictions decreases with planning distance

  3. MHSA primarily extracts information from goal states and recent steps

  4. Middle and upper layers encode short-term future decisions

Figure 11: Single step intervened analysis in Vicuna-7b.
Figure 11: Single step intervened analysis in Vicuna-7b.

Figure 11 The research used two prominent LLMs for evaluation: - Llama-2-7b-chat - Vicuna-7B Both models showed similar patterns in their planning mechanisms, achieving complete plan success rates of around 61-63% for complex tasks. Limitations:

  1. Analysis limited to open-source models

  2. Focus primarily on Blocksworld environment

  3. Difficulty in evaluating commonsense planning tasks This research represents a significant step forward in understanding how LLMs approach planning tasks and opens new avenues for improving their planning capabilities. The findings suggest that while LLMs do possess look-ahead planning abilities, these capabilities are primarily effective for short-term planning and diminish over longer sequences.