Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs

2026-04-22

AI Summary

This work addresses the heavy reliance of current large language models on prompt engineering for generating task planning explanations, a limitation exacerbated by the lack of systematic understanding of how users construct and refine prompts. To bridge this gap, the authors propose COMPASS, a novel method that formalizes users’ cognitive states—such as attention, comprehension, and uncertainty—and integrates implicit cognitive signals with explicit interaction cues through a Partially Observable Markov Decision Process (POMDP). This framework enables adaptive prompt synthesis tailored to task planning explanations. Experimental evaluations on two cyber-physical system case studies demonstrate that COMPASS effectively leverages user cognition and feedback to significantly enhance both the quality and personalization of generated explanations.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task planning. However, the quality and reliability of these explanations heavily depend on effective prompt engineering. The lack of a systematic understanding of how diverse stakeholder groups formulate and refine prompts hinders the development of tools that can automate this process. We introduce COMPASS (COgnitive Modelling for Prompt Automated SynthesiS), a proof-of-concept self-adaptive approach that formalises prompt engineering as a cognitive and probabilistic decision-making process. COMPASS models unobservable users' latent cognitive states, such as attention and comprehension, uncertainty, and observable interaction cues as a POMDP, whose synthesised policy enables adaptive generation of explanations and prompt refinements. We evaluate COMPASS using two diverse cyber-physical system case studies to assess the adaptive explanation generation and their qualities, both quantitatively and qualitatively. Our results demonstrate the feasibility of COMPASS integrating human cognition and user profile's feedback into automated prompt synthesis in complex task planning systems.

Problem

• prompt engineering
• task planning
• explanation generation
• Large Language Models
• human-AI interaction

Innovation

• prompt engineering
• cognitive modeling
• POMDP
• self-adaptive explanation
• LLM-based planning