Introspective Planning: Guiding Language-Enabled Agents to Refine Their Own Uncertainty

Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding. However, LLM hallucination may result in robots confidently executing plans that are misaligned with user goals or, in extreme cases, unsafe. Additionally, inherent ambiguity in natural language instructions can induce task uncertainty, particularly in situations where multiple valid options exist. To address this issue, LLMs must identify such uncertainty and proactively seek clarification. This paper explores the …

advanced agents cases cs.ai cs.cl cs.lg enabling hallucination language language models large language large language models llm llm hallucination llms natural natural language planning reasoning refine robots skills through uncertainty