mlsanityData to 14 Oct 2026

Human-Centered Planning

arXiv 2311.04403 ↗First posted 8 Nov 2023cs.AIcited by 1

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Abstract (arXiv)

LLMs have recently made impressive inroads on tasks whose output is structured, such as coding, robotic planning and querying databases. The vision of creating AI-powered personal assistants also involves creating structured outputs, such as a plan for one's day, or for an overseas trip. Here, since the plan is executed by a human, the output doesn't have to satisfy strict syntactic constraints. A useful assistant should also be able to incorporate vague constraints specified by the user in natural language. This makes LLMs an attractive option for planning. We consider the problem of planning one's day. We develop an LLM-based planner (LLMPlan) extended with the ability to self-reflect on its output and a symbolic planner (SymPlan) with the ability to translate text constraints into a symbolic representation. Despite no formal specification of constraints, we find that LLMPlan performs explicit constraint satisfaction akin to the traditional symbolic planners on average (2% performance difference), while retaining the reasoning of implicit requirements. Consequently, LLM-based planners outperform their symbolic counterparts in user satisfaction (70.5% vs. 40.4%) during interactive evaluation with 40 users.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

16 of 28 references.

References · 16

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  6. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  7. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  8. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
  9. 2023Teaching Large Language Models to Self-Debugcited by 988
  10. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  11. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 398
  12. 2023Language Models can Solve Computer Taskscited by 372
4 more
  1. 2021Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Modelscited by 139
  2. 2023On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)cited by 85
  3. 2022Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning taskscited by 56
  4. 2022Plansformer: Generating Symbolic Plans using Transformerscited by 41

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problemscited by 4