mlsanityData to 7 Oct 2026

LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions

arXiv 2501.00830 ↗First posted 1 Jan 2025cs.CL cs.AIcited by 10

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To address this limitation, we propose a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning strengths of action languages. Our approach, termed "LLM+AL," leverages the LLM's strengths in semantic parsing and commonsense knowledge generation alongside the action language's proficiency in automated reasoning based on encoded knowledge. We compare LLM+AL against state-of-the-art LLMs, including ChatGPT-4, Claude 3 Opus, Gemini Ultra 1.0, and o1-preview, using benchmarks for complex reasoning about actions. Our findings indicate that, although all methods exhibit errors, LLM+AL, with relatively minimal human corrections, consistently leads to correct answers, whereas standalone LLMs fail to improve even with human feedback. LLM+AL also contributes to automated generation of action languages.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 22 of its 36 references that are in the map. First posted on arXiv 1 Jan 2025.

Builds on

22 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers23 Oct
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Models6 Oct

Other references · 20

  1. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  2. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  3. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  4. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,057
  5. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  6. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  7. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
  8. 2022Socratic Models: Composing Zero-Shot Multimodal Reasoning with Languagecited by 513
  9. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 496
  10. 2023Language Models can Solve Computer Taskscited by 371
  11. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  12. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 230
8 more
  1. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 204
  2. 2024On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Taskscited by 137
  3. 2024Chain of Thoughtlessness? An Analysis of CoT in Planningcited by 100
  4. 2021Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoningcited by 85
  5. 2018Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systemscited by 40
  6. 2023When do you need Chain-of-Thought Prompting for ChatGPT?cited by 34
  7. 2023On Loop Formulas with Variablescited by 2
  8. 2023Elementary Sets for Logic Programscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.

  1. 2025LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Modelscited by 38
  2. 2025Unifying Inference-Time Planning Language Generationcited by 3
  3. 2025Language Model as Planner and Formalizer under Constraintscited by 3
  4. 2025Iterative Formalization and Planning in Partially Observable Environmentscited by 1
  5. 2025Learning Hierarchical Domain Models Through Environment-Grounded Interactioncited by 1
  6. 2026Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisationcited by 1
  7. 2026Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expandingcited by 1
  8. 2025A Community-driven vision for a new Knowledge Resource for AIcited by 0
  9. 2026Logic-Guided Data Extraction with Answer Set Programming and Large Language Modelscited by 0
  10. 2026LLMs for Executable Multi-Agent System Specification Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
  2. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
  3. 2026The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
  4. 2025From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users
  5. 2026A Comprehensive Evaluation of LLM Reasoning: From Single-Model to Multi-Agent Paradigms
  6. 2025OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases
  7. 2025A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
  8. 2024LLM2: Let Large Language Models Harness System 2 Reasoning