mlsanityData to 14 Oct 2026

The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs

arXiv 2306.14325 ↗First posted 25 Jun 2023cs.AI cs.LGcited by 23

Not on the map. Why?

Abstract (arXiv)

Human beings are social creatures. We routinely reason about other agents, and a crucial component of this social reasoning is inferring people's goals as we learn about their actions. In many settings, we can perform intuitive but reliable goal inference from language descriptions of agents, actions, and the background environments. In this paper, we study this process of language driving and influencing social reasoning in a probabilistic goal inference domain. We propose a neuro-symbolic model that carries out goal inference from linguistic inputs of agent scenarios. The "neuro" part is a large language model (LLM) that translates language descriptions to code representations, and the "symbolic" part is a Bayesian inverse planning engine. To test our model, we design and run a human experiment on a linguistic goal inference task. Our model closely matches human response patterns and better predicts human judgements than using an LLM alone.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 23 of its 52 references that are in the map. First posted on arXiv 25 Jun 2023.

Builds on

23 of 52 references.

References · 23

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  5. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  6. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 349
  7. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 292
  8. 2023Faithful Chain-of-Thought Reasoningcited by 288
  9. 2020Consistent Estimators for Learning to Defer to an Expertcited by 271
  10. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 163
  11. 2022Synchromesh: Reliable code generation from pre-trained language modelscited by 156
  12. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
11 more
  1. 2020Learning to Complement Humanscited by 124
  2. 2023From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thoughtcited by 119
  3. 2023Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programscited by 80
  4. 2020Online Bayesian Goal Inference for Boundedly-Rational Planning Agentscited by 68
  5. 2022Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning taskscited by 56
  6. 2019On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inferencecited by 49
  7. 2015Learning the Preferences of Ignorant, Inconsistent Agentscited by 45
  8. 2021AGENT: A Benchmark for Core Psychological Reasoningcited by 42
  9. 2023Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMscited by 20
  10. 2021Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mindcited by 19
  11. 2023LaMPP: Language Models as Probabilistic Priors for Perception and Actioncited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 11; 2026: 4.

23 papers.

Sort:

By area:

  1. 2024Building Machines that Learn and Think with Peoplecited by 73
  2. 2024OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Modelscited by 54
  3. 2025Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlocited by 46
  4. 2025LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Modelscited by 38
  5. 2025Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Modelscited by 29
  6. 2024Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planningcited by 28
  7. 2024GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignmentcited by 16
  8. 2025Social World Modelscited by 16
  9. 2025Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Modelscited by 14
  10. 2024Neuro-Symbolic AI: Explainability, Challenges, and Future Trendscited by 10
  11. 2024Grounding Language about Belief in a Bayesian Theory-of-Mindcited by 8
  12. 2025Resource Rational Contractualism Should Guide AI Alignmentcited by 8
11 more
  1. 2025Infusing Theory of Mind into Socially Intelligent LLM Agentscited by 8
  2. 2024Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mindcited by 6
  3. 2025EnigmaToM: Improve LLMs' Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity Statescited by 6
  4. 2025Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Plannercited by 5
  5. 2026Post-training makes large language models less human-likecited by 5
  6. 2026Think Thrice Before You Speak: Dual knowledge-enhanced Theory-of-Mind Reasoning for Persuasive Agentscited by 2
  7. 2026Grounding Social Perception in Intuitive Physicscited by 1
  8. 2024Bridging the Gap: Representation Spaces in Neuro-Symbolic AIcited by 0
  9. 2025Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causalitycited by 0
  10. 2025What's in the Box? Reasoning about Unseen Objects from Multimodal Cuescited by 0
  11. 2026MindZero: Learning Online Mental Reasoning With Zero Annotationscited by 0