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

• URL: http://arxiv.org/abs/2306.14325v2
• Date: Tue, 27 Jun 2023 23:26:47 GMT
• Title: The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
• Authors: Lance Ying, Katherine M. Collins, Megan Wei, Cedegao E. Zhang, Tan Zhi-Xuan, Adrian Weller, Joshua B. Tenenbaum, Lionel Wong
• Abstract summary: We study the 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. Our model closely matches human response patterns and better predicts human judgements than using an LLM alone.
• Score: 50.32802502923367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• AMONGAGENTS: Evaluating Large Language Models in the Interactive Text-Based Social Deduction Game [12.384945632524424]
  This paper focuses on creating proxies of human behavior in simulated environments, with Among Us utilized as a tool for studying simulated human behavior. Our work demonstrates that state-of-the-art large language models (LLMs) can effectively grasp the game rules and make decisions based on the current context.
  arXiv  Detail & Related papers  (2024-07-23T14:34:38Z)
• Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion? [18.770522926093786]
  Large Language Models have shown exceptional generative abilities in various natural language and generation tasks. We study a special application of ToM abilities that has higher stakes and possibly irreversible consequences. We focus on the task of Perceived Behavior Recognition, where a robot employs a Large Language Model (LLM) to assess the robot's generated behavior in a manner similar to human observer.
  arXiv  Detail & Related papers  (2024-01-10T18:09:36Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• Why can neural language models solve next-word prediction? A mathematical perspective [53.807657273043446]
  We study a class of formal languages that can be used to model real-world examples of English sentences. Our proof highlights the different roles of the embedding layer and the fully connected component within the neural language model.
  arXiv  Detail & Related papers  (2023-06-20T10:41:23Z)
• Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks [39.39138995087475]
  We ask how much of human-like thinking can be captured by learning statistical patterns in language alone. Our benchmark contains two problem-solving domains (planning and explanation generation) and is designed to require generalization. We find that humans are far more robust than LLMs on this benchmark.
  arXiv  Detail & Related papers  (2022-05-11T18:14:33Z)
• Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
  We study the task of few-shot $textitlanguage coordination$. We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities. This requires the ability to model the partner's beliefs, a vital component of human communication.
  arXiv  Detail & Related papers  (2021-07-12T19:26:11Z)
• PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [86.21137454228848]
  We factorize PIGLeT into a physical dynamics model, and a separate language model. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation. It is able to correctly forecast "what happens next" given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%.
  arXiv  Detail & Related papers  (2021-06-01T02:32:12Z)
• Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI [0.0]
  We argue that an explainable artificial intelligence must possess a rationale for its decisions, be able to infer the purpose of observed behaviour, and be able to explain its decisions in the context of what its audience understands and intends. To communicate that rationale requires natural language, a means of encoding and decoding perceptual states. We propose a theory of meaning in which, to acquire language, an agent should model the world a language describes rather than the language itself.
  arXiv  Detail & Related papers  (2021-04-23T13:13:46Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.