Summary Translating Natural Language to Probabilistic World Models arxiv.org
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The document explores the integration of language and probabilistic reasoning in order to understand and reason about the world, emphasizing the challenges and potential applications of translating natural language into probabilistic world models.
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Key Points
- The document proposes a computational framework called rational meaning construction that integrates neural models of language with probabilistic models for flexible commonsense reasoning.
- The document discusses the challenges faced by modular and symbolic models of language and thought and emphasizes the theoretical distinction between language and thought.
- The paper highlights the power of probabilistic reasoning and the ability to reason about complex domains, including in the kinship domain and for physical scene understanding.
- The document explores the integration of language with planning, physical reasoning, and vision, as well as the translation of natural language into probabilistic world models.
- The authors suggest that building probabilistic programming frameworks that incorporate language models can lead to more sophisticated AI systems and discuss the implications of their framework for AI, emphasizing verifiability, robustness, and trustworthiness in language understanding systems.
- Future directions for research include exploring the role of probabilistic programming in language understanding and developing techniques for controlling generation in language models.
Summaries
593 word summary
The document aims to situate developments in large language models within a broader cognitive perspective. It proposes a computational framework called rational meaning construction that integrates neural models of language with probabilistic models for flexible commonsense reasoning. The document emphasizes the theoretical distinction between language and thought and addresses the challenges faced by modular and symbolic models of language and thought. It discusses the scale and scope of distributional vision in relation to language models. The paper proposes a framework called rational meaning construction that integrates language and thought. The document also presents a proposal for a general framework that combines language and probabilistic reasoning to understand and reason about the world. The excerpt discusses the translation of natural language into probabilistic world models, including in the kinship domain and for physical scene understanding. It highlights the power of probabilistic reasoning and the ability to reason about complex domains. The text also discusses the implementation of a discrete event semantics and the use of generative models to represent physical scenes. It mentions the integration of language with planning and physical reasoning. The document discusses the translation of natural language into probabilistic world models and demonstrates how language can be grounded in physical reasoning and probabilistic inference. It discusses the relationship between language, inference, and perception and provides examples of how language can be used to reason about physical events and describe scenes involving objects with specific properties. The document also discusses the translation of language to program expressions and the integration of linguistic knowledge with vision. The excerpt includes dialogue, queries, and explanations about agents' preferences, actions, goals, and planning. It mentions the use of generative models, probabilistic forward and inverse inferences, as well as the relationship between language and other cognitive modules. The text also refers to visual reasoning, physical scenes, and the implementation of a physics engine. Learning new concepts and world models from language is discussed as a powerful tool for structuring learning. The document emphasizes the importance of a structured domain model and the meanings of sentences. It discusses how language can construct, describe, and drive inferences about scenes, physics, and agents' goals and plans. The document also explores the integration of planning approaches and probabilistic language of thought. It highlights the ability of language to reason about agents, their plans, actions, and preferences. The document discusses the translation of language using language-program distributions and the grounding of vague terms and quantifiers in context-specific conditions. The integration of a model-based planner is mentioned to ground the basic ways of talking about agents in probabilistic language. The document concludes by discussing The document discusses the translation of natural language to probabilistic world models and the challenges and potential applications of this process. It explores the use of neural models in translating sentences into probabilistic programs and the need to balance fidelity to knowledge about the world with relevance to specific problems. The document emphasizes the importance of scaling the framework and incorporating the full spectrum of experiences in the world. It also highlights the parallels between language and thought in the human brain and proposes a semantic framework for modeling language acquisition and processing. The authors suggest that building probabilistic programming frameworks that incorporate language models can lead to more sophisticated AI systems. They also discuss the implications of their framework for AI, emphasizing verifiability, robustness, and trustworthiness in language understanding systems. The document concludes by mentioning future directions for research, including exploring the role of probabilistic programming in language understanding and developing techniques for controlling generation in language models.
3381 word summary
The document discusses the translation of natural language to probabilistic world models. It mentions the use of code language models (LLMs) and their potential for generating and editing code based on natural language instructions. The text highlights recent advances in language-guided code editing and the potential for iterative growth and repair of domain theories. It also mentions the use of OpenAI's InstructGPT models for code editing and the combination of reinforcement learning and fine-tuning to improve adherence to human-authored instructions. The document provides examples of translated code snippets in various domains, such as navigation, visual scenes, kinship, and tug-of-war. It mentions the challenge of integrating utterances that bend or break the rules of domain theories. The document also discusses syntactic bootstrapping for language-to-code models and raises open questions regarding code editing. Finally, it presents a generative domain theory for the restaurant navigation domain and includes translations for social reasoning examples. The document excerpt includes code snippets for translating natural language to probabilistic world models. The code snippets involve functions and definitions related to gridworlds, value functions, utility functions, motion utilities, and food utilities. It also includes code related to generating scenes and events in the physics domain. The code snippets are organized into separate paragraphs for distinct ideas. The text excerpt is a code block that defines functions and generates scenes for a generative domain theory of tabletop scenes. It includes functions for filtering objects based on shape and color, generating objects in a scene, and choosing attributes for objects such as shape and color. The code also includes predefined color values for yellow, green, blue, and red. The functions are used to generate prompt examples for translation tasks in the visual domain. The document discusses translating natural language into probabilistic world models. It focuses on the interplay between perceptual and physical reasoning in Bayesian inference and human cognition. The use of generative world models for static visual scenes is highlighted. The limitations of using Prolog for deductive and inductive inferences are discussed, along with the need for incorporating and trading off uncertainty in world models. The document also provides utility functions and code blocks for kinship trees. Examples of translation prompts are given, emphasizing the use of conditions and queries. The excerpt is from a document titled "Translating Natural Language to Probabilistic World Models." It contains various code blocks and statements related to translating natural language into probabilistic world models. The main ideas in the excerpt include the use of condition and query statements, the translation of user-defined statements, and examples related to tug-of-war. The document also includes references and appendices with additional information. The summary includes a list of references from various sources. The summary is organized into separate paragraphs to distinguish distinct ideas.
Paragraph 1: The text excerpt includes various references to different sources such as papers, articles, and preprints. It mentions authors like Mikolov, Sutskever, Chen, Corrado, Dean, Menenti, Gierhan, Segaert, Hagoort, McDermott, McCarthy, Marcus, Davis, Aaronson, Mansinghka, Schaechtle, Handa, Radul, Chen, Rinard, Maynez, Narayan, Bohnet, McDonald, Mahowald, Ivanova, Blank, Kanwisher, Tenenbaum, MacSweeney, Woll, Campbell, McGuire, David, Williams, Brammer, Lyu, Havaldar, Stein, Zhang, Rao, Wong, Callison-Burch, Liu, Wei, Gu, Wu, Vosoughi, Cui, Dai, Ning, Teng, Zhou, Zhang, Biswas, Stone, Lipkin, Wong, Grand, Luria, Tsvetkova, Futer, Lowie, Liu, Jiang, Zhang, Biswas, Stone, Linzen.
Paragraph 2: It also mentions topics related to natural language processing and probabilistic programming such as translating natural language to probabilistic world models, faithfulness and factuality in abstractive summarization, Bayesian inference with incomplete knowledge, probabilistic models of larval zebrafish behavior.
Paragraph 3: Additionally, it includes discussions on cognitive science and human cognition such as dissociating language and thought in large language models from a cognitive perspective, resource-rational analysis of human cognition as the optimal use of limited computational resources.
Paragraph 4: Other topics mentioned in the text excerpt include neural systems underlying British Sign Language and audio-visual English processing in native users, modeling human ad hoc coordination.
Paragraph 5: Lastly, it refers to specific methodologies and techniques such as hierarchical task and motion planning, Bayesian filtering with multiple internal models, structure compilation for trading structure for features, learning executable semantic parsers for natural language understanding, data recombination for neural semantic parsing.
Please note that the summary is based on the provided text excerpt and does not include any additional information. The summary includes multiple references and citations from various sources. It is difficult to discern the main ideas and key points due to the lack of context and organization. The authors would like to acknowledge the support they received from various sources, including grants and collaborations. They express gratitude to their collaborators and colleagues who provided valuable feedback on the manuscript. The authors also mention specific individuals who made significant contributions to the development of the manuscript. The excerpt discusses the importance of language in understanding and reasoning, and proposes a framework for translating natural language into probabilistic world models. The authors emphasize the need for cognitive and AI models that can reliably understand and interpret human language. They suggest that language models should be built on a substrate for thinking and cognition, allowing for more efficient learning and better control over language understanding. The authors also highlight the significance of language in our cognition and propose that AI models should capture the cognitive theory of human language. They discuss the potential for AI models to produce new language and even construct new languages. The framework presented in the paper aims to capture the relationship between language and thought, and to build AI models that can reliably understand and explain language. The authors suggest that data-efficient training methods, inspired by human language acquisition, can improve the performance of language models. They propose using simpler models to bootstrap more complex ones, and incorporating constrained translation objectives for initializing complex models. The authors also discuss the implications of their framework for AI, emphasizing the importance of verifiability, robustness, and trustworthiness in language understanding systems. They suggest that explicit world models and symbolic languages can improve interpretability and control over language models. The excerpt concludes by mentioning future directions for research, including exploring the role of probabilistic programming in language understanding and developing techniques for harnessing and controlling generation in language models. The text excerpt discusses the integration of language models with probabilistic world models and the implications for AI and cognitive science. It suggests that building probabilistic programming frameworks that incorporate language models can lead to more sophisticated implementations of AI systems. The text also highlights the importance of a unified framework for expressing world models and the need to incorporate probabilistic inference in language-guided thinking. It explores the parallels between language and thought in the human brain and the potential for resource-rational approximate inference models. Additionally, it discusses the possibility of using large language-to-code models to extract syntactic patterns and bootstrap language acquisition. The text emphasizes the role of structured mappings between language and underlying thoughts in efficient communication and proposes a semantic framework for modeling language acquisition and other aspects of language processing. The excerpt discusses the need for a model that can discover structural regularities in human languages and learn language in a more sample-efficient way. It explores the idea of using probabilistic symbolic models of meaning and how they can be implemented and composed with other models. The text also mentions the challenges of translating utterances into formal meaning representations and the potential for neurosymbolic models in language generation. It suggests using data-driven proposals and amortization in meaning construction and problem-solving. The excerpt highlights the need to scale probabilistic inference and the challenges in scaling models of rational meaning construction. It also mentions the potential of game engines and dynamic world model synthesis in understanding common-sense background knowledge. The text concludes by discussing the bridging of probabilistic programs and the approximation of globally coherent inferences. Recent research has focused on synthesizing and editing probabilistic programs using natural language. Progress has been made in language-level support for dynamic world modeling. Most probabilistic programming languages were designed for inference in a single world model, but it remains unclear how to select relevant parts for reasoning about a specific problem. There are proposals for addressing this issue in causal, probabilistic reasoning. The challenge lies in balancing tradeoffs between fidelity to knowledge about the world, relevance to the problem at hand, and the efficiency and robustness of inference. The ability to craft bespoke world models and understand language about the world without explicit instruction is an important question. Neural models can be used to translate sentences into probabilistic programs, demonstrating the potential for generating programmatic world models. Scaling the framework requires addressing challenges in modeling language, reasoning, and their interaction, as well as automating the process of building meaning representations for new domains. Open questions include how systems can remember and expand on prior world models, incorporate the full spectrum of experiences in the world, and generalize to new kinds of language and thinking. The ability to construct new world models from language can be accomplished by translating each sentence into code defining the model. This process involves constructing the domain model from program expressions and growing world models by interpreting observations and queries. The meaning of sentences can be interpreted as distributions over possible definitions of variables and concepts. The framework offers a roadmap for progress in modeling language, reasoning, and their interaction, with future directions focused on scaling the framework and incorporating the full spectrum of experiences in the world. In this document, the authors discuss the process of translating natural language into probabilistic world models. They explore how language can be used to define new domain models and extend existing ones. The authors emphasize the importance of understanding language in the context of a particular world model. They propose a framework that integrates language, meanings, and reasoning about new observations. The framework involves constructing new world models from language and using probabilistic programming for inference. The authors provide examples of how concepts can be learned from language and incorporated into world models. They also discuss the role of language in constructing programs and extending domain models. The authors highlight the flexibility and power of their framework in capturing the ability to learn new concepts and reason about the world. Learning new concepts and world models from language is a powerful tool for structuring learning. Language allows us to define new concepts, domains, and interrelated conceptual systems. Human communication involves teaching each other new concepts and domain theories. To scale beyond hand-coded knowledge, a structured domain model and the meanings of sentences are needed. Generative world modeling programs provide a flexible and powerful scaffolding for language understanding. Language can construct, describe, and drive inferences about scenes, physics, and agents' goals and plans. Planning approaches can be nested within and integrated into a probabilistic language of thought. Language can be used to reason about agents, their plans, and actions. It can also express preferences and derive inferences about agents' behavior. Language can be translated using language-program distributions to explain someone's actions. The probabilistic generative model can ground vague terms and quantifiers in context-specific conditions. The model can also infer reasonable utility thresholds and generalize to new preference words. By augmenting the generative model with a planner, we can ground the basic ways we talk about agents in probabilistic language. The generative model can express preferences, available actions, and the conditions of the environment. It can model background states and observations about agents' goals and actions. Integrating a model-based planner allows the generative model to express a prior on how agents will act based on their preferences. The excerpted text is a mixture of dialogue, queries, and explanations from a document discussing the translation of natural language to probabilistic world models. The important details include discussions about agents' preferences for restaurants, their actions and goals, and the integration of language with planning and physical reasoning. The text also mentions the use of generative models, probabilistic forward and inverse inferences, and the relationship between language and other cognitive modules. Additionally, there are references to visual reasoning, physical scenes, and the potential integration of linguistic knowledge with vision. The document also briefly mentions the implementation of a physics engine and the translation of language to program expressions. In the document "Translating Natural Language to Probabilistic World Models," the authors discuss the implementation of a discrete event semantics using a physics engine to simulate collisions between objects. They demonstrate how language can be translated into conditions and queries that capture distributions over physical properties and events in a world model. The generative model integrates language with a physics simulation engine, allowing for reasoning about physical scenes based on natural language descriptions. The approach is related to other work in the AI literature that explores the connection between language, inference, and perception. The authors provide examples of how language can be used to reason about physical events and describe scenes involving objects with specific properties. They also discuss the use of a physics simulation engine and graphics rendering engine to visualize and simulate scenes in a generative world model. Overall, the approach demonstrates how language can be grounded in physical reasoning and probabilistic inference. The excerpt discusses the translation of natural language into probabilistic world models. It highlights the ability of language models to make inferences and predict meanings based on context-specific priors. The implementation uses a generative model to represent scene states and a rendering engine to visualize the scenes. The translated text focuses on the key points of the approach, omitting unnecessary details and unrelated information. This excerpt discusses the use of probabilistic inference to recover the physical content of scenes from vision. It highlights the relationship between visual imagination and visual scene understanding, as well as the approach of modeling language and visual reasoning. The text also mentions the struggle of large models with abstract relational concepts in language and the need for integrating external engines for perception and physical reasoning. It explores the use of ProbLog for representing generative models and logical reasoning, and discusses the challenges and future directions in reasoning with language models. Additionally, it explains how language can be translated into program expressions for reasoning about kinship relations in a structured domain. The excerpt concludes by emphasizing the role of language in shaping our intuitive theories of complex domains and the need for incorporating social and cultural nuance in models. The excerpt discusses the translation of natural language into probabilistic world models, specifically focusing on a kinship domain. The model aims to capture the nuances and complexities of kinship relations and reasoning. It utilizes a generative model in Church, a probabilistic programming language, to represent family trees and simulate various kinship events such as births, partnerships, and children. The model incorporates both deductive and inductive reasoning, allowing for the inference of relationships based on sparse evidence. The framework demonstrates how natural language can be translated into a probabilistic language of thought, enabling rich inferences and reasoning. The approach can be extended to other domains and provides a foundation for language understanding and reasoning in computational cognitive models. The text discusses the translation of natural language into probabilistic world models. It introduces the concepts of faculty-team and student-team and highlights the ability to define and reference new concepts symbolically. The text also mentions the use of the define construct, amortization examples, and context-driven amortizations. It discusses the possibility of using thresholds for categorization and the importance of grounding utterances into appropriate condition and query statements. The text emphasizes the flexibility of the framework in modeling complex reasoning from language and highlights the power of probabilistic reasoning. It explains the process of translating language utterances into Church code and demonstrates the use of LLMs for broad-coverage exposure to natural language. The text also discusses the reasoning process and the ability to obtain a posterior distribution over values of interest. It provides translation examples and explains the use of observations, condition statements, and query statements in the framework. The text concludes by mentioning the potential of probabilistic reasoning in capturing human-like intuitions about latent traits. The excerpt is from a document discussing the translation of natural language into probabilistic world models. The focus is on reasoning from language given a pre-specified world model and how language can be used to grow and construct new world models. The document proposes a rational meaning construction framework that integrates language and probabilistic reasoning. It suggests that large language models can be used to map between language and code, allowing for broad-coverage mappings between human sentences and meanings. The framework aims to model how meanings drive inferences about what is true and provides a general substrate for representing, thinking about, and receiving new information about the world. It also discusses the potential integration of language models with structured reasoning engines and symbolic reasoning tools. Overall, the document presents a proposal for a general framework that combines language and probabilistic reasoning to understand and reason about the world. This proposal presents a framework for translating natural language into probabilistic world models. It formalizes rational meaning construction and emphasizes the relationship between language and thought. The framework involves modeling language relative to thought and implementing thinking using probabilistic programs. It aims to understand and model the cognitive and computational structures underlying human scaling route to intelligence and language use. The framework also highlights the importance of resource-rational agents that allocate computational resources efficiently. It considers language as a system of goal-directed actions and proposes a unified framework for translating language into code in various domains of reasoning. The proposed framework offers a powerful tool for driving and constructing new thought. This paper proposes a framework called rational meaning construction that integrates language and thought. It suggests that language can be modeled as translation from utterances in language to expressions in a general probabilistic programming language (PPL). The framework decomposes language-informed thinking into two modules: a meaning function that translates natural language into PPL statements, and an inference function that computes probabilities over possible worlds consistent with the information in the language. The paper explores how this framework can support flexible inferences and coherent reasoning. It discusses the challenges of scaling language models and presents the potential of integrating large language models (LLMs) with probabilistic programming languages (PPLs) to achieve robust and systematic reasoning. The paper also highlights the need for further research and development to address the scalability challenges and bridge the gap between LLMs and general cognitive models. The document discusses the scale and scope of distributional vision in today's world, particularly in relation to language models. It highlights the use of large language models (LLMs) that utilize deep neural networks to learn probabilistic distributions of words from vast datasets. These models follow the tradition of distributional approaches and aim to extract representations of meaning from statistical patterns in word usage.
The document also addresses the challenges faced by modular and symbolic models of language and thought, including their scalability and limited scope. It mentions the need for mapping from sentences to symbolic representations and the limitations of hand-engineered mapping functions. Semantic parsing systems, which aim to map human language into symbolic representations, have also faced challenges regarding their generalizability and scalability.
Furthermore, the document emphasizes the theoretical distinction between language and thought, with evidence suggesting separate but interconnected brain systems for each. It discusses how infants possess a toolkit for modeling and thinking about the world before learning language. The relationship between language and thought is explored, along with the question of how language informs and supports various aspects of human cognition.
The paper proposes a computational framework called rational meaning construction, which integrates neural models of language with probabilistic models for flexible commonsense reasoning. It suggests that a theory of linguistic meaning can be leveraged to build machines that think in more human-like ways. The framework utilizes probabilistic programming and generative world modeling, providing a unified interface for commonsense thinking. Examples are provided to illustrate the framework's capabilities in probabilistic reasoning, logical reasoning, visual and physical reasoning, and social reasoning.
Overall, the document aims to situate developments in large language models within a broader cognitive perspective, offering insights from both modern and classical computational approaches. It seeks to provide a roadmap towards AI systems that synthesize language and intelligence effectively.