Related papers: Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning

Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning

URL: http://arxiv.org/abs/2312.05230v1
Date: Fri, 8 Dec 2023 18:25:22 GMT
Title: Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning
Authors: Zhiting Hu, Tianmin Shu
Abstract summary: We present a new perspective of machine reasoning, LAW, that connects the concepts of Language models, Agent models, and World models. World and agent models are a better abstraction of reasoning, that introduces the crucial elements of deliberate human-like reasoning.
Score: 33.573628038590634
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Despite their tremendous success in many applications, large language models often fall short of consistent reasoning and planning in various (language, embodied, and social) scenarios, due to inherent limitations in their inference, learning, and modeling capabilities. In this position paper, we present a new perspective of machine reasoning, LAW, that connects the concepts of Language models, Agent models, and World models, for more robust and versatile reasoning capabilities. In particular, we propose that world and agent models are a better abstraction of reasoning, that introduces the crucial elements of deliberate human-like reasoning, including beliefs about the world and other agents, anticipation of consequences, goals/rewards, and strategic planning. Crucially, language models in LAW serve as a backend to implement the system or its elements and hence provide the computational power and adaptability. We review the recent studies that have made relevant progress and discuss future research directions towards operationalizing the LAW framework.

Related papers

The Sociolinguistic Foundations of Language Modeling [34.02231580843069]
We argue that large language models are inherently models of varieties of language. We discuss how this perspective can help address five basic challenges in language modeling.
arXiv Detail & Related papers (2024-07-12T13:12:55Z)
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
arXiv Detail & Related papers (2023-09-29T17:57:00Z)
Statler: State-Maintaining Language Models for Embodied Reasoning [19.884696137429813]
We propose Statler, a framework in which large language models are prompted to maintain an estimate of the world state. Our framework then conditions each action on the estimate of the current world state. It significantly outperforms strong competing methods on several robot planning tasks.
arXiv Detail & Related papers (2023-06-30T17:58:02Z)
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)
Improving Factuality and Reasoning in Language Models through Multiagent Debate [95.10641301155232]
We present a complementary approach to improve language responses where multiple language model instances propose and debate their individual responses and reasoning processes over multiple rounds to arrive at a common final answer. Our findings indicate that this approach significantly enhances mathematical and strategic reasoning across a number of tasks. Our approach may be directly applied to existing black-box models and uses identical procedure and prompts for all tasks we investigate.
arXiv Detail & Related papers (2023-05-23T17:55:11Z)
A Survey of Large Language Models [81.06947636926638]
Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
arXiv Detail & Related papers (2023-03-31T17:28:46Z)
Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
arXiv Detail & Related papers (2023-03-07T18:44:07Z)
On the Universality of Deep COntextual Language Models [15.218264849664715]
Deep Contextual Language Models (LMs) like ELMO, BERT, and their successors dominate the landscape of Natural Language Processing. Multilingual versions of such models like XLM-R and mBERT have given promising results in zero-shot cross-lingual transfer. Due to this initial success, pre-trained models are being used as Universal Language Models'
arXiv Detail & Related papers (2021-09-15T08:00:33Z)

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