Advances in apparent conceptual physics reasoning in GPT-4

• URL: http://arxiv.org/abs/2303.17012v3
• Date: Sun, 16 Apr 2023 17:49:14 GMT
• Title: Advances in apparent conceptual physics reasoning in GPT-4
• Authors: Colin G. West
• Abstract summary: ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation. Recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something close to a minimal understanding of Newtonian Mechanics on the Force Concept Inventory. This work replicates those results and also demonstrates that the latest version, GPT-4, has reached a much higher mark in the latter context.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation. Despite lacking any explicit programming regarding the laws of physics, recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something close to a minimal understanding of Newtonian Mechanics on the Force Concept Inventory. This work replicates those results and also demonstrates that the latest version, GPT-4, has reached a much higher mark in the latter context. Indeed, its responses come quite close to perfectly demonstrating expert-level competence, with a few very notable exceptions and limitations. We briefly comment on the implications of this for the future of physics education and pedagogy.

Related papers

• Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation [51.750634349748736]
  Text-to-video (T2V) models have made significant strides in visualizing complex prompts. However, the capacity of these models to accurately represent intuitive physics remains largely unexplored. We introduce PhyGenBench to evaluate physical commonsense correctness in T2V generation.
  arXiv  Detail & Related papers  (2024-10-07T17:56:04Z)
• PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models [58.33913881592706]
  Humans can easily apply their intuitive physics to grasp skillfully and change grasps efficiently, even for objects they have never seen before. This work delves into infusing such physical commonsense reasoning into robotic manipulation. We introduce PhyGrasp, a multimodal large model that leverages inputs from two modalities: natural language and 3D point clouds.
  arXiv  Detail & Related papers  (2024-02-26T18:57:52Z)
• Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding [25.769293445579816]
  This study investigates the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics. Three LLMs, including ChatGPT (GPT-3.5), ChatGPT (GPT-4), and Claude (Claude-2.1) were subjected to evaluation against engineering faculties and students with or without mechanical engineering background. The findings reveal GPT-4's superior performance over the other two LLMs and human cohorts in answering questions across various mechanics topics, except for Continuum Mechanics.
  arXiv  Detail & Related papers  (2024-01-13T19:19:04Z)
• GRASP: A novel benchmark for evaluating language GRounding And Situated Physics understanding in multimodal language models [4.354672867211922]
  This paper presents GRASP, a novel benchmark to evaluate the language grounding and physical understanding capabilities of video-based multimodal large language models (LLMs) We use it to evaluate several state-of-the-art multimodal LLMs. Our evaluation reveals significant shortcomings in the language grounding and intuitive physics capabilities of these models.
  arXiv  Detail & Related papers  (2023-11-15T15:38:28Z)
• X-VoE: Measuring eXplanatory Violation of Expectation in Physical Events [75.94926117990435]
  This study introduces X-VoE, a benchmark dataset to assess AI agents' grasp of intuitive physics. X-VoE establishes a higher bar for the explanatory capacities of intuitive physics models. We present an explanation-based learning system that captures physics dynamics and infers occluded object states.
  arXiv  Detail & Related papers  (2023-08-21T03:28:23Z)
• Sparks of Artificial General Intelligence: Early experiments with GPT-4 [66.1188263570629]
  GPT-4, developed by OpenAI, was trained using an unprecedented scale of compute and data. We demonstrate that GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more. We believe GPT-4 could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.
  arXiv  Detail & Related papers  (2023-03-22T16:51:28Z)
• AI and the FCI: Can ChatGPT Project an Understanding of Introductory Physics? [0.0]
  ChatGPT is a groundbreaking AI interface built on a large language model that was trained on an enormous corpus of human text to emulate human conversation. We present a preliminary analysis of how two versions of ChatGPT fare in the field of first-semester university physics.
  arXiv  Detail & Related papers  (2023-03-02T08:43:11Z)
• Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language [92.7638697243969]
  We propose a unified framework that can jointly learn visual concepts and infer physics models of objects from videos and language. This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.
  arXiv  Detail & Related papers  (2021-10-28T17:59:13Z)
• Lectures on quantum supreme matter [0.0]
  Notes are based on lectures serving the advanced graduate education of the Delta Institute of Theoretical Physics in the Netherlands in autumn 2021. The goal is to explain in a language that can be understood by non-specialists very recent advances in quantum information. The holographic duality discovered in string theory appears to be a mathematical machinery capable of computing observable properties of such matter.
  arXiv  Detail & Related papers  (2021-10-03T09:34:36Z)
• Machine-Learning Non-Conservative Dynamics for New-Physics Detection [69.45430691069974]
  Given a trajectory governed by unknown forces, our Neural New-Physics Detector (NNPhD) aims to detect new physics. We demonstrate that NNPhD successfully discovers new physics by decomposing the force field into conservative and non-conservative components. We also show how NNPhD coupled with an integrator outperforms previous methods for predicting the future of a damped double pendulum.
  arXiv  Detail & Related papers  (2021-05-31T18:00:10Z)

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.