Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, and so on). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign biases: convergent constraints that emerge at LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the mirroring of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human categorical perception in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4.
Differentiate ChatGPT-generated and Human-written Medical Texts
South China University of Technology（华南理工大学）；University of Georgia（佐治亚大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital and Harvard Medical School（马萨诸塞总医院和哈佛医学院）
ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing
University of North Texas（北德克萨斯大学）；Emporia State University（恩波利亚州立大学）；Zhejiang Tongji Vocational College of Science and Technology（浙江同济科技职业学院）；Babasaheb Bhimrao Ambedkar University（巴巴萨海布·比姆拉奥·安贝德卡尔大学）；Baker University（贝克大学）
Advanced large language models like ChatGPT have gained considerable attention recently, including among students. However, while the debate on ChatGPT in academia is making waves, more understanding is needed among lecturers and teachers on how students use and perceive ChatGPT. To address this gap, we analyzed the content on ChatGPT available on TikTok in February 2023. TikTok is a rapidly growing social media platform popular among individuals under 30. Specifically, we analyzed the content of the 100 most popular videos in English tagged with #chatgpt, which collectively garnered over 250 …
University of California, Los Angeles（加州大学洛杉矶分校）；Nanyang Technological University（南洋理工大学）；University of California, Davis（加州大学戴维斯分校）；National University of Singapore（新加坡国立大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
ChatGPT-generated texts show authorship traits that identify them as non-human
Università degli Studi di Pavia（帕维亚大学）；University of Birmingham（伯明翰大学）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高等研究院）
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
University of Waterloo（滑铁卢大学）；Multimodal Art Projection Research Community（多模态艺术投影研究社区）；Vector Institute（矢量研究所）；Waseda University（早稻田大学）；HKUST（香港科技大学）；Ohio State University（俄亥俄州立大学）
Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss
Northeastern University（东北大学）；VA Connecticut Healthcare System（康涅狄格州医疗系统）；Portland Psychotherapy Clinic, Research and Training Center（波特兰心理治疗诊所、研究与培训中心）；University of Washington（华盛顿大学）；The University of Melbourne（墨尔本大学）；Drexel University（德雷塞尔大学）
ELLIS Alicante（阿利坎特ELLIS）；University of South Bohemia（南波希米亚大学）；Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague（布拉格捷克理工大学捷克信息学、机器人与控制论研究所）
Natural language applied to natural 2D images describes a fundamentally 3D world. We present the Voxel-informed Language Grounder (VLG), a language grounding model that leverages 3D geometric information in the form of voxel maps derived from the visual input using a volumetric reconstruction model. We show that VLG significantly improves grounding accuracy on SNARE, an object reference game task. At the time of writing, VLG holds the top place on the SNARE leaderboard, achieving SOTA results with a 2.0% absolute improvement.
