GPT-based Models Meet Simulation; Survey on ChatGPT And Beyond; Transformer Architecture Of GPT Models; and More.

Editor's Paper Recommendations

Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion: Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods; that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications. To alleviate this challenge, we present a novel LR-GCN framework that automatically captures valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC. The proposed approach comprises two main components: a GNN-based predictor and a reasoning path distiller. The reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges, explicitly compositing long-range dependencies into the predictor. This step also plays an essential role in densifying KGs, effectively alleviating the sparse issue. Furthermore, the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the predictor. These two components are jointly optimized using a well-designed variational EM algorithm. Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.

Deceptive AI Ecosystems: The Case of ChatGPT: ChatGPT, an AI chatbot, has gained popularity for its capability to generate human-like responses. However, this feature carries several risks, most notably due to its deceptive behavior, such as offering users misleading or fabricated information that could further cause ethical issues. To better understand the impact of ChatGPT on our social, cultural, economic, and political interactions, it is crucial to investigate how ChatGPT operates in the real world, where various societal pressures influence its development and deployment. This paper emphasizes the need to study ChatGPT "in the wild" as part of its embedded ecosystem, focusing strongly on user involvement. We examine the ethical challenges stemming from ChatGPT's deceptive human-like interactions and propose a roadmap for developing more transparent and trustworthy chatbots. Central to our approach is the importance of proactive risk assessment and user participation in shaping the future of chatbot technology.

GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Tasks: The disruptive technology provided by large-scale pre-trained language models (LLMs) such as ChatGPT or GPT-4 has received significant attention in several application domains, often with an emphasis on high-level opportunities and concerns. This paper is the first examination regarding using LLMs for scientific simulations. We focus on four modeling and simulation tasks, each assessing LLMs’ expected benefits and limitations while providing practical guidance regarding the steps involved for modelers. The first task is to explain a conceptual model's structure to promote participants' engagement in the modeling process. The second task summarizes simulation outputs so model users can identify a preferred scenario. The third task seeks to broaden accessibility to simulation platforms by conveying the insights of simulation visualizations via text. Finally, the last task evokes the possibility of explaining simulation errors and providing guidance to resolve them.

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond: This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks. We provide discussions and insights into using LLMs from the perspectives of models, data, and downstream tasks. Firstly, we offer an introduction and summary of current GPT- and BERT-style LLMs. Then, we discuss the influence of pre-training, training, and test data. Most importantly, we provide a detailed discussion about the use and non-use cases of large language models for various natural language processing tasks, such as knowledge-intensive tasks, traditional natural language understanding tasks, natural language generation tasks, emergent abilities, and considerations for specific tasks. We present various use and non-use cases to illustrate LLMs' practical applications and limitations in real-world scenarios. We also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice. This comprehensive guide aims to provide researchers and practitioners with valuable insights and best practices for working with LLMs, thereby enabling the successful implementation of these models in a wide range of NLP tasks. A curated list of practical guide resources of LLMs, regularly updated, can be found at {this https URL}.

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