Introduction Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code. Despite their utility, challenges including the misuse of these technologies for generating harmful content, such as racial discrimination or spreading misinformation, have arisen.

Transformative Advances in Language Models through External Knowledge Integration Author: Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex Author: Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering Author: Wenyi Pi (ORCID: 0009-0002-2884-2771 ) When searching for information on the website, it is common to come across a flood of

Introduction When searching for information on the website, it is common to come across a flood of both relevant and irrelevant results. In that case, how can people discern the required information? This article explores and utilises the integration of knowledge-enhanced pre-trained language models (KE-PLMs) and Knowledge Graphs to advance open-domain conversational question answering.

Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications.

Introduction Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s. Typically, QA tasks aim to answer questions in natural language based on large-scale unstructured passages such as Wikipedia.

Efficient creation of a stoplight report with data dashboard images Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

Introduction Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process. This article introduces how to use GPT-4V to compare two data dashboards quickly and accurately.

Introduction Virtual assistants have popped up on numerous websites over the years. These assistants utilise question answering (QA) systems, which are gaining traction as we continue to automate response generation, thereby improving QA applications like search engines and chatbots. As the name implies, QA involves users posing queries to machines which in turn, provide relevant, well-crafted answers.

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph Author Qingqin Fang (ORCID: 0009–0003–5348–4264) Introduction Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.