The Three Oldest Pillars of NLP Author Dhruv Gupta ( ORCID : 0009–0004–7109–5403) Introduction Natural Language Processing (NLP) has almost become synonymous with Large Language Models (LLMs), Generative AI, and fancy chatbots. With the ever-increasing amount of textual data and exponential growth in computational knowledge, these models are improving every day.
A Unified and Collaborative Framework for LLM Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.
Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.
The Commonmeta standard for scholarly metadata continues towards version 1.0 with some important changes in version v0.14, released this week. And metadata for all DOIs from Crossref and DataCite can now be retrieved in commonmeta format via a new web service.
Latest effort in assessing the security of the code generated by large language models Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) Introduction With the surge of Large Language Models (LLMs) nowadays, there is a rising trend among developers to use Large Language Models to assist their daily code writing. Famous products include GitHub Copilot or simply ChatGPT.
Introduction Alright, folks, let’s dive into the wild world of statistics and data science! Picture this: you’re knee-deep in data, trying to make sense of the chaos.
Previously, I took advantage of a dataset that linked preprints to their published counterparts to look at the fraction of papers in a journal that are preprinted. This linkage can be used to answer other interesting questions. Such as: when do authors preprint their papers relative to submission? And does this differ by journal? There’s a bit of preamble. If you just want to know the answer, click here.
The Rogue Scholar science blog archive is built with Open Source software, starting with Javascript in June 2023 and adding an API written in Python in October 2023. This week I am releasing the first version of commonmeta written in Go to simplify the Rogue Scholar backend.
Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram
Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution Author · Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest.