Introduction Since OpenAI launched ChatGPT, a large language model (LLM) based chatbot, in 2023, it has set off a technological wave. These models, capable of understanding and generating human-like text, can adapt to various conversational contexts and address questions across a broad range of topics. They even simulate human actions, revolutionising the way humans interact with machines.

Author Dhruv Gupta ( ORCID : 0009–0004–7109–5403) Introduction Large Language Models (LLMs) have become the new face of Natural language processing (NLP). With their generative power and ability to comprehend human language, the human reliance on these models is increasing every day. However, the LLMs have been known to hallucinate and thus produce wrong outputs.

Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships between them. They have been employed across numerous domains, like retail, healthcare, and search engines. However, one critical factor limiting the usage of KGs is the difficult and costly

Introduction Data tagging, in simple terms, is the process of assigning labels or tags to your data so that they are easier to retrieve or analyse. For example, when you are dealing with a database consisting of scientific journals, you may want to tag these documents with their relevant topics so that users can later easily find the journal they are interested in using a filter button without too much effort.

An improvement architecture superior to the Transformer, proposed by Meta Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction Recently, researchers from Meta and the University of Southern California have introduced a model called Megalodon. They claim that this model can expand the context window of language models to handle millions of tokens without overwhelming your memory.

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Attention mechanism not getting enough attention Author Dhruv Gupta ( ORCID : 0009–0004–7109–5403) Introduction As discussed in this article, RNNs were incapable of learning long-term dependencies. To solve this issue both LSTMs and GRUs were introduced. However, even though LSTMs and GRUs did a fairly decent job for textual data they did not perform well.

Introduction Large Language Models (LLMs), usually trained with extensive text data, can demonstrate remarkable capabilities in handling various tasks with state-of-the-art performance. However, people nowadays typically want something more personalised instead of a general solution. For example, one may want LLMs to assist in code writing while the other may seek models that are specialised in medical knowledge.

Introduction In recent years, fake news has become an increasing concern for many, and for good reason. Newspapers, which we once trusted to deliver credible news through accountable journalists, are vanishing en masse along with their writers. Updates about events and happenings around the world spread faster through social media than journalists can report, and the Internet’s nature is that anyone can post anything.

Introduction Large Language Models (LLMs) have achieved remarkable success. But, they still face significant limitations, especially in domain-specific or knowledge-intensive tasks such as question answering, producing “hallucinations” where the models generate responses that sound plausible but are actually incorrect when handling queries beyond their training data or requiring current information.