From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation

Supervised Fine-tuning, Reinforcement Learning from Human Feedback and the latest SteerLM

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.

Latest effort in assessing the security of the code generated by large language models

Understanding the Balance between Internal Knowledge and External Sources Author Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

Neo4j APOC Library Use Case Author Wenyi Pi ( ORCID: 0009–0002–2884–277) Introduction In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

Latest findings in pre-training graphs and using them for link recommendation

Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution

Integrating temporal data into static knowledge graphs