5 citations · 6 across the 6 of their papers we have counts for
6 papers
LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems
Arijit Khan, Longxu Sun, Xin Huang
Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelli…
A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Uses
Yuanming Zhang, Yan Lin, Arijit Khan +1
We compile 129 public LLM prompt datasets with more than 1.22TB and more than 673M instances and organize them into a unified taxonomy. We use seven datasets for detailed analysis…
NAEx: A Plug-and-Play Framework for Explaining Network Alignment
Shruti Saxena, Arijit Khan, Joydeep Chandra
Network alignment (NA) identifies corresponding nodes across multiple networks, with applications in domains like social networks, co-authorship, and biology. Despite advances in a…
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models
Feiyang Li, Peng Fang, Zhan Shi +5
Chain-of-thought (CoT) reasoning boosts large language models' (LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-g…
Logical Consistency of Large Language Models in Fact-checking
Bishwamittra Ghosh, Sarah Hasan, Naheed Anjum Arafat +1
In recent years, large language models (LLMs) have demonstrated significant success in performing varied natural language tasks such as language translation, question-answering, su…
Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly
Arijit Khan
We study distributed graph algorithms that adopt an iterative vertex-centric framework for graph processing, popularized by the Google's Pregel system. Since then, there are severa…