8 citations · 14 across the 6 of their papers we have counts for
6 papers
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
Balkrishna Giri, Md Toufique Hasan, Jussi Rasku +2
Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Rel…
Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning
Noor Islam S. Mohammad, Mahmudul Hasan
Large language models (LLMs) deployed for logical reasoning in knowledge-intensive domains exhibit a subtle but critical failure: coherence can be vacuously achieved through system…
Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study
Md. Toufique Hasan, Ayman Asad Khan, Mika Saari +2
Large language models show promise for knowledge-intensive domains, yet their use in agriculture is constrained by weak grounding, English-centric training data, and limited real-w…
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell +3
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accu…
TimeLess: A Vision for the Next Generation of Software Development
Zeeshan Rasheed, Malik Abdul Sami, Jussi Rasku +16
Present-day software development faces three major challenges: complexity, time consumption, and high costs. Developing large software systems often requires battalions of teams an…
Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
Ayman Asad Khan, Md Toufique Hasan, Kai Kristian Kemell +2
This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture com…