5 papers
When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
Mahdi Astaraki, Mohammad Arshi Saloot, Ali Shiraee Kasmaee +2
Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-reasoning loops meaningfully outpe…
ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings
Ali Shiraee Kasmaee, Mohammad Khodadad, Mehdi Astaraki +5
Retrieval-Augmented Generation (RAG) systems in chemistry heavily depend on accurate and relevant retrieval of chemical literature. However, general-purpose text embedding models f…
Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Case Study
Mohammad Khodadad, Ali Shiraee Kasmaee, Mahdi Astaraki +3
In this study, we introduced a new benchmark consisting of a curated dataset and a defined evaluation process to assess the compositional reasoning capabilities of large language m…
ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance & Efficiency on a Specific Domain
Ali Shiraee Kasmaee, Mohammad Khodadad, Mohammad Arshi Saloot +4
Recent advancements in language models have started a new era of superior information retrieval and content generation, with embedding models playing an important role in optimizin…
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
Mohammad Khodadad, Morteza Rezanejad, Ali Shiraee Kasmaee +3
The analysis of 3D point clouds has diverse applications in robotics, vision and graphics. Processing them presents specific challenges since they are naturally sparse, can vary in…