1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Towards Domain Specification of Embedding Models in Medicine
Mohammad Khodadad, Ali Shiraee Kasmaee, Mahdi Astaraki +1
Medical text embedding models are foundational to a wide array of healthcare applications, ranging from clinical decision support and biomedical information retrieval to medical qu…
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…