3 papers
cs.CL2025
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…
cs.IR2025
ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings
Ali Shiraee Kasmaee, Mohammad Khodadad, Mehdi Astaraki +4
Retrieval-Augmented Generation (RAG) systems in chemistry heavily depend on accurate and relevant retrieval of chemical literature. However, general-purpose text embedding models f…
cs.CL2025
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…