2025
LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval
EMNLP 2025
Materials science research requires multi-step reasoning and precise material informatics retrieval, where minor errors can propagate into significant failures in downstream experiments. Despite their general success, Large Language Models (LLMs) often struggle with hallucinations, handling domain-s