DOE OSTI · code-170337
LLaMP v0.1.0
Abstract
Reducing hallucination of Large Language Models (LLMs) is imperative for use in the sciences, where reliability and reproducibility are crucial. However, LLMs inherently lack long-term memory, making it a nontrivial, ad hoc, and inevitably biased task to fine-tune them on domain-specific literature and data. LLaMP is a multimodal retrieval-augmented generation (RAG) framework of hierarchical reasoning and acting (ReAct) agents that can dynamically and recursively interact with Materials Project to ground large language models on high-fidelity materials informatics.
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Riebesell, Janosh [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Chiang, Yuan [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Chou, Chia-Hong [Chia-Hong Chou], Hsieh, Haw-Ting [Haw-Ting Hsieh]. 2025-04-24. LLaMP v0.1.0. https://doi.org/10.11578/dc.20251118.3
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