Reducing hallucination of Large Language Models (LLMs) is imperative for use
in the sciences where reproducibility is 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. Here we introduce LLaMP,
a multimodal retrieval-augmented generation (RAG) framework of multiple
data-aware reasoning-and-acting (ReAct) agents that dynamically interact with
computational and experimental data on Materials Project (MP). Without
fine-tuning, LLaMP demonstrates an ability to comprehend and integrate various
modalities of materials science concepts, fetch relevant data stores on the
fly, process higher-order data (such as crystal structures and elastic
tensors), and summarize multi-step procedures for solid-state synthesis. We
show that LLaMP effectively corrects errors in GPT-3.5's intrinsic knowledge,
reducing a 5.21% MAPE on frequently-documented bandgaps and a significant
1103.54% MAPE on formation energies -- errors that GPT-3.5 seems to derive from
mixed data sources. Additionally, LLaMP substantially reduces the hallucinated
volumetric strain in a diamond cubic silicon structure from 66.3% to 0. The
proposed framework offers an intuitive and nearly hallucination-free approach
to exploring materials informatics and establishes a pathway for knowledge
distillation and fine-tuning other language models. We envision the framework
as a valuable component for scientific hypotheses and a foundation for future
autonomous laboratories where multiple LLM agents communicate and cooperate
with robotics to drive material synthesis and chemical reactions without
hard-coded human logic and intervention.

在科学领域，LLMs 的幻觉问题是必须解决的，因为可重现性是至关重要的。本文介绍了 LLaMP，这是一个多模态检索增强生成（RAG）框架，由多个数据感知推理和行动（ReAct）代理组成，动态与 Materials Project（MP）上的计算和实验数据交互。LLaMP 在不进行精细调整的情况下，能够理解和整合各种材料科学概念的多模态信息，实时获取相关数据，处理高阶数据（如晶体结构和弹性张量），并对固态合成的多步骤过程进行总结。同时，LLaMP 能有效纠正 GPT-3.5 在频繁文档中的带隙和形成能量方面的错误，并将钻立方硅结构的虚幻体积应变从 66.3％降低到 0。该框架为材料信息学提供了一种直观且几乎无幻觉的方法，并为知识蒸馏和精细调整其他语言模型奠定了基础。我们设想该框架作为科学假设的有价值组成部分，并为未来的自主实验室奠定基础，多个 LLM 代理与机器人通信和协作，驱动材料合成和化学反应，无需硬编码的人类逻辑和干预。