生物仿生设计解决方案的检索增强生成和 LLM 代理

Christopher Toukmaji, Allison Tee
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摘要

我们推出了生物启发设计与研究助手(BIDARA),以解决生物仿生的复杂性问题--即从生物现象中汲取灵感设计现代工程解决方案的实践。大型语言模型(LLMs)已被证明可以充当通用任务求解器,但在需要特定领域和最新知识的情况下,它们往往会出现幻觉和失败。我们整合了检索增强生成(RAG)和推理与行动代理,以帮助 LLM 在生成生物仿生设计方案时避免幻觉并利用最新知识。我们发现,无论是在提示还是在代理设置中,结合 RAG 都能提高设计方案的可行性。据我们所知,这是第一项在 LLM 生成的仿生设计方案中整合和评估检索增强生成的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions
We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry -- the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general-purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.
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