Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

Authors

  • Christopher Toukmaji University of California, Irvine
  • Allison Tee Stanford University

DOI:

https://doi.org/10.1609/aaaiss.v3i1.31210

Keywords:

Retrieval-Augmented Generation, Biomimicry, Large Language Model Agents, LLMs

Abstract

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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Published

2024-05-20

Issue

Section

Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge