Concept generation is a creative step in the conceptual design phase, where
designers often turn to brainstorming, mindmapping, or crowdsourcing design
ideas to complement their own knowledge of the domain. Recent advances in
natural language processing (NLP) and machine learning (ML) have led to the
rise of Large Language Models (LLMs) capable of generating seemingly creative
outputs from textual prompts. The success of these models has led to their
integration and application across a variety of domains, including art,
entertainment, and other creative work. In this paper, we leverage LLMs to
generate solutions for a set of 12 design problems and compare them to a
baseline of crowdsourced solutions. We evaluate the differences between
generated and crowdsourced design solutions through multiple perspectives,
including human expert evaluations and computational metrics. Expert
evaluations indicate that the LLM-generated solutions have higher average
feasibility and usefulness while the crowdsourced solutions have more novelty.
We experiment with prompt engineering and find that leveraging few-shot
learning can lead to the generation of solutions that are more similar to the
crowdsourced solutions. These findings provide insight into the quality of
design solutions generated with LLMs and begins to evaluate prompt engineering
techniques that could be leveraged by practitioners to generate higher-quality
design solutions synergistically with LLMs.

本论文研究利用自然语言处理和机器学习技术进行概念生成，比较生成方案和众包解决方案的异同，结论表明，利用 LLM 技术生成的方案平均可行性和实用性更高，而众包解决方案更具新颖性。

利用大型语言模型进行概念设计生成

Conceptual Design Generation Using Large Language Models

We present a new method for design problems wherein the goal is to maximize
or specify the value of one or more properties of interest. For example, in
protein design, one may wish to find the protein sequence that maximizes
fluorescence. We assume access to one or more, potentially black box,
stochastic "oracle" predictive functions, each of which maps from input (e.g.,
protein sequences) design space to a distribution over a property of interest
(e.g. protein fluorescence). At first glance, this problem can be framed as one
of optimizing the oracle(s) with respect to the input. However, many
state-of-the-art predictive models, such as neural networks, are known to
suffer from pathologies, especially for data far from the training
distribution. Thus we need to modulate the optimization of the oracle inputs
with prior knowledge about what makes `realistic' inputs (e.g., proteins that
stably fold). Herein, we propose a new method to solve this problem,
Conditioning by Adaptive Sampling, which yields state-of-the-art results on a
protein fluorescence problem, as compared to other recently published
approaches. Formally, our method achieves its success by using model-based
adaptive sampling to estimate the conditional distribution of the input
sequences given the desired properties.

提出一种基于自适应采样的设计问题方法，解决优化预测模型输入时的路径问题和先验知识问题，用于蛋白质设计领域并获得了最先进的结果。