Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insights

This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs). Our two-step approach involves data point summarization and hidden state extraction. Initially, data is condensed via summarization using an LLM, reducing complexity and highlighting essential information in sentences. Subsequently, the summarization sentences are fed through another LLM to extract hidden states, serving as compact, feature-rich representations. This approach leverages the advanced comprehension and generative capabilities of LLMs, offering a scalable and efficient strategy for similarity identification across diverse datasets. We demonstrate the effectiveness of our method in identifying similar data points on multiple datasets. Additionally, our approach enables non-technical domain experts, such as fraud investigators or marketing operators, to quickly identify similar data points tailored to specific scenarios, demonstrating its utility in practical applications. In general, our results open new avenues for leveraging LLMs in data analysis across various domains.

Opportunities and challenges of ChatGPT for design knowledge management

Recent advancements in Natural Language Processing have opened up new possibilities for the development of large language models like ChatGPT, which can facilitate knowledge management in the design process by providing designers with access to a vast array of relevant information. However, integrating ChatGPT into the design process also presents new challenges. In this paper, we provide a concise review of the classification and representation of design knowledge, and past efforts to support designers in acquiring knowledge. We analyze the opportunities and challenges that ChatGPT presents for knowledge management in design and propose promising future research directions. A case study is conducted to validate the advantages and drawbacks of ChatGPT, showing that designers can acquire targeted knowledge from various domains, but the quality of the acquired knowledge is highly dependent on the prompt.