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

Understand

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.