Summary of Exploring Human-ai Perception Alignment in Sensory Experiences: Do Llms Understand Textile Hand?, by Shu Zhong et al.
Exploring Human-AI Perception Alignment in Sensory Experiences: Do LLMs Understand Textile Hand?
by Shu Zhong, Elia Gatti, Youngjun Cho, Marianna Obrist
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how well large language models (LLMs) align with human experiences related to touch. The researchers created an interaction where participants handled two textile samples without seeing them, then described the differences between them to the LLM. The LLM attempted to identify the target textile by assessing similarity in its high-dimensional embedding space. While a degree of perceptual alignment exists, it varies significantly among different textile samples. For example, LLM predictions are well aligned for silk satin but not for cotton denim. This study paves the way for future research on human-AI perceptual alignment and its potential benefits for everyday tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers understand what we feel when we touch things. The researchers wanted to see if large language models can match our feelings when we handle different textures like silk or denim. They asked people to describe the differences between two fabrics without looking at them, and then used this information to try to guess which one was which. The results show that there is some connection between what humans feel and what computers understand, but it’s not perfect. Some fabrics, like silk satin, are easier for computers to understand than others, like cotton denim. This study helps us learn more about how humans and computers can work together better. |
Keywords
» Artificial intelligence » Alignment » Embedding space
