An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face

2026-04-07

AI Summary

This study addresses a critical gap in understanding user-perceived experiences with general-purpose large language models (GLLMs) and multimodal large language models (MLLMs) in real-world settings. Drawing on 662 open community discussions from Hugging Face covering 38 representative models, the authors employ manual annotation and qualitative content analysis to develop a three-tiered taxonomy that systematically categorizes user feedback. Their analysis uncovers five core challenges: accessibility barriers, generation quality issues, deployment complexity, usability limitations, and documentation deficiencies. Based on these findings, the paper offers actionable recommendations to enhance the large model ecosystem, providing empirical grounding for future model design and optimization efforts.

Abstract

Large language models (LLMs) have rapidly evolved from general-purpose systems to multimodal models capable of processing text, images, and audio. As both general-purpose LLMs (GLLMs) and multimodal LLMs (MLLMs) gain widespread adoption, understanding user perceptions in real-world settings becomes increasingly important. However, existing studies often rely on surveys or platform-specific data (e.g., Reddit or GitHub issues), which either constrain user feedback through predefined questions or overemphasize failure-driven, debugging-oriented discussions, thus failing to capture diverse, experience-driven, and cross-model user perspectives in practice. To address this issue, we conduct an empirical study of user discussions on Hugging Face, a major model hub with diverse models and active communities. We collect and manually annotate 662 discussion threads from 38 representative models (21 GLLMs and 17 MLLMs), and develop a three-level taxonomy to systematically characterize user concerns. Our analysis reveals that LLM access barriers, generation quality, and deployment and invocation complexity are the most prominent concerns, alongside issues such as documentation limitations and resource constraints. Based on these findings, we derive actionable implications for improving LLM ecosystem.

Problem

• user perception
• large language models
• multimodal LLMs
• empirical study
• Hugging Face

Innovation

• empirical study
• user perception
• multimodal LLMs
• Hugging Face
• taxonomy