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arXiv · 2310.12418

The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions

Siru Ouyang·Shuohang Wang·Yang Liu·Ming Zhong·Yizhu Jiao·Dan Iter·Reid Pryzant·Chenguang Zhu·Heng Ji·Jiawei Han
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Abstract

Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the existing focus of NLP research accurately captures the genuine requirements of human users. This paper provides a comprehensive analysis of the divergence between current NLP research and the needs of real-world NLP applications via a large-scale collection of user-GPT conversations. We analyze a large-scale collection of real user queries to GPT. We compare these queries against existing NLP benchmark tasks and identify a significant gap between the tasks that users frequently request from LLMs and the tasks that are commonly studied in academic research. For example, we find that tasks such as ``design'' and ``planning'' are prevalent in user interactions but are largely neglected or different from traditional NLP benchmarks. We investigate these overlooked tasks, dissect the practical challenges they pose, and provide insights toward a roadmap to make LLMs better aligned with user needs.

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Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han. 2023-10-19. The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions. https://arxiv.org/abs/2310.12418

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