Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913397908439040 |
|---|---|
| author | Kim, Yoonsu Son, Kihoon Kim, Seoyoung Kim, Juho |
| author_facet | Kim, Yoonsu Son, Kihoon Kim, Seoyoung Kim, Juho |
| contents | AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions increasingly involve users specifying desired results for AI systems. In order to support better AI intent alignment, we aim to explore human strategies for intent specification in human-human communication. By studying and comparing human-human and human-LLM communication, we identify key strategies that can be applied to the design of AI systems that are more effective at understanding and aligning with user intent. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05678 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment Kim, Yoonsu Son, Kihoon Kim, Seoyoung Kim, Juho Human-Computer Interaction Computation and Language AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions increasingly involve users specifying desired results for AI systems. In order to support better AI intent alignment, we aim to explore human strategies for intent specification in human-human communication. By studying and comparing human-human and human-LLM communication, we identify key strategies that can be applied to the design of AI systems that are more effective at understanding and aligning with user intent. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems. |
| title | Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2405.05678 |