One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929209968951296 |
|---|---|
| author | Clarke, Christopher Krishnamurthy, Karthik Talamonti, Walter Kang, Yiping Tang, Lingjia Mars, Jason |
| author_facet | Clarke, Christopher Krishnamurthy, Karthik Talamonti, Walter Kang, Yiping Tang, Lingjia Mars, Jason |
| contents | Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote "ask me anything" functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI Clarke, Christopher Krishnamurthy, Karthik Talamonti, Walter Kang, Yiping Tang, Lingjia Mars, Jason Human-Computer Interaction Computation and Language Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote "ask me anything" functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers. |
| title | One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2401.07123 |