Beyond Turing: Intelligent Agents Centered on the User

By Maxine Eskénazi, Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao. 2019

13 citations recorded by the source corpus.

Most research on intelligent agents centers on the agent and not on the user. We look at the origins of agent-centric research for slot-filling, gaming and chatbot agents. We then argue that it is important to concentrate more on the user. After reviewing relevant literature, some approaches for creating and assessing user-centric systems are proposed.

Subfields: Artificial Intelligence, LLM Agents & Tool Use

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In LLM Agents & Tool Use

Citations in the corpus

Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (2019, background): “Eskenazi et al. (2018) also found that the correctly assessing the appropriateness of chatbot responses is highly dependent on user responses and not preceding context alone.”
Unsupervised Evaluation of Interactive Dialog with DialoGPT (2020, background): “Eskenazi et al. (2019) assessed the quality of a sys-tem utterance in an interactive setting by looking at the following user response .”
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation (2022, background): “Recently, Mehri and Eskenazi (2020); Eskenazi et al. (2019) have shown the effectiveness of looking into the next user utterance as a proxy to measure the quality of the chatbot’s generated responses.”
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (2020, background): “Dialog flow is a usercentric measure of whether a conversation is “going smoothly” (Eskénazi et al., 2019).”