most citedEvaluating Large Language Models as Generative User Simulators for Conversational Recommendation

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.CL20241 cited

Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation

Se-eun Yoon, Zhankui He, Jessica Maria Echterhoff +1

Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems. Large language models show promise in simulating human-like behav…

cs.CV2023

Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving

Jessica Echterhoff, An Yan, Kyungtae Han +3

Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context…

cs.HC2023

Should you make your decisions on a WhIM? Data-Driven Decision making using a What-If Machine for Evaluation of Hypothetical Scenarios

Jessica Maria Echterhoff, Bhaskar Sen, Yifei Ren +1

What-if analysis can be used as a process in data-driven decision making to inspect the behavior of a complex system under some given hypothesis. We propose a What-If Machine that…

cs.HC2023

SpecTracle: Wearable Facial Motion Tracking from Unobtrusive Peripheral Cameras

Yinan Xuan, Varun Viswanath, Sunny Chu +3

Facial motion tracking in head-mounted displays (HMD) has the potential to enable immersive "face-to-face" interaction in a virtual environment. However, current works on facial tr…

cs.CL2023

Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews

Jessica Echterhoff, An Yan, Julian McAuley

It is time-consuming to find the best product among many similar alternatives. Comparative sentences can help to contrast one item from others in a way that highlights important fe…