12 citations · 14 across the 11 of their papers we have counts for
11 papers
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
PDB-Eval: An Evaluation of Large Multimodal Models for Description and Explanation of Personalized Driving Behavior
Junda Wu, Jessica Echterhoff, Kyungtae Han +3
Understanding a driver's behavior and intentions is important for potential risk assessment and early accident prevention. Safety and driver assistance systems can be tailored to i…
Quantifying Cognitive Bias Induction in LLM-Generated Content
Abeer Alessa, Param Somane, Akshaya Lakshminarasimhan +3
Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We invest…
MUSCLE: A Model Update Strategy for Compatible LLM Evolution
Jessica Echterhoff, Fartash Faghri, Raviteja Vemulapalli +4
Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. Within the update process, developers often prioritize…
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…
Cognitive Bias in Decision-Making with LLMs
Jessica Echterhoff, Yao Liu, Abeer Alessa +2
Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks. Given their training on human (created) data, LLMs have be…
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…
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…
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…
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…
PAR: Personal Activity Radius Camera View for Contextual Sensing
Jessica Maria Echterhoff, Edward J. Wang
Contextual sensing using wearable cameras has seen a variety of different camera angles proposed to capture a wide gamut of different visual scenes. In this paper, we propose a new…