10 citations · 14 across the 12 of their papers we have counts for
12 papers
Report on The 1st Workshop on Human-Centered Proactive and Personalized Agents for Interactive Information Access at CHIIR 2026
Kirandeep Kaur, Vinayak Gupta, Tanya Roosta +3
Interactive information access is increasingly moving beyond reactive query-response paradigms toward agentic systems that can personalize interaction, retain context, infer latent…
Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective
Xiaoyan Zhao, Yujie Cai, Yang Zhang +2
Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to ground their outputs in extern…
Agents in the Wild: Where Research Meets Deployment
Grace Hui Yang, Pranav N. Venkit, Hooman Sedghamiz +3
Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and other agents are rapidly transitioning from r…
Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
Xubo Lin, Zezhi Deng, Shihao Wang +2
Most existing dialogue systems are user-driven, primarily designed to fulfill user requests. However, in many critical real-world scenarios, a conversational agent must proactively…
YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents
Victor De Lima, Grace Hui Yang
Most conversational agents (CAs) are designed to satisfy user needs through user-driven interactions. However, many real-world settings, such as academic interviewing, judicial pro…
Stability Regimes for Framing-Sensitive Fine-Tuning in Language Models
Victor De Lima, Jiqun Liu, Grace Hui Yang
AI systems increasingly shape how individuals access and evaluate information, intensifying concerns about misinformation. Computational simulation offers a controlled complement t…
Sequencing Matters: A Generate-Retrieve-Generate Model for Building Conversational Agents
Quinn Patwardhan, Grace Hui Yang
This paper contains what the Georgetown InfoSense group has done in regard to solving the challenges presented by TREC iKAT 2023. Our submitted runs outperform the median runs by a…
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval
Sibo Dong, Justin Goldstein, Grace Hui Yang
This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input si…
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
Mingze Wang, Ziyang Zhang, Grace Hui Yang
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL me…
Feature Modulation to Improve Struggle Detection in Web Search: A Psychological Approach
Jiyun Luo, Yan Yang, Valerie Nayak +1
Searcher struggle is important feedback to Web search engines. Existing Web search struggle detection methods rely on effort-based features to identify the struggling moments. Thei…
High-Quality Diversification for Task-Oriented Dialogue Systems
Zhiwen Tang, Hrishikesh Kulkarni, Grace Hui Yang
Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully. Training DRL…
A Re-classification of Information Seeking Tasks and Their Computational Solutions
Zhiwen Tang, Grace Hui Yang
This article presents a re-classification of information seeking (IS) tasks, concepts, and algorithms. The proposed taxonomy provides new dimensions to look into information seekin…