22 citations · 33 across the 8 of their papers we have counts for
8 papers
Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage
Kaige Xie, Philippe Laban, Prafulla Kumar Choubey +2
Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly for open-ended questions that lack definitive answers and require coverage of multiple sub…
Making Large Language Models into World Models with Precondition and Effect Knowledge
Kaige Xie, Ian Yang, John Gunerli +1
World models, which encapsulate the dynamics of how actions affect environments, are foundational to the functioning of intelligent agents. In this work, we explore the potential o…
Creating Suspenseful Stories: Iterative Planning with Large Language Models
Kaige Xie, Mark Riedl
Automated story generation has been one of the long-standing challenges in NLP. Among all dimensions of stories, suspense is very common in human-written stories but relatively und…
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
Kaige Xie, Tong Yu, Haoliang Wang +6
In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To effici…
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes
Kaige Xie, Sarah Wiegreffe, Mark Riedl
Multi-hop Question Answering (QA) is a challenging task since it requires an accurate aggregation of information from multiple context paragraphs and a thorough understanding of th…
Guiding Neural Story Generation with Reader Models
Xiangyu Peng, Kaige Xie, Amal Alabdulkarim +3
Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-…
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Tiancheng Zhao, Kaige Xie, Maxine Eskenazi
Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use…
Towards Universal Dialogue State Tracking
Liliang Ren, Kaige Xie, Lu Chen +1
Dialogue state tracking is the core part of a spoken dialogue system. It estimates the beliefs of possible user's goals at every dialogue turn. However, for most current approaches…