55 citations · 62 across the 10 of their papers we have counts for
10 papers
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization
Yun Luo, Yingjie Li, Xiangkun Hu +5
As online platforms and recommendation algorithms evolve, people are increasingly trapped in echo chambers, leading to biased understandings of various issues. To combat this issue…
Can Language Models Learn to Skip Steps?
Tengxiao Liu, Qipeng Guo, Xiangkun Hu +4
Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intrig…
ECon: On the Detection and Resolution of Evidence Conflicts
Cheng Jiayang, Chunkit Chan, Qianqian Zhuang +7
The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and c…
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models
Qingkai Min, Qipeng Guo, Xiangkun Hu +3
Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utili…
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
Xiangkun Hu, Dongyu Ru, Lin Qiu +7
Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-tri…
EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs
Cheng Jiayang, Lin Qiu, Chunkit Chan +3
Narrative reasoning relies on the understanding of eventualities in story contexts, which requires a wealth of background world knowledge. To help machines leverage such knowledge,…
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang, Lin Qiu, Qipeng Guo +6
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsens…
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Cheng Jiayang, Lin Qiu, Tsz Ho Chan +9
Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind larg…
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
Tengxiao Liu, Qipeng Guo, Yuqing Yang +4
As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a gr…
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…