8 papers
Beyond But-for Test: Counterfactual Explanation in Abstract Argumentation via Actual Causality (Extended Version)
Siyi Liu, Muyun Shao, Beishui Liao
Counterfactual explanation in abstract argumentation calls for an answer to the what-if query: would the topic argument still be accepted if the status of certain other arguments w…
ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence
Siyi Liu, Aaron Halfaker, Dan Roth +1
Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting a…
Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation
Songyang Gao, Yinghui Xia, Siyi Liu +1
Research idea generation is the innovation-driving step of automated scientific research. Recently, large language models (LLMs) have shown potential for automating idea generation…
Learning Human-Perceived Fakeness in AI-Generated Videos via Multimodal LLMs
Xingyu Fu, Siyi Liu, Yinuo Xu +13
Can humans identify AI-generated (fake) videos and provide grounded reasons? While video generation models have advanced rapidly, a critical dimension -- whether humans can detect…
Towards Long Context Hallucination Detection
Siyi Liu, Kishaloy Halder, Zheng Qi +6
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is eith…
Talking Point based Ideological Discourse Analysis in News Events
Nishanth Nakshatri, Nikhil Mehta, Siyi Liu +4
Analyzing ideological discourse even in the age of LLMs remains a challenge, as these models often struggle to capture the key elements that shape real-world narratives. Specifical…
Conflicts in Texts: Data, Implications and Challenges
Siyi Liu, Dan Roth
As NLP models become increasingly integrated into real-world applications, it becomes clear that there is a need to address the fact that models often rely on and generate conflict…
Open Domain Question Answering with Conflicting Contexts
Siyi Liu, Qiang Ning, Kishaloy Halder +8
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of t…