Web IR NLP Group @ NUS
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Corpus-trained Text Generation for Summarization
Min-Yen Kan
,
Kathleen R. McKeown
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Using the Annotated Bibliography as a Resource for Indicative Summarization
Min-Yen Kan
,
Judith L. Klavans
,
Kathleen R. McKeown
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Using librarian techniques in automatic text summarization for information retrieval
A current application of automatic text summarization is to provide an overview of relevant documents coming from an information …
Min-Yen Kan
,
Judith L. Klavans
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Applying Natural Language Generation to Indicative Summarization
Min-Yen Kan
,
Kathleen R. McKeown
,
Judith L. Klavans
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PERSIVAL: personalized summarization over multimedia health-care information
In this demonstration, we present several integrated components of PER SIVAL PErsonalized Retrieval and Summarization of Image, Video …
Noemie Elhadad
,
Min-Yen Kan
,
Simon Lok
,
Smaranda Muresan
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Role of Verbs in Document Analysis
Judith L. Klavans
,
Min-Yen Kan
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Linear Segmentation and Segment Significance
Min-Yen Kan
,
Judith L. Klavans
,
Kathleen R. McKeown
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URL
Aligning Large Language Models with Human Opinions through Persona Selection and Value–Belief–Norm Reasoning
Reasoning and predicting human opinions with large language models (LLMs) is essential yet challenging. Current methods employ …
Xuan Long Do
,
Kenji Kawaguchi
,
Min-Yen Kan
,
Nancy F. Chen
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LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach …
Xuan Long Do
,
Hai Nguyen Ngoc
,
Tiviatis Sim
,
Hieu Dao
,
Shafiq Joty
,
Kenji Kawaguchi
,
Nancy F. Chen
,
Min-Yen Kan
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Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models
We present Multi-expert Prompting, a novel enhancement of ExpertPrompting (Xu et al., 2023), designed to improve the large language …
Xuan Long Do
,
Duong Ngoc Yen
,
Anh Tuan Luu
,
Kenji Kawaguchi
,
Min-Yen Kan
,
Nancy F. Chen
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