Search Results for author: Liyan Tang

Found 9 papers, 3 papers with code

EchoGen: Generating Conclusions from Echocardiogram Notes

no code implementations BioNLP (ACL) 2022 Liyan Tang, Shravan Kooragayalu, Yanshan Wang, Ying Ding, Greg Durrett, Justin F. Rousseau, Yifan Peng

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length.


TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

1 code implementation20 Feb 2024 Liyan Tang, Igor Shalyminov, Amy Wing-mei Wong, Jon Burnsky, Jake W. Vincent, Yu'an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

We find that there are diverse errors and error distributions in model-generated summaries and that non-LLM based metrics can capture all error types better than LLM-based evaluators.

Hallucination News Summarization +2

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses

no code implementations30 May 2023 Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding, Greg Durrett, Justin F. Rousseau

To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans.

Contrastive Learning Decision Making +1

Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors

1 code implementation25 May 2022 Liyan Tang, Tanya Goyal, Alexander R. Fabbri, Philippe Laban, Jiacheng Xu, Semih Yavuz, Wojciech Kryściński, Justin F. Rousseau, Greg Durrett

We compare performance of state-of-the-art factuality metrics, including recent ChatGPT-based metrics, on this stratified benchmark and show that their performance varies significantly across different types of summarization models.

Abstractive Text Summarization

Prior Knowledge Enhances Radiology Report Generation

no code implementations11 Jan 2022 Song Wang, Liyan Tang, Mingquan Lin, George Shih, Ying Ding, Yifan Peng

In this work, we propose to mine and represent the associations among medical findings in an informative knowledge graph and incorporate this prior knowledge with radiology report generation to help improve the quality of generated reports.

RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification

no code implementations28 Oct 2021 Ajay Jaiswal, Liyan Tang, Meheli Ghosh, Justin Rousseau, Yifan Peng, Ying Ding

Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements.

Classification Contrastive Learning

Making Document-Level Information Extraction Right for the Right Reasons

no code implementations14 Oct 2021 Liyan Tang, Dhruv Rajan, Suyash Mohan, Abhijeet Pradhan, R. Nick Bryan, Greg Durrett

We show that regularization with small amounts of evidence supervision during training can substantially improve the quality of extracted evidence.

Sentence slot-filling +1

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