Search Results for author: Yingya Li

Found 8 papers, 4 papers with code

Detecting Health Advice in Medical Research Literature

1 code implementation EMNLP 2021 Yingya Li, Jun Wang, Bei Yu

We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.

Retrieval Sentence

Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly

1 code implementation18 Oct 2023 Sheng Lu, Shan Chen, Yingya Li, Danielle Bitterman, Guergana Savova, Iryna Gurevych

In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.

In-Context Learning

Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

1 code implementation5 Apr 2023 Shan Chen, Yingya Li, Sheng Lu, Hoang Van, Hugo JWL Aerts, Guergana K. Savova, Danielle S. Bitterman

The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice.

domain classification Prompt Engineering +1

Measuring Correlation-to-Causation Exaggeration in Press Releases

1 code implementation COLING 2020 Bei Yu, Jun Wang, Lu Guo, Yingya Li

By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22{\%} of press releases made exaggerated causal claims from correlational findings in observational studies.

Detecting Causal Language Use in Science Findings

no code implementations IJCNLP 2019 Bei Yu, Yingya Li, Jun Wang

We then applied the prediction model to measure the causal language use in the research conclusions of about 38, 000 observational studies in PubMed.


An NLP Analysis of Exaggerated Claims in Science News

no code implementations WS 2017 Yingya Li, Jieke Zhang, Bei Yu

The discrepancy between science and media has been affecting the effectiveness of science communication.

Misinformation Text Classification

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