Papers by Wenjie Mo

2 papers
A Causal View of Entity Bias in (Large) Language Models (2023.findings-emnlp)

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Challenge: Entity bias affects pretrained (large) language models, causing them to rely on (biased) parametric knowledge to make unfaithful predictions.
Approach: They propose a structured causal model whose parameters are easier to estimate . they propose to perturb the original entity with neighboring entities .
Outcome: The proposed model reduces biasing information pertaining to the original entity while still preserving sufficient semantic information from similar entities.
Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)

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Challenge: Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading.
Approach: They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets.
Outcome: The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans.

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