Papers by Yuchen Wen
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective (2025.findings-acl)
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| Challenge: | Existing studies have shown that large language models (LLMs) can elicit implicit biases that hurt certain demographics without explicit harmful words. |
| Approach: | They propose three attack approaches to elicit agreements to biased viewpoints from LLMs from a psychometric perspective and built two benchmarks to compare them. |
| Outcome: | The proposed methods elicit agreements to biased viewpoints more effectively than baselines. |
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)
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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting. |
| Approach: | They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset. |
| Outcome: | The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting. |
A Data-Centric Approach to Generalizable Speech Deepfake Detection (2026.acl-long)
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| Challenge: | Speech deepfake detection (SDD) is a critical research area as speech synthesis technologies become more sophisticated. |
| Approach: | They propose a data-centric approach to generalize SDD data from two perspectives . they propose naive aggregation strategies for mixing heterogeneous data and diversity-optimized sampling strategy for a single dataset and multiple datasets. |
| Outcome: | The proposed approach outperforms the naive aggregation baseline on a 12k-hour data pool while using only 3% of the total available data. |
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation (2023.findings-emnlp)
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| Challenge: | Existing code translation datasets focus on a single pair of programming languages . early software systems are developed using programming languages such as Fortran and COBOL . |
| Approach: | They propose a large-scale comprehensive benchmark that supports the largest variety of programming languages for code translation. |
| Outcome: | The proposed framework supports translations between multiple programming languages and a cross-framework dataset for deep learning code across different frameworks. |