Papers by Jiali Cheng
EqualizeIR: Mitigating Linguistic Biases in Retrieval Models (2025.naacl-short)
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| Challenge: | Existing information retrieval models show significant linguistic biases based on the linguistic complexity of queries. |
| Approach: | They propose a framework to mitigate linguistic biases in IR models by using a linguistically biased weak learner to capture biased queries and then train a robust model by regularizing and refining its predictions. |
| Outcome: | The proposed framework reduces performance disparities across simple and complex queries while improving overall retrieval performance. |
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)
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Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters. |
| Approach: | They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters. |
| Outcome: | The proposed method is compatible with a tunable module and tested on 11 NLP tasks. |
MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries (2024.findings-acl)
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| Challenge: | Medical decisions directly impact individuals’ health and well-being. |
| Approach: | They propose to use a dataset to jointly extract and classify medical decisions within clinical notes. |
| Outcome: | The proposed dataset contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. |
A Mechanistic Perspective and Difficulty Metric for Unlearning (2026.findings-acl)
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| Challenge: | Existing studies show that machine unlearning success varies across samples . easy-to-unlearn samples are associated with shorter, shallower interactions . hard-to unlear rely on longer and deeper pathways closer to late-stage computation. |
| Approach: | They propose a pre-unlearning metric that assigns each sample a continuous difficulty score . they show that CUD reliably separates intrinsically easy and hard samples . |
| Outcome: | The proposed method reliably separates intrinsically easy and hard samples and remains stable across unlearning methods. |
FairFlow: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding (2024.emnlp-main)
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| Challenge: | Existing debiasing frameworks can detect known dataset biases and spurious correlations in data. |
| Approach: | They propose a framework that learns to be undecided in its predictions for data samples . they propose 'contrary' objective that learn debiased and robust representations from biased views . |
| Outcome: | The proposed framework outperforms existing methods against out-of-domain and hard test samples without compromising performance. |