Papers by Chuanyuan Tan
UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets to assess LLMs' performance on unanswerable questions lack factual knowledge support. |
| Approach: | They propose a bilingual unanswerable question dataset with auxiliary factual knowledge created from a Knowledge Graph and two new tasks to measure LLMs' ability to utilize internal and external factual information. |
| Outcome: | The proposed datasets show that LLMs do not consistently perform well even when they have factual knowledge stored. |
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025.coling-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks. |
| Approach: | They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario. |
| Outcome: | The proposed model outperforms baseline methods by 5.65 points and protects target individuals’ personal data while maintaining general capabilities. |
Probing Language Models for Pre-training Data Detection (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown impressive capabilities, while raising concerns about the data contamination due to privacy issues and leakage of benchmark datasets in the pre-training phase. |
| Approach: | They propose to utilize the probing technique to examine the model’s internal activations to detect pre-training data contamination by examining the model's internal activates. |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art performance on both WikiMIA and ArxivMIA, with additional experiments confirming its efficacy. |