Papers by Kexin Luo
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
Copied to clipboard
Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks. |
| Approach: | They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage. |
| Outcome: | Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. |
Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels (2024.lrec-main)
Copied to clipboard
| Challenge: | a framework for analyzing gender bias in terms of female objectification is proposed . male gaze refers to a phenomenon in which women are depicted as objects of aesthetic pleasure . |
| Approach: | They propose a framework for analyzing gender bias in terms of female objectification . they compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities . |
| Outcome: | The proposed framework measures female objectification along two axes. |