262 citations · 1.1k across the 41 of their papers we have counts for
7 papers · 1 filter
Fact-Level Confidence Calibration and Self-Correction
Yige Yuan, Bingbing Xu, Hexiang Tan +5
Confidence calibration in LLMs, i.e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their o…
Understanding the Collapse of LLMs in Model Editing
Wanli Yang, Fei Sun, Jiajun Tan +4
Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse. A…
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…
Unlink to Unlearn: Simplifying Edge Unlearning in GNNs
Jiajun Tan, Fei Sun, Ruichen Qiu +2
As concerns over data privacy intensify, unlearning in Graph Neural Networks (GNNs) has emerged as a prominent research frontier in academia. This concept is pivotal in enforcing t…
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
Wanli Yang, Fei Sun, Xinyu Ma +3
Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, w…
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
Kaike Zhang, Qi Cao, Yunfan Wu +3
Sequential recommender systems stand out for their ability to capture users' dynamic interests and the patterns of item-to-item transitions. However, the inherent openness of seque…
Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?
Hexiang Tan, Fei Sun, Wanli Yang +3
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts genera…