117 citations · 133 across the 24 of their papers we have counts for
5 papers · 2 filters
Uncovering Overfitting in Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods oft…
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing
Mengqi Zhang, Bowen Fang, Qiang Liu +4
Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these is…
Learning Domain-Invariant Features for Out-of-Context News Detection
Yimeng Gu, Mengqi Zhang, Ignacio Castro +2
Out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside a mismatched news image. Existing out-of-context news d…
Knowledge Graph Enhanced Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing…
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
Pengjie Ren, Chengshun Shi, Shiguang Wu +5
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increa…