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cs.CL2024
ELICIT: LLM Augmentation via External In-Context Capability
Futing Wang, Jianhao Yan, Yue Zhang +1
Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application. Traditional fine-tuning methods require substantial data and co…
cs.CL2024
Keys to Robust Edits: from Theoretical Insights to Practical Advances
Jianhao Yan, Futing Wang, Yun Luo +2
Large language models (LLMs) struggle with maintaining accurate knowledge due to conflicting/outdated parametric memories. While locate-and-edit methods address this, their relianc…
cs.CL2024
Potential and Challenges of Model Editing for Social Debiasing
Jianhao Yan, Futing Wang, Yafu Li +1
Large language models (LLMs) trained on vast corpora suffer from inevitable stereotype biases. Mitigating these biases with fine-tuning could be both costly and data-hungry. Model…