2 citations · 3 across the 4 of their papers we have counts for
4 papers
Large Language Model Sourcing: A Survey
Liang Pang, Jia Gu, Sunhao Dai +7
Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement…
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration
Kangxi Wu, Liang Pang, Huawei Shen +1
The black-box nature of large language models (LLMs) poses challenges in interpreting results, impacting issues such as data intellectual property protection and hallucination trac…
LLMDet: A Third Party Large Language Models Generated Text Detection Tool
Kangxi Wu, Liang Pang, Huawei Shen +2
Generated texts from large language models (LLMs) are remarkably close to high-quality human-authored text, raising concerns about their potential misuse in spreading false informa…
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
Yunchang Zhu, Liang Pang, Kangxi Wu +3
Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. Whi…