Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration

2024 inproceedings wu2024llmtda ○ Not yet verified

Authors

Kangxi Wu, Liang Pang, Huawei Shen, Xueqi Cheng

Venue

EMNLP

Abstract

Enhances training data attribution methods for large language models including LLaMA2, QWEN2, and Mistral by considering fitting error in the attribution process.

BibTeX

Local Entry

@inproceedings{wu2024llmtda,
  title = {Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration},
  author = {Kangxi Wu and Liang Pang and Huawei Shen and Xueqi Cheng},
  year = {2024},
  booktitle = {EMNLP},
  url = {https://aclanthology.org/2024.emnlp-main.782/},
  abstract = {Enhances training data attribution methods for large language models including LLaMA2, QWEN2, and Mistral by considering fitting error in the attribution process.}
}

From AUTO:OPENALEX

@inproceedings{wu2024llmtda,
  title = {Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration},
  author = {Kangxi Wu and Liang Pang and Huawei Shen and Xueqi Cheng},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.782}
}