WASA: WAtermark-based Source Attribution for Large Language
  Model-Generated Data
Jingtan Wang Xinyang Lu Zitong Zhao Zhongxiang Dai Chuan-Sheng Foo See-Kiong Ng Bryan Kian Hsiang Low
The impressive performances of large language models (LLMs) and their immense
potential for commercialization have given rise to serious concerns over the
intellectual property (IP) of their training data. In particular, the synthetic
texts generated by LLMs may infringe the IP of the data being used to train the
LLMs. To this end, it is imperative to be able to (a) identify the data
provider who contributed to the generation of a synthetic text by an LLM
(source attribution) and (b) verify whether the text data from a data provider
has been used to train an LLM (data provenance). In this paper, we show that
both problems can be solved by watermarking, i.e., by enabling an LLM to
generate synthetic texts with embedded watermarks that contain information
about their source(s). We identify the key properties of such watermarking
frameworks (e.g., source attribution accuracy, robustness against adversaries),
and propose a WAtermarking for Source Attribution (WASA) framework that
satisfies these key properties due to our algorithmic designs. Our WASA
framework enables an LLM to learn an accurate mapping from the texts of
different data providers to their corresponding unique watermarks, which sets
the foundation for effective source attribution (and hence data provenance).
Extensive empirical evaluations show that our WASA framework achieves effective
source attribution and data provenance.