Three Bricks to Consolidate Watermarks for Large Language Models
The task of discerning between generated and natural texts is increasingly
challenging. In this context, watermarking emerges as a promising technique for
ascribing generated text to a specific model. It alters the sampling generation
process so as to leave an invisible trace in the generated output, facilitating
later detection. This research consolidates watermarks for large language
models based on three theoretical and empirical considerations. First, we
introduce new statistical tests that offer robust theoretical guarantees which
remain valid even at low false-positive rates (less than 10^{-6}).
Second, we compare the effectiveness of watermarks using classical benchmarks
in the field of natural language processing, gaining insights into their
real-world applicability. Third, we develop advanced detection schemes for
scenarios where access to the LLM is available, as well as multi-bit
watermarking.
64c9da66533bbc49aa0d662b
Pierre Fernandez
admin_assigned
2023-08-02T07:49:24.793Z
616c22e040e2f69baa1c7ae0
/avatars/d6c00e7d98e5e8a52e99aa7b1a7815ee.svg
Pierre Fernandez
user
pierrefdz
64c9da66533bbc49aa0d662c
Antoine Chaffin
claimed_verified
2023-08-02T07:39:13.582Z
609bbe2f4932693ca2009d6a
https://cdn-avatars.huggingface.co/v1/production/uploads/1620819560688-609bbe2f4932693ca2009d6a.jpeg
Antoine Chaffin
user
NohTow
64c9da66533bbc49aa0d662d
Karim Tit
admin_assigned
2023-08-02T07:49:42.370Z
6180eaa864badb2ca4712345
/avatars/5b4da039d45025d713002016aded9d69.svg
Karim TIT
user
karimtito
64c9da66533bbc49aa0d662e
Vivien Chappelier
64c9da66533bbc49aa0d662f
Teddy Furon
claimed_verified
2024-10-04T20:12:03.856Z
670000393ec4cc293d41d92c
/avatars/b67843e1f7addaf6769c2cba868bf501.svg
Teddy Furon
user
tfuron
Three Bricks to Consolidate Watermarks for Large Language Models
The task of discerning between generated and natural texts is increasingly
challenging. In this context, watermarking emerges as a promising technique for
ascribing generated text to a specific model. It alters the sampling generation
process so as to leave an invisible trace in the generated output, facilitating
later detection. This research consolidates watermarks for large language
models based on three theoretical and empirical considerations. First, we
introduce new statistical tests that offer robust theoretical guarantees which
remain valid even at low false-positive rates (less than 10^{-6}).
Second, we compare the effectiveness of watermarks using classical benchmarks
in the field of natural language processing, gaining insights into their
real-world applicability. Third, we develop advanced detection schemes for
scenarios where access to the LLM is available, as well as multi-bit
watermarking.
This research develops robust watermarking techniques for large language models, introducing statistical tests, comparing watermarks through classical benchmarks, and enhancing detection schemes.
watermarking large language models statistical tests natural language processing multi-bit watermarking