Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Overview

The StakcMIAsub dataset serves as a benchmark for membership inference attack (MIA) topic. StackMIAsub is build based on the Stack Exchange corpus, which is widely used for pre-training. See our paper (to-be-released) for detailed description.

Data format

StakcMIAsub is formatted as a jsonlines file in the following manner:

{"snippet": "SNIPPET1", "label": 1 or 0}
{"snippet": "SNIPPET2", "label": 1 or 0}
...

📌 label 1 denotes to members, while label 0 denotes to non-members.

Applicability

Our dataset supports most white- and black-box models, which are released before May 2024 and pretrained with Stack Exchange corpus :

Black-box OpenAI models:
- text-davinci-001
- text-davinci-002
- ...
White-box models:
- LLaMA and LLaMA2
- Pythia
- GPT-Neo
- GPT-J
- OPT
- StableLM
- Falcon
- ...

Related repo

To run our PAC method to perform membership inference attack, visit our code repo

Cite our work

⭐️ If you find our dataset helpful, please kindly cite our work :

@misc{ye2024data,
      title={Data Contamination Calibration for Black-box LLMs}, 
      author={Wentao Ye and Jiaqi Hu and Liyao Li and Haobo Wang and Gang Chen and Junbo Zhao},
      year={2024},
      eprint={2405.11930},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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Paper for darklight03/StackMIAsub

Data Contamination Calibration for Black-box LLMs

Paper • 2405.11930 • Published May 20, 2024