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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Abstract

Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs' capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two popular benchmarks for Python code generation, analyzing their diversity and difficulty. Our findings unveil a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. Furthermore, we uncover a worrying prevalence of easy tasks that can inflate model performance estimations. To address these limitations, we propose a novel benchmark, PythonSaga, featuring 185 hand-crafted prompts in a balanced representation of 38 programming concepts across diverse difficulty levels. The robustness of our benchmark is demonstrated by the poor performance of existing Code-LLMs. The code and dataset are openly available to the NLP community at https://github.com/PythonSaga/PythonSaga.

PythonSaga

This dataset follows the rules and diversity of template suggested in the paper "PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLM" The goal is to make benchmarks better at assessing Code Generating Language Models (LLMs).

Model	Size	Pass@1	Pass@10
StarCoderBase	7B	0.0029	0.0149
StarCoder2	7B	0.0024	0.0217
Code Llama	7B	0.0067	0.0472
CodeQwen1.5-Chat	7B	0.0059	0.0497
Nxcode-CQ-orpo	7B	0.0058	0.0523
Mistral-Instruct-v0.1	7B	0.0140	0.0552
Code Llama Instruct	7B	0.0178	0.0744
Deepseek Coder Instruct	6.7B	0.0137	0.0889
Code Llama Python	7B	0.0240	0.0979
Llama 3	8B	0.0370	0.1125
Phi-2	2.7B	0.0302	0.1187
OpenCodeInterpreter-DS	6.7B	0.0259	0.1206
Deepseek Coder	6.7B	0.0343	0.1415
Code Llama Python	13B	0.0405	0.1514
GPT-3.5	NA	0.0724	0.2384
GPT-4	NA	0.1243	0.3311

Comparison between open and closed-source models on PythonSaga. We use the number of samples (n) as 20 for both open and closed-source models.

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