GlitchBench
Collection
3 items • Updated • 1
Datasets:
Dataset Viewer
The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
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 failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
GlitchBench
This repository contains the dataset for the paper GlitchBench: Can large multimodal models detect video game glitches?
by Mohammad Reza Taesiri, Tianjun Feng, Anh Nguyen, and Cor-Paul Bezemer
(CVPR 2024)
Abstract
Large multimodal models (LMMs) have evolved from large language models (LLMs) to integrate multiple input modalities, such as visual inputs. This integration augments the capacity of LLMs in tasks requiring visual comprehension and reasoning. However, the extent and limitations of their enhanced abilities are not fully understood. To address this gap, we introduce GlitchBench, a novel benchmark designed to test and evaluate the common-sense reasoning and visual recognition capabilities of large multimodal models. Our dataset is curated from a variety of unusual, infrequent, and glitched scenarios from video game content and aims to challenge both the visual and linguistic reasoning powers of LMMs in detecting and interpreting out-of-the-ordinary events and scene composition.
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