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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TableEval dataset

TableEval is developed to benchmark and compare the performance of (M)LLMs on tables from scientific vs. non-scientific sources, represented as images vs. text. It comprises six data subsets derived from the test sets of existing benchmarks for question answering (QA) and table-to-text (T2T) tasks, containing a total of 3017 tables and 11312 instances. The scienfific subset includes tables from pre-prints and peer-reviewed scholarly publications, while the non-scientific subset involves tables from Wikipedia and financial reports. Each table is available as a PNG image and in four textual formats: HTML, XML, LaTeX, and Dictionary (Dict). All task annotations are taken from the source datasets. Please, refer to the Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data paper for more details.

Overview and statistics

Dataset	Task	Source	Image	Dict	LaTeX	HTML	XML
ComTQA (PubTables-1M)	VQA	PubMed Central	⬇️	⚙️	⚙️	⚙️	📄
numericNLG	T2T	ACL Anthology	📄	⬇️	⚙️	⬇️	⚙️
SciGen	T2T	arXiv and ACL Anthology	📄	⬇️	📄	⚙️	⚙️
ComTQA (FinTabNet)	VQA	Earnings reports of S&P 500 companies	📄	⚙️	⚙️	⚙️	⚙️
LogicNLG	T2T	Wikipedia	⚙️	⬇️	⚙️	📄	⚙️
Logic2Text	T2T	Wikipedia	⚙️	⬇️	⚙️	📄	⚙️

**Symbol ⬇️ indicates formats already available in the given corpus, while 📄 and ⚙️ denote formats extracted from the table source files (e. g., article PDF, Wikipedia page) and generated from other formats in this study, respectively.

Number of tables per format and data subset

Dataset	Image	Dict	LaTeX	HTML	XML
ComTQA (PubTables-1M)	932	932	932	932	932
numericNLG	135	135	135	135	135
SciGen	1035	1035	928	985	961
ComTQA (FinTabNet)	659	659	659	659	659
LogicNLG	184	184	184	184	184
Logic2Text	72	72	72	72	72
Total	3017	3017	2910	2967	2943

Total number of instances per format and data subset

Dataset	Image	Dict	LaTeX	HTML	XML
ComTQA (PubTables-1M)	6232	6232	6232	6232	6232
numericNLG	135	135	135	135	135
SciGen	1035	1035	928	985	961
ComTQA (FinTabNet)	2838	2838	2838	2838	2838
LogicNLG	917	917	917	917	917
Logic2Text	155	155	155	155	155
Total	11312	11312	11205	11262	11238

Structure

├── ComTQA               
│   ├── FinTabNet 
│   │   ├── comtqa_fintabnet.json
│   │   ├── comtqa_fintabnet_imgs.zip
│   ├── PubTab1M     
│   │   ├── comtqa_pubtab1m.json
│   │   ├── comtqa_pubtab1m_imgs.zip
│   ├── Logic2Text   
│   │   ├── logic2text.json
│   │   ├── logic2text_imgs.zip
│   ├── LogicNLG   
│   │   ├── logicnlg.json
│   │   ├── logicnlg_imgs.zip
│   ├── SciGen  
│   │   ├── scigen.json
│   │   ├── scigen_imgs.zip
│   ├── numericNLG  
│   │   ├── numericnlg.json
└── └── └── numericnlg_imgs.zip

For more details on each subset, please, refer to the respective README.md files: ComTQA, Logic2Text, LogicNLG, SciGen, numericNLG.

Citation

@inproceedings{borisova-etal-2025-table,
    title = "Table Understanding and (Multimodal) {LLM}s: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data",
    author = {Borisova, Ekaterina  and
      Barth, Fabio  and
      Feldhus, Nils  and
      Abu Ahmad, Raia  and
      Ostendorff, Malte  and
      Ortiz Suarez, Pedro  and
      Rehm, Georg  and
      M{\"o}ller, Sebastian},
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.trl-1.10/",
    pages = "109--142",
    ISBN = "979-8-89176-268-8",
    abstract = "Tables are among the most widely used tools for representing structured data in research, business, medicine, and education. Although LLMs demonstrate strong performance in downstream tasks, their efficiency in processing tabular data remains underexplored. In this paper, we investigate the effectiveness of both text-based and multimodal LLMs on table understanding tasks through a cross-domain and cross-modality evaluation. Specifically, we compare their performance on tables from scientific vs. non-scientific contexts and examine their robustness on tables represented as images vs. text. Additionally, we conduct an interpretability analysis to measure context usage and input relevance. We also introduce the TableEval benchmark, comprising 3017 tables from scholarly publications, Wikipedia, and financial reports, where each table is provided in five different formats: Image, Dictionary, HTML, XML, and LaTeX. Our findings indicate that while LLMs maintain robustness across table modalities, they face significant challenges when processing scientific tables."
}