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helloboyn/WMT25-TS

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Use restrictions

The declared licence (cc-by-nc-4.0) is classified as having use restrictions.

Declared licence
cc-by-nc-4.0, from the tagEvidence: README.md at b722719
Licence files
No licence file at the top level of the repositoryEvidence: file list at b722719
Declared sources
None listed in the card metadata
Access gated on Hugging Face
Yes
helloboyn/WMT25-TShelloboyn/WMT25-TScc-by-nc-4.0: declared licencecc-by-nc-4.0declared licenceREADME.md ↗no licence file: found at top levelno licence fi…found at top levelfile list ↗
helloboyn/WMT25-TShelloboyn/WMT25…cc-by-nc-4.0: declared licencecc-by-nc-4.0declared licenceREADME.md ↗no licence file: found at top levelno licence fi…found at top levelfile list ↗
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Citation text from the card

@InProceedings{singh-kumar-ekbal:2025:WMT2,
  author    = {Singh, Kshetrimayum Boynao  and  Kumar, Deepak  and  Ekbal, Asif},
  title     = {Evaluation of LLM for English to Hindi Legal Domain Machine Translation Systems},
  booktitle      = {Proceedings of the Tenth Conference on Machine Translation (WMT 2025)},
  month          = {November},
  year           = {2025},
  address        = {Suzhou, China},
  publisher      = {Association for Computational Linguistics},
  pages     = {823--833},
  abstract  = {The study critically examines various Machine Translation systems, particularly focusing on Large Language Models, using the WMT25 Legal Domain Test Suite for translating English into Hindi. It utilizes a dataset of 5,000 sentences designed to capture the complexity of legal texts based on word frequency ranges from 5 to 54. Each frequency range contains 100 sentences, collectively forming a corpus that spans from simple legal terms to intricate legal provisions. Six metrics were used to evaluate the performance of the system: BLEU, METEOR, TER, CHRF++, BERTScore and COMET. The findings reveal diverse capabilities and limitations of LLM architectures in handling complex legal texts. Notably, Gemini-2.5-Pro, Claude-4, and ONLINE-B topped the performance charts in terms of human evaluation, showcasing the potential of LLMs for nuanced translation. Despite these advances, the study identified areas for further research, especially in improving robustness, reliability, and ex

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dataset_licence({ "id": "helloboyn/WMT25-TS" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0