Cross-Lingual Paraphrase Identification

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (6 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• ByT5: Towards a token-free future with pre-trained byte-to-byte models
  28 May 2021 5 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
• Rethinking embedding coupling in pre-trained language models
  24 Oct 2020 4 repositories listed
  We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models.
• Do Multilingual Language Models Think Better in English?
  2 Aug 2023 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models.
• PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
  7 Jun 2023 1 repository listed
  Furthermore, we present a comprehensive analysis to understand the mystery behind prompt robustness and its transferability.
• mGPT: Few-Shot Learners Go Multilingual
  15 Apr 2022 1 repository listed
  Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for using the pre-trained language models.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.