SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
Generating Difficult-to-Translate Texts
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ComplexityMT benchmark finds higher CEFR levels increase translation difficulty and MT systems often shift target CEFR levels versus source texts in most of six languages tested.
citing papers explorer
-
When Languages Disagree: Self-Evolving Multilingual LLM Judges
SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
-
ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translation
ComplexityMT benchmark finds higher CEFR levels increase translation difficulty and MT systems often shift target CEFR levels versus source texts in most of six languages tested.