Unlocking Fine-Grained Translation Quality Estimation in LRMs through Synergistically Evolving Implicit and Explicit Reasoning
arXiv:2605.31378v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that LRMs already possess strong multilingual capabilities, while the core challenge stems from the intrinsic difficulty of learning the fine-grained QE task. In this paper, we propose RIEQE (Reasoning both Implicitly and Explicitly for QE), a simple two-stage training framework that enables the co-evolution of implicit (layer-wise) and explicit (token-wise) reasoning capabilities. To make implicit reasonin
The continuous evolution of large language models necessitates ongoing research into improving their specific task performance, with fine-grained translation quality as a key challenge.
Improving translation quality estimation in Large Reasoning Models can significantly enhance the reliability and applicability of AI-driven translation services across various sectors.
The proposed RIEQE framework introduces a new method for training LRMs to improve their fine-grained translation quality estimation by combining implicit and explicit reasoning.
- · AI developers
- · Translation services
- · Multilingual content platforms
- · Traditional translation quality assessment methods
More accurate and reliable AI-powered translation tools become available.
Increased adoption of AI for complex, nuanced translation tasks in business and diplomacy.
Reduced barriers to cross-cultural communication and information exchange, potentially fostering new global collaborations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL