Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
Findings of the WMT 25 Shared Task on Automated Translation Evaluation Systems: Linguistic Diversity is Challenging and References Still Help
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Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.
Explicit purpose instructions improve LLM translation adaptedness across 50 languages and 8 domains, with larger gains on informal text, while standard metrics often penalize the adapted outputs.
Dynamic Meta-Metrics learns source-sentence conditioned combinations of MT metrics, with MLP-based and soft-conditioned versions showing gains over linear and GP ensembles on WMT data.
Reward models for LLMs frequently select socially undesirable options across four social domains, show no overall best performer, and exhibit a bias-avoidance versus context-sensitivity trade-off.
LLMs generate Xiaohongshu-style posts that elicit social comparison but show stable failures in prompt-based detection of the same reader-grounded signal.
Lexical richness is a robust linguistic signal for AI-generated text detection across models and domains, while most other features are context-dependent.
Small open-source LLMs achieve competitive system-level correlations with human judgments in machine translation quality estimation, outperforming traditional neural metrics and fine-tuned models via single-pass multi-output prompting.
HydraQE is a new end-to-end speech translation QE system using Qwen3-ASR backbone, sparsemax layer mixing, bidirectional Transformer, and multi-task curriculum training on human and pseudo labels that outperforms cascaded baselines.
Large-scale benchmarks of multilingual embeddings and QE models show no universal performer; direction-aware routing and calibration recommended for parallel data assessment.
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
Introduces LLM Consumer Behavior Theory to analyze consumer behavior when LLMs serve as autonomous decision-making agents in markets.
ROC analysis is proposed for evaluating translation quality estimation systems, claimed to match existing methods while providing actionable business insights.
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.
A feature-based decision tree with parsing-derived signals and heuristics detects LLM-generated code in a lightweight, CPU-only setup for SemEval-2026 Task 13.
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
citing papers explorer
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Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs
Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.
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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.