Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
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COMET : A Neural Framework for MT Evaluation
16 Pith papers cite this work. Polarity classification is still indexing.
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ReflectMT internalizes reflection via two-stage RL to enable direct high-quality machine translation that outperforms explicit reasoning models like DeepSeek-R1 on WMT24 while using 94% fewer tokens.
LQM introduces a six-level linguistically motivated error taxonomy for MT evaluation and applies it via expert annotation to LLM outputs on a new 3,850-sentence multi-dialect Arabic corpus.
Reinforcement learning with semantic rewards lets LLMs gain low-resource language skills without the alignment tax that degrades general capabilities in supervised fine-tuning.
SLoW selects low-frequency word dictionaries to boost LLM translation quality and efficiency across 100 languages from FLORES.
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.
COPRA introduces conditional parameter adaptation via RL to dynamically tune frozen VLMs for video anomaly detection, outperforming static methods in in-domain and cross-domain settings while generalizing to other video tasks.
Selective replacement of the worst 20-30% of text-only subtitle segments with visual-enhanced outputs raises COMET scores for Indic languages, but full visual grounding is ineffective because of temporal misalignment between subtitles and frames.
User study with professional En-Nl translators found LLM-based error highlights and APE correction suggestions did not improve productivity or quality over standard post-editing but were better received and enhanced user experience.
Generative AI evaluation must shift from static benchmark scores to measuring sustained improvements in human capabilities within specific deployment contexts.
Combining English and target-language web retrieval boosts medical QA for low-resource languages to match high-resource performance, while English web data benefits high-resource languages most and specialized sources like PubMed lack multilingual coverage.
BM25 retrieval makes many-shot ICL for low-resource MT roughly 5x more data-efficient, with 50 examples matching 250 random ones and 250 matching 1000.
Frequent sentence-level text improves LLM prompting and fine-tuning performance across math, translation, commonsense, and tool-use tasks via a proposed frequency law and curriculum ordering.
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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Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations
Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
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ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
ReflectMT internalizes reflection via two-stage RL to enable direct high-quality machine translation that outperforms explicit reasoning models like DeepSeek-R1 on WMT24 while using 94% fewer tokens.
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LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation
LQM introduces a six-level linguistically motivated error taxonomy for MT evaluation and applies it via expert annotation to LLM outputs on a new 3,850-sentence multi-dialect Arabic corpus.
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Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax
Reinforcement learning with semantic rewards lets LLMs gain low-resource language skills without the alignment tax that degrades general capabilities in supervised fine-tuning.
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SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models
SLoW selects low-frequency word dictionaries to boost LLM translation quality and efficiency across 100 languages from FLORES.
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CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
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.
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COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection
COPRA introduces conditional parameter adaptation via RL to dynamically tune frozen VLMs for video anomaly detection, outperforming static methods in in-domain and cross-domain settings while generalizing to other video tasks.
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Towards Visually-Guided Movie Subtitle Translation for Indic Languages
Selective replacement of the worst 20-30% of text-only subtitle segments with visual-enhanced outputs raises COMET scores for Indic languages, but full visual grounding is ineffective because of temporal misalignment between subtitles and frames.
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Smarter edits? Post-editing with error highlights and translation suggestions
User study with professional En-Nl translators found LLM-based error highlights and APE correction suggestions did not improve productivity or quality over standard post-editing but were better received and enhanced user experience.
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Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility
Generative AI evaluation must shift from static benchmark scores to measuring sustained improvements in human capabilities within specific deployment contexts.
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Effects of Cross-lingual Evidence in Multilingual Medical Question Answering
Combining English and target-language web retrieval boosts medical QA for low-resource languages to match high-resource performance, while English web data benefits high-resource languages most and specialized sources like PubMed lack multilingual coverage.
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An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages
BM25 retrieval makes many-shot ICL for low-resource MT roughly 5x more data-efficient, with 50 examples matching 250 random ones and 250 matching 1000.
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Adam's Law: Textual Frequency Law on Large Language Models
Frequent sentence-level text improves LLM prompting and fine-tuning performance across math, translation, commonsense, and tool-use tasks via a proposed frequency law and curriculum ordering.
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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.
- Dynamic Meta-Metrics: Source-Sentence Conditioned Weighting for MT Evaluation
- VIDA: A dataset for Visually Dependent Ambiguity in Multimodal Machine Translation