The recent developments in the field of multilingual language models (LLMs) and machine translation (MT) have shown a significant shift towards more sophisticated and adaptive approaches. There is a growing emphasis on creating benchmarks and evaluation frameworks that are not only multilingual but also context-aware, addressing the specific needs of different languages and domains. Innovations in data processing pipelines, particularly for large-scale multilingual models, have led to more efficient and transparent methodologies, enhancing the models' performance and compliance with data regulations. Additionally, there is a noticeable trend towards leveraging the inherent imbalances in language representation within LLMs to drive self-improvement, focusing on enhancing performance in underrepresented languages. Explainable interfaces and tools for fine-grained analysis of translation systems are also gaining traction, providing deeper insights into model performance and error patterns. Notably, the integration of human-machine collaboration in data collection for MT is proving to be a cost-effective and high-quality alternative to traditional methods. The field is also witnessing advancements in the detection and correction of errors in historical texts, as well as the development of metrics for evaluating the isochrony of translations, particularly in video dubbing contexts. Overall, the research area is moving towards more inclusive, efficient, and interpretable solutions that cater to a broader range of languages and applications.
Multilingual LLMs and MT: Sophisticated, Adaptive, and Inclusive Solutions
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MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations
Data Processing for the OpenGPT-X Model Family
Towards Cross-Lingual LLM Evaluation for European Languages
Language Imbalance Driven Rewarding for Multilingual Self-improving
Translation Canvas: An Explainable Interface to Pinpoint and Analyze Translation Systems
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs
Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data
An Annotated Dataset of Errors in Premodern Greek and Baselines for Detecting Them
IsoChronoMeter: A simple and effective isochronic translation evaluation metric
PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation
Tokenization and Morphology in Multilingual Language Models: A~Comparative Analysis of mT5 and ByT5
Leveraging Structure Knowledge and Deep Models for the Detection of Abnormal Handwritten Text
IntGrad MT: Eliciting LLMs' Machine Translation Capabilities with Sentence Interpolation and Gradual MT
A State-of-the-Art Morphosyntactic Parser and Lemmatizer for Ancient Greek
GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correction
Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors
Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention
LLM-based Translation Inference with Iterative Bilingual Understanding
Improving Instruction-Following in Language Models through Activation Steering
Qtok: A Comprehensive Framework for Evaluating Multilingual Tokenizer Quality in Large Language Models
Automatic Translation Alignment Pipeline for Multilingual Digital Editions of Literary Works
Reference-Based Post-OCR Processing with LLM for Diacritic Languages
Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs
Linguistically Grounded Analysis of Language Models using Shapley Head Values
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?