Jannis Vamvas
18 papers · 2021–2026 · 5 venues · across top CS/AI venues
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machine translation
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large language model
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multilingual nlp
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unsupervised learning
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contrastive conditioning
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word alignment
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masked language model
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zero-shot learning
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text generation
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human judgment
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parallel corpus
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knowledge distillation
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instruction tuning
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word sense disambiguation
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multilingual machine translation
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Papers
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
ACL 2026
SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents
ACL 2026
The Mediomatix Corpus: Parallel Data for Romansh Language Varieties via Comparable Schoolbooks
EACL 2026
Expanding the WMT24++ Benchmark with Rumantsch Grischun, Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader
EMNLP 2025
Leveraging In-Context Learning for Political Bias Testing of LLMs
ACL 2025
Machine Translation Models are Zero-Shot Detectors of Translation Direction
ACL 2025
Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
EMNLP 2025
UZH at SemEval-2025 Task 3: Token-Level Self-Consistency for Hallucination Detection
SemEval 2025
Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models
NAACL 2024
Linear-time Minimum Bayes Risk Decoding with Reference Aggregation
ACL 2024
Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding
EACL 2024
Modular Adaptation of Multilingual Encoders to Written Swiss German Dialect
EACL 2024
Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents
EMNLP 2023
Trained MT Metrics Learn to Cope with Machine-translated References
EMNLP 2023
As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning
ACL 2022
NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures
EMNLP 2022
Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias
EMNLP 2021
On the Limits of Minimal Pairs in Contrastive Evaluation
EMNLP 2021