12 papers · 1 filter
Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation
Di Wu, Sergey Troshin, Christof Monz +2
Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answer attempts depend on earlier…
How Far Can Machine Translation Quality Take You? Extrinsic Discourse Evaluation in Goal-Oriented Setups
Wafaa Mohammed, Kata Naszadi, Vlad Niculae
Existing machine translation (MT) metrics and discourse-focused evaluations primarily assess translation quality intrinsically, without measuring the downstream consequences of tra…
Your Multimodal Speech Model Says I Have a Face for Radio
Maya K. Nachesa, Vlad Niculae, Vagrant Gautam
As large neural models have become better at language tasks, researchers are increasingly building multi- and omnimodal models that handle more modalities of data. One example is t…
Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
Evgeniia Tokarchuk, Maya K. Nachesa, Sergey Troshin +1
Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-to…
Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding
Wafaa Mohammed, Vlad Niculae, Chrysoula Zerva
Large language models (LLMs) have emerged as strong contenders in machine translation.Yet, they still struggle to adequately handle discourse phenomena, such as pronoun resolution…
Asking a Language Model for Diverse Responses
Sergey Troshin, Irina Saparina, Antske Fokkens +1
Large language models increasingly rely on explicit reasoning chains and can produce multiple plausible responses for a given context. We study the candidate sampler that produces…