7 papers
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…
Control the Temperature: Selective Sampling for Diverse and High-Quality LLM Outputs
Sergey Troshin, Wafaa Mohammed, Yan Meng +3
Diversity is an essential metric for evaluating the creativity of outputs generated by language models. Temperature-based sampling is a common strategy to increase diversity. Howev…
Angular Dispersion Accelerates -Nearest Neighbors Machine Translation
Evgeniia Tokarchuk, Sergey Troshin, Vlad Niculae
Augmenting neural machine translation with external memory at decoding time, in the form of k-nearest neighbors machine translation (-NN MT), is a well-established strategy for…
Movie Facts and Fibs (MF): A Benchmark for Long Movie Understanding
Emmanouil Zaranis, António Farinhas, Saul Santos +28
Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly due to limitations in current be…
Keep your distance: learning dispersed embeddings on
Evgeniia Tokarchuk, Hua Chang Bakker, Vlad Niculae
Learning well-separated features in high-dimensional spaces, such as text or image embeddings, is crucial for many machine learning applications. Achieving such separation can be e…