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20232026
most citedLarge Multimodal Models for Low-Resource Languages: A Survey

9 citations · 19 across the 34 of their papers we have counts for

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13 papers · 1 filter

cs.CL2026

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu

Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered…

cs.CL2026

Multilingual Coreference Resolution via Cycle-Consistent Machine Translation

Adriana-Valentina Costache, Eduard Poesina, Silviu-Florin Gheorghe +2

Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summarization, etc. While the tas…

cs.CL2026

MOSLD-Bench: Multilingual Open-Set Learning and Discovery Benchmark for Text Categorization

Adriana-Valentina Costache, Daria-Nicoleta Dragomir, Silviu-Florin Gheorghe +3

Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalizatio…

cs.CL2026

CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning

Alexandra Dragomir, Florin Brad, Radu Tudor Ionescu

Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). Some follow-up works further improved MT performance via…

cs.CL2025

A Large-Scale Benchmark for Evaluating Large Language Models on Medical Question Answering in Romanian

Ana-Cristina Rogoz, Radu Tudor Ionescu, Alexandra-Valentina Anghel +3

We introduce MedQARo, the first large-scale medical QA benchmark in Romanian, alongside a comprehensive evaluation of state-of-the-art large language models (LLMs). We construct a…

cs.CL2025

Text Classification Under Class Distribution Shift: A Survey

Adriana Valentina Costache, Silviu Florin Gheorghe, Eduard Gabriel Poesina +2

The basic underlying assumption of machine learning (ML) models is that the training and test data are sampled from the same distribution. However, in daily practice, this assumpti…