6 citations · 8 across the 5 of their papers we have counts for
5 papers
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
Marcos Treviso, Nuno M. Guerreiro, Sweta Agrawal +7
While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these e…
Is Context Helpful for Chat Translation Evaluation?
Sweta Agrawal, Amin Farajian, Patrick Fernandes +2
Despite the recent success of automatic metrics for assessing translation quality, their application in evaluating the quality of machine-translated chats has been limited. Unlike…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
Weijia Xu, Sweta Agrawal, Eleftheria Briakou +2
Neural sequence generation models are known to "hallucinate", by producing outputs that are unrelated to the source text. These hallucinations are potentially harmful, yet it remai…
Can Multilinguality benefit Non-autoregressive Machine Translation?
Sweta Agrawal, Julia Kreutzer, Colin Cherry
Non-autoregressive (NAR) machine translation has recently achieved significant improvements, and now outperforms autoregressive (AR) models on some benchmarks, providing an efficie…