17 citations · 45 across the 20 of their papers we have counts for
3 papers · 1 filter
Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications
Manuel Faysse, Gautier Viaud, Céline Hudelot +1
Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requ…
Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning
Duarte M. Alves, Nuno M. Guerreiro, João Alves +5
Large language models (LLMs) are a promising avenue for machine translation (MT). However, current LLM-based MT systems are brittle: their effectiveness highly depends on the choic…
xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection
Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt +3
Widely used learned metrics for machine translation evaluation, such as COMET and BLEURT, estimate the quality of a translation hypothesis by providing a single sentence-level scor…