Search Results for author:
Found 3 papers, 2 papers with code
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of parallel data.
Trained MT Metrics Learn to Cope with Machine-translated References
Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood.
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
