21 citations · 28 across the 10 of their papers we have counts for
10 papers · 1 filter
AFRIDOC-MT: Document-level MT Corpus for African Languages
Jesujoba O. Alabi, Israel Abebe Azime, Miaoran Zhang +13
This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, and Zulu. The da…
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?
Dawei Zhu, Pinzhen Chen, Miaoran Zhang +3
Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.…
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Dawei Zhu, Sony Trenous, Xiaoyu Shen +3
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 paralle…
Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
Vagrant Gautam, Eileen Bingert, Dawei Zhu +2
Robust, faithful and harm-free pronoun use for individuals is an important goal for language model development as their use increases, but prior work tends to study only one or two…
LawBench: Benchmarking Legal Knowledge of Large Language Models
Zhiwei Fei, Xiaoyu Shen, Dawei Zhu +6
Large language models (LLMs) have demonstrated strong capabilities in various aspects. However, when applying them to the highly specialized, safe-critical legal domain, it is uncl…
Weaker Than You Think: A Critical Look at Weakly Supervised Learning
Dawei Zhu, Xiaoyu Shen, Marius Mosbach +2
Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it…