29 citations · 120 across the 17 of their papers we have counts for
5 papers · 1 filter
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models
Minghao Wu, Thuy-Trang Vu, Lizhen Qu +1
Large language models (LLMs) are typically fine-tuned on diverse and extensive datasets sourced from various origins to develop a comprehensive range of skills, such as writing, re…
(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts
Minghao Wu, Jiahao Xu, Yulin Yuan +4
Literary translation remains one of the most challenging frontiers in machine translation due to the complexity of capturing figurative language, cultural nuances, and unique styli…
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations
Chiyu Zhang, Yifei Sun, Minghao Wu +9
Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world. In this work, we introduce EmbSum, a novel framework that…
Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models
Chenyang Lyu, Minghao Wu, Alham Fikri Aji
Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) resea…
Adapting Large Language Models for Document-Level Machine Translation
Minghao Wu, Thuy-Trang Vu, Lizhen Qu +2
Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often outperform larg…