29 citations · 120 across the 17 of their papers we have counts for
17 papers
Reducing Object Hallucination in LVLMs via Emphasizing Image-negative Tokens
Meng Shen, Minghao Wu, Deepu Rajan
Object hallucination is a significant challenge that hinders the application of large vision-language models (LVLMs) in practice. We hypothesize that one possible origin of halluci…
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…
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Renxi Wang, Haonan Li, Minghao Wu +4
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datas…
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
Zhanyu Wang, Longyue Wang, Zhen Zhao +7
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of in…
Style Over Substance: Evaluation Biases for Large Language Models
Minghao Wu, Alham Fikri Aji
As large language models (LLMs) continue to advance, accurately and comprehensively evaluating their performance becomes increasingly challenging. Ranking the relative performance…
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
Chenyang Lyu, Minghao Wu, Longyue Wang +5
Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text ha…
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation
Haonan Li, Fajri Koto, Minghao Wu +2
Instruction tuning has shown great promise in improving the performance of large language models. However, research on multilingual instruction tuning has been limited due to the s…
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models
Chenyang Lyu, Zefeng Du, Jitao Xu +7
Machine Translation (MT) has greatly advanced over the years due to the developments in deep neural networks. However, the emergence of Large Language Models (LLMs) like GPT-4 and…
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
Minghao Wu, Abdul Waheed, Chiyu Zhang +2
Large language models (LLMs) with instruction fine-tuning demonstrate superior generative capabilities. However, these models are resource-intensive. To alleviate this issue, we ex…
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Minghao Wu, George Foster, Lizhen Qu +1
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.…
Universal Conditional Masked Language Pre-training for Neural Machine Translation
Pengfei Li, Liangyou Li, Meng Zhang +2
Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT). Different from prior works where pre-trained models usually adopt an unidirect…
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
Minghao Wu, Yitong Li, Meng Zhang +3
Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in re…
Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Minghao Wu, Fei Liu, Trevor Cohn
Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this…