55 citations · 465 across the 67 of their papers we have counts for
68 papers
Multi-Candidate Speculative Decoding
Sen Yang, Shujian Huang, Xinyu Dai +1
Large language models have shown impressive capabilities across a variety of NLP tasks, yet their generating text autoregressively is time-consuming. One way to speed them up is sp…
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation
Xu Huang, Zhirui Zhang, Xiang Geng +3
This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task, aiming to better understand the mechanisms behin…
A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
Peng Ding, Jun Kuang, Dan Ma +4
Large Language Models (LLMs), such as ChatGPT and GPT-4, are designed to provide useful and safe responses. However, adversarial prompts known as 'jailbreaks' can circumvent safegu…
Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models
Shuaijie She, Shujian Huang, Xingyun Wang +2
LLMs (Large Language Models) usually interact with users in the form of dialogue and generate responses following their instructions, which naturally require dialogue comprehension…
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
Changjiang Gao, Shujian Huang, Jixing Li +1
Recent large language models (LLMs) have revealed strong abilities to understand natural language. Since most of them share the same basic structure, i.e. the transformer block, po…
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
Xu Huang, Zhirui Zhang, Ruize Gao +6
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art model…