2 citations · 2 across the 4 of their papers we have counts for
4 papers
IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation
Lingyi Meng, Zecong Tang, Haoran Li +12
In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic, multi-turn interleaved ima…
Teaching Language Models to Self-Improve by Learning from Language Feedback
Chi Hu, Yimin Hu, Hang Cao +2
Aligning Large Language Models (LLMs) with human intentions and values is crucial yet challenging. Current methods primarily rely on human preferences, which are costly and insuffi…
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
Chi Hu, Yuan Ge, Xiangnan Ma +5
Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors du…
The NiuTrans System for the WMT21 Efficiency Task
Chenglong Wang, Chi Hu, Yongyu Mu +8
This paper describes the NiuTrans system for the WMT21 translation efficiency task (http://statmt.org/wmt21/efficiency-task.html). Following last year's work, we explore various te…