21 citations · 36 across the 10 of their papers we have counts for
10 papers
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
Zhiwei He, Xing Wang, Wenxiang Jiao +4
Insufficient modeling of human preferences within the reward model is a major obstacle for leveraging human feedback to improve translation quality. Fortunately, quality estimation…
Benchmarking LLMs via Uncertainty Quantification
Fanghua Ye, Mingming Yang, Jianhui Pang +5
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods. However, current evalu…
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
Jianhui Pang, Fanghua Ye, Longyue Wang +4
The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which have acted as benchmarks for progress in…
Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
Longyue Wang, Zhaopeng Tu, Yan Gu +14
Translating literary works has perennially stood as an elusive dream in machine translation (MT), a journey steeped in intricate challenges. To foster progress in this domain, we h…
Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
Tian Liang, Zhiwei He, Jen-tse Huang +7
The automatic evaluation of LLM-based agent intelligence is critical in developing advanced LLM-based agents. Although considerable effort has been devoted to developing human-anno…
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…
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
Chang Gao, Haiyun Jiang, Deng Cai +2
Most existing prompting methods suffer from the issues of generalizability and consistency, as they often rely on instance-specific solutions that may not be applicable to other in…
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines
Yi Chen, Haiyun Jiang, Wei Bi +4
This work presents a new task of Text Expansion (TE), which aims to insert fine-grained modifiers into proper locations of the plain text to concretize or vivify human writings. Di…
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping
Yongrui Chen, Haiyun Jiang, Xinting Huang +2
The improvement of LLMs' instruction-following capabilities relies heavily on the availability of high-quality instruction-response pairs. Unfortunately, the current methods used t…
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study
Yi Chen, Rui Wang, Haiyun Jiang +2
Evaluating the quality of generated text is a challenging task in NLP, due to the inherent complexity and diversity of text. Recently, large language models (LLMs) have garnered si…