7 citations · 13 across the 17 of their papers we have counts for
17 papers
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation
Yanzhi Tian, Cunxiang Wang, Zeming Liu +5
Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Network Services, literature etc.…
Incorporating Self-Rewriting into Large Language Model Reasoning Reinforcement
Jiashu Yao, Heyan Huang, Shuang Zeng +6
Through reinforcement learning (RL) with outcome correctness rewards, large reasoning models (LRMs) with scaled inference computation have demonstrated substantial success on compl…
SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs
Hongfei Xia, Hongru Wang, Zeming Liu +3
Large Language Models (LLMs) have exhibited great performance in autonomously calling various tools in external environments, leading to better problem solving and task automation…
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models
Jingjing Liu, Zeming Liu, Zihao Cheng +7
Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair, which may substantially reduce the time consumption o…
PRIM: Towards Practical In-Image Multilingual Machine Translation
Yanzhi Tian, Zeming Liu, Zhengyang Liu +4
In-Image Machine Translation (IIMT) aims to translate images containing texts from one language to another. Current research of end-to-end IIMT mainly conducts on synthetic data, w…
HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices
Silin Li, Yuhang Guo, Jiashu Yao +2
Large language models (LLMs) have the potential to revolutionize smart home assistants by enhancing their ability to accurately understand user needs and respond appropriately, whi…
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments
Yuheng Lu, Qian Yu, Hongru Wang +7
Graphical User Interface (GUI) agents, which autonomously operate on digital interfaces through natural language instructions, hold transformative potential for accessibility, auto…
DocMEdit: Towards Document-Level Model Editing
Li Zeng, Zeming Liu, Chong Feng +2
Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost. Prior research has proposed a variety of datasets to assess the e…
ToolSpectrum : Towards Personalized Tool Utilization for Large Language Models
Zihao Cheng, Hongru Wang, Zeming Liu +4
While integrating external tools into large language models (LLMs) enhances their ability to access real-time information and domain-specific services, existing approaches focus na…
Exploring In-Image Machine Translation with Real-World Background
Yanzhi Tian, Zeming Liu, Zhengyang Liu +1
In-Image Machine Translation (IIMT) aims to translate texts within images from one language to another. Previous research on IIMT was primarily conducted on simplified scenarios su…
Optimizing Chain-of-Thought Reasoning: Tackling Arranging Bottleneck via Plan Augmentation
Yuli Qiu, Jiashu Yao, Heyan Huang +1
Multi-step reasoning ability of large language models is crucial in tasks such as math and tool utilization. Current researches predominantly focus on enhancing model performance i…
FAME: Towards Factual Multi-Task Model Editing
Li Zeng, Yingyu Shan, Zeming Liu +2
Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within…
Deterministic Reversible Data Augmentation for Neural Machine Translation
Jiashu Yao, Heyan Huang, Zeming Liu +1
Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data bec…
Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges
Xiaoming Shi, Zeming Liu, Li Du +6
This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical comm…
CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training
Mengge Liu, Wen Zhang, Xiang Li +5
Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available. Prefix-to-prefix framework is often a…
Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation
Mengge Liu, Wen Zhang, Xiang Li +4
Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence. Ho…
Neural Chinese Word Segmentation as Sequence to Sequence Translation
Xuewen Shi, Heyan Huang, Ping Jian +3
Recently, Chinese word segmentation (CWS) methods using neural networks have made impressive progress. Most of them regard the CWS as a sequence labeling problem which construct mo…