18 citations · 23 across the 15 of their papers we have counts for
11 papers · 1 filter
Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification
Yingfeng Luo, Shaowei Wei, Daixin Wang +7
Terminal agents rely on self-verification to assess and correct their solutions as they solve tasks through interaction with command-line environments. Yet how trustworthy such sel…
RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment
Yingfeng Luo, Hongyu Liu, Dingyang Lin +6
Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy…
APR: Penalizing Structural Redundancy in Large Reasoning Models via Anchor-based Process Rewards
Kaiyan Chang, Chenwei Zhu, Yingfeng Luo +7
Test-Time Scaling (TTS) has significantly enhanced the capabilities of Large Reasoning Models (LRMs) but introduces a critical side-effect known as Overthinking. We conduct a preli…
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang +9
Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains chall…
Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models
Kaiyan Chang, Yonghao Shi, Chenglong Wang +7
Test-Time Scaling (TTS) is a promising approach to progressively elicit the model's intelligence during inference. Recently, training-based TTS methods, such as continued reinforce…
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
Yingfeng Luo, Tong Zheng, Yongyu Mu +8
The field of neural machine translation (NMT) has changed with the advent of large language models (LLMs). Much of the recent emphasis in natural language processing (NLP) has been…
Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models
Yongyu Mu, Hengyu Li, Junxin Wang +7
Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes p…
Early Exit Is a Natural Capability in Transformer-based Models: An Empirical Study on Early Exit without Joint Optimization
Weiqiao Shan, Long Meng, Tong Zheng +5
Large language models (LLMs) exhibit exceptional performance across various downstream tasks. However, they encounter limitations due to slow inference speeds stemming from their e…
Efficient Prompting Methods for Large Language Models: A Survey
Kaiyan Chang, Songcheng Xu, Chenglong Wang +4
Prompting is a mainstream paradigm for adapting large language models to specific natural language processing tasks without modifying internal parameters. Therefore, detailed suppl…
Prompting Neural Machine Translation with Translation Memories
Abudurexiti Reheman, Tao Zhou, Yingfeng Luo +3
Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community. However, previous approaches require either a…
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction
Yanyang Li, Yingfeng Luo, Ye Lin +5
Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish.…