activity
20202026
most citedMulti-layer Representation Fusion for Neural Machine Translation

46 citations · 61 across the 10 of their papers we have counts for

collaborators

14 papers

cs.CL2026

Membership Inference on LLMs in the Wild

Jiatong Yi, Yanyang Li

Membership Inference Attacks (MIAs) act as a crucial auditing tool for the opaque training data of Large Language Models (LLMs). However, existing techniques predominantly rely on…

cs.CV2025

Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision Geometry Priors

Duo Zheng, Shijia Huang, Yanyang Li +1

Previous research has investigated the application of Multimodal Large Language Models (MLLMs) in understanding 3D scenes by interpreting them as videos. These approaches generally…

cs.LG2025

Learning to Reason from Feedback at Test-Time

Yanyang Li, Michael Lyu, Liwei Wang

Solving complex tasks in a single attempt is challenging for large language models (LLMs). Iterative interaction with the environment and feedback is often required to achieve succ…

cs.CL2024

CLEVA: Toward Comprehensive and Contamination-Free Language Model Evaluation

Yanyang Li, Tin Long Wong, Cheung To Hung +5

Recent advances in large language models (LLMs) have shown significant promise, yet their evaluation raises concerns, particularly regarding data contamination due to the lack of a…

cs.CL20221 cited

Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation

Yanyang Li, Jianqiao Zhao, Michael R. Lyu +1

Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to levera…

cs.CL2022

Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency

Yanyang Li, Fuli Luo, Runxin Xu +3

Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigat…