5 citations · 10 across the 16 of their papers we have counts for
17 papers
RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification
Jingzheng Li, Yufei Ge, Qianren Mao +5
Autonomous driving risk identification aims to determine which observed object is likely to become safety-critical to the ego vehicle. Existing approaches typically predict scene-l…
End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation
Jingzheng Li, Yufei Ge, Zhijun Chen +8
Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical intera…
H3D: Benchmarking Unsupervised Text Hashing for Fine-Grained Document Deduplication
Qianren Mao, Jiaxun Lyu, Junnan Liu +4
Document hashing provides compact representations for efficient similarity search and document deduplication, but existing studies rarely compare hashing pipelines under a unified…
Enhancing Multilingual Reasoning via Steerable Model Merging
Zhuoran Li, Rui Xu, Jian Yang +8
Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reaso…
NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model
Qianren Mao, Yashuo Luo, Ziqi Qin +12
The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effect…
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
Xiaodong Lu, Xiaohan Wang, Jiajun Chai +7
Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods uti…
LLMBoost: Make Large Language Models Stronger with Boosting
Zehao Chen, Tianxiang Ai, Yifei Li +11
Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or fina…
Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process
Zhijun Chen, Zeyu Ji, Qianren Mao +13
We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving
Jingzheng Li, Tiancheng Wang, Xingyu Peng +4
Autonomous Driving (AD) systems demand the high levels of safety assurance. Despite significant advancements in AD demonstrated on open-source benchmarks like Longest6 and Bench2Dr…
Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation
Qianren Mao, Qili Zhang, Hanwen Hao +11
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution for enhancing the accuracy and credibility of Large Language Models (LLMs), particularly in Questi…
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Jingzheng Li, Xianglong Liu, Shikui Wei +6
Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous d…
Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing
Zhuoran Li, Chunming Hu, Junfan Chen +2
Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based o…
Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
Zhijun Chen, Xiaodong Lu, Jingzheng Li +12
LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
Zhuoran Li, Chunming Hu, Junfan Chen +3
Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual…
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
Zhijun Chen, Hailong Sun, Wanhao Zhang +3
We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected…
Learning from Noisy Crowd Labels with Logics
Zhijun Chen, Hailong Sun, Haoqian He +1
This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd…
Learning from Multiple Annotators by Incorporating Instance Features
Jingzheng Li, Hailong Sun, Jiyi Li +3
Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of possibly noisy labels provided by…