4 citations · 4 across the 12 of their papers we have counts for
13 papers
Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning
Yuqi Jia, Hangsheng Zhang, Chenxi Liu +2
Large language model (LLM) agents are increasingly deployed in multi-agent systems (MAS) to accomplish complex tasks. Prior black-box red-teaming attacks mainly focus on single age…
Rubrics as Visual-Repair Context for Self-Evolving UI-to-Code Generation
Tianyi Xiong, Zhengyuan Yang, Xiaofei Wang +10
Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify a fundamental obstacle, ter…
LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
Tong Zheng, Haolin Liu, Chengsong Huang +10
Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS…
Learning from Self-Debate: Preparing Reasoning Models for Multi-Agent Debate
Chenxi Liu, Yanshuo Chen, Ruibo Chen +3
The reasoning abilities of large language models (LLMs) have been substantially improved by reinforcement learning with verifiable rewards (RLVR). At test time, collaborative reaso…
Explore Data Left Behind in Reinforcement Learning for Reasoning Language Models
Chenxi Liu, Junjie Liang, Yuqi Jia +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for improving the reasoning abilities of large language models (LLMs). The Group Relative…
Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following
Tianyi Xiong, Yi Ge, Ming Li +13
Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences.…
Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining
Chenxi Liu, Tianyi Xiong, Yanshuo Chen +5
The task adaptation and alignment of Large Multimodal Models (LMMs) have been significantly advanced by instruction tuning and further strengthened by recent preference optimizatio…
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning
Junfeng Guo, Yiming Li, Ruibo Chen +4
Large language models (LLMs) are increasingly integrated into real-world personalized applications through retrieval-augmented generation (RAG) mechanisms to supplement their respo…
A Watermark for Order-Agnostic Language Models
Ruibo Chen, Yihan Wu, Yanshuo Chen +3
Statistical watermarking techniques are well-established for sequentially decoded language models (LMs). However, these techniques cannot be directly applied to order-agnostic LMs,…
OPTune: Efficient Online Preference Tuning
Lichang Chen, Jiuhai Chen, Chenxi Liu +6
Reinforcement learning with human feedback~(RLHF) is critical for aligning Large Language Models (LLMs) with human preference. Compared to the widely studied offline version of RLH…
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Chenxi Liu, Zhenyi Wang, Tianyi Xiong +4
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL methods usually…
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
Ruibo Chen, Yihan Wu, Lichang Chen +6
Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a…
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
Ruibo Chen, Tianyi Xiong, Yihan Wu +6
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-cont…