8 citations · 9 across the 12 of their papers we have counts for
13 papers
Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
Peng Sun, Yi Yang, Antong Zhang +7
As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Ex…
Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
Peng Sun, Yi Yang, Antong Zhang +7
Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance.…
Reachability Is Not Realization: Tracing the Sources of LLM Benchmark Gains
Yanchao Li, Wanhao Liu, Jiaqing Xie +4
Benchmark gains are often treated as evidence of greater LLM capability. Yet the same gain can reflect different changes in model behavior. A model may reach new answers, or produc…
Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
Yue Huang, Haomin Zhuang, Jiayi Ye +6
Hard-gated safety checkers often over-refuse and misalign with a vendor's model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less us…
Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT
Peng Sun, Yi Yang, Huawen Shen +4
Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, many samples can be solved via linguistic patterns or common-sense shortcuts, with…
ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions
Yue Huang, Zhengzhe Jiang, Xiaonan Luo +12
Empowering large language models (LLMs) with chemical intelligence remains a challenge due to the scarcity of high-quality, domain-specific instruction-response datasets and the mi…
Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models
Zixiang Xu, Yanbo Wang, Yue Huang +4
Large Language Models (LLMs) have achieved remarkable success in Natural Language Processing (NLP), yet their cross-lingual performance consistency remains a significant challenge.…
AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking in Large Language Models
Xiangqi Wang, Yue Huang, Yanbo Wang +4
LLMs often need effective configurations, like temperature and reasoning steps, to handle tasks requiring sophisticated reasoning and problem-solving, ranging from joke generation…
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation
Qihui Zhang, Munan Ning, Zheyuan Liu +7
Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
Adaptive Distraction: Probing LLM Contextual Robustness with Automated Tree Search
Yanbo Wang, Zixiang Xu, Yue Huang +6
Large Language Models (LLMs) often struggle to maintain their original performance when faced with semantically coherent but task-irrelevant contextual information. Although prior…
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
Jiayi Ye, Yanbo Wang, Yue Huang +9
LLM-as-a-Judge has been widely utilized as an evaluation method in various benchmarks and served as supervised rewards in model training. However, despite their excellence in many…
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
Han Bao, Yue Huang, Yanbo Wang +7
Large Vision-Language Models (LVLMs) have become essential for advancing the integration of visual and linguistic information. However, the evaluation of LVLMs presents significant…