3 citations · 4 across the 9 of their papers we have counts for
9 papers
RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation
Rui Min, Liang Yao, Shiyu Miao +5
A robust Multimodal Large Language Model (MLLM) for Earth Observation should maintain consistent interpretation and reasoning under realistic input variations. However, current Rem…
OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences
Ming Wen, Kun Yang, Jingyu Zhang +4
While safety alignment for Multimodal Large Language Models (MLLMs) has gained significant attention, current paradigms primarily target malicious intent or situational violations.…
PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation
Yuxuan Liu
The alignment of large language models with human values presents a critical challenge, particularly when balancing conflicting objectives like helpfulness and harmlessness. Existi…
ASI++: Towards Distributionally Balanced End-to-End Generative Retrieval
Yuxuan Liu, Tianchi Yang, Zihan Zhang +5
Generative retrieval, a promising new paradigm in information retrieval, employs a seq2seq model to encode document features into parameters and decode relevant document identifier…
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models
Jiaxing Chen, Yuxuan Liu, Dehu Li +5
The rise of Multimodal Large Language Models (MLLMs), renowned for their advanced instruction-following and reasoning capabilities, has significantly propelled the field of visual…
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition
Yuxuan Liu, Tianchi Yang, Shaohan Huang +6
Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations. However, the alignment and coverage of LLM-based evaluations are often limited…
Text Diffusion with Reinforced Conditioning
Yuxuan Liu, Tianchi Yang, Shaohan Huang +6
Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio. Due to their adaptiveness in iterative refinement, they provide a st…
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Zhaoyang Wang, Shaohan Huang, Yuxuan Liu +8
Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and cl…
Calibrating LLM-Based Evaluator
Yuxuan Liu, Tianchi Yang, Shaohan Huang +6
Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation qua…