4 papers
EMO: Frustratingly Easy Progressive Training of Extendable MoE
Linghao Jin, Chufan Shi, Huijuan Wang +4
Sparse Mixture-of-Experts (MoE) models offer a powerful way to scale model size without increasing compute, as per-token FLOPs depend only on k active experts rather than the total…
Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities
Nuan Wen, Xuezhe Ma
Large language models (LLMs) are increasingly utilized as proxies for computational social analysis; yet, their ability to faithfully represent the "thick descriptions" (Geertz, 19…
Asymmetric Idiosyncrasies in Multimodal Models
Muzi Tao, Chufan Shi, Huijuan Wang +2
In this work, we study idiosyncrasies in the caption models and their downstream impact on text-to-image models. We design a systematic analysis: given either a generated caption o…
Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models
Xu Han, Linghao Jin, Xuezhe Ma +1
Fine-tuning pre-trained Vision-Language Models (VLMs) has shown remarkable capabilities in medical image and textual depiction synergy. Nevertheless, many pre-training datasets are…