5 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…
Handle with CARE: Can LLMs Reproduce How Online Communities React?
Nuan Wen, Xuezhe Ma, Chanbin Lim
Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick descriptions" (Geertz, 1973) of human commun…
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…
Towards Chapter-to-Chapter Context-Aware Literary Translation via Large Language Models
Linghao Jin, Li An, Xuezhe Ma
Discourse phenomena in existing document-level translation datasets are sparse, which has been a fundamental obstacle in the development of context-aware machine translation models…
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…