Large Language Model
Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions Heng Li
Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. However, current VLN frameworks often rely on static environments and optimal expert supervision, limiting their real-world applicability. To address this, we introduce Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities and relaxing key assumptions.
Tackling Uncertain Correspondences for Multi-Modal Entity Alignment Liyi Chen
Recently, multi-modal entity alignment has emerged as a pivotal endeavor for the integration of Multi-Modal Knowledge Graphs (MMKGs) originating from diverse data sources.
Streaming Long Video Understanding with Large Language Models
Y et, within the domain of video understanding, long video sequences pose a formidable challenge. Work done during an internship in Shanghai AI Laboratory.
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
Despite the recent strides in crafting specific architectures for time-series forecasting and developing pre-trained universal models, a comprehensive examination of their capability in accommodating varied-horizon forecasting during inference is still lacking.
Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass Ethan Shen Alan Fan Sarah Pratt Jae Sung Park Matthew Wallingford Sham Kakade Ari Holtzman Ranjay Krishna
Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggestions.
SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries.
Improving Generalization and Convergence by Enhancing Implicit Regularization Mingze Wang 1,3, Jinbo Wang 1, 3 Haotian He1,3 Zilin Wang 1
We show that IRE can be practically incorporated with generic base optimizers without introducing significant computational overload.
Unveiling the Tapestry of Consistency in Large Vision-Language Models
However, when faced with prompts in different sizes of solution spaces, L VLMs fail to always give consistent answers regarding the same knowledge point.
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates
Public LLMs such as the Llama 2-Chat underwent alignment training and were considered safe. Recently Qi et al. [2024] reported that even benign fine-tuning on seemingly safe datasets can give rise to unsafe behaviors in the models.
Knowledge Circuits in Pretrained Transformers
In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge.