Large Language Model
Generating Anatomically Controllable Consistent Text-to-3D Animals
Our method is capable of generating novel imaginary animals that previous text-to-3D generative methods are unable to create.
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models
We find that a small subset of "cherry" parameters exhibit a
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
Given the description of an environment and a task, we use an LLM guided by the GIF-MCTS method to iteratively generate and refine a candidate CWM. The candidate's correctness is evaluated by checking if it correctly
ColJailBreak: Collaborative Generation and Editing for Jailbreaking Text-to-Image Deep Generation Yizhuo Ma
Warning: This paper contains model outputs that are offensive in nature.
Training Code Language Models with Comprehensive Semantics Reasoning
Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data
ETO: Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses
During the coarse matching phase, we organize multiple homography hypotheses to approximate continuous matches. Each hypothesis encompasses several features to be matched, significantly reducing the number of features that require enhancement via transformers.
