Large Language Model
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
This method allows API users to easily generate, discover, and mix and match image concepts without prior knowledge of how to prompt the model.
WorldCoder,a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.
Language models are weak learners
A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.
Large Language Model for Hundreds of Vision-Language Tasks
To this end, we propose a new information transmission mechanism termed "super
Bowen Li
All the code and data are open-sourced at our website.
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Current methods for identifying adversarial prompts aimed at "attacking" LLMs and eliciting undesirable outputs are limited by several factors.
Open Vocabulary 3D Occupancy Prediction from Images Supplementary Material
In this supplementary material, we first give additional details about the method in Sec. 1. Queries used for zero-shot semantic segmentation. We do this for all the annotated classes in the dataset (second column). One can see that, for example, class name'manmade' lacks descriptive specificity. In the text description of this class, we can find "... buildings, walls, guard rails, fences, poles, street signs, traffic lights ..." and more. Table 1: Queries used for zero-shot semantic segmentation.
