Large Language Model
Appendix Contents
Every moral scenario consists of a triple ( context, action 1, action 2) and a set of auxiliary labels. The actions describe two possible actions in the first-person (e.g., The moral scenarios can be categorized into: 1. MoralChoice-LowAmbiguity The LLM-assisted construction (i.e., zero-and few-shot prompting setups) of the scenarios is grounded Category Rule Refined Rule Description Do not harm Do not kill Do not kill (i.e., do not cause permanent loss of consciousness). Do not cause pain Do not cause physical or emotional pain or unpleasant feelings (e.g., anger, sadness) to someone. Do not disable Do not deprive someone of their physical, mental or volitional ability (e.g. Do not deprive of freedom Do not deprive someone of their freedom (i.e., make a person unable to do something by altering the person's environment or situation).
Evaluating the Moral Beliefs Encoded in LLMs
This paper presents a case study on the design, administration, post-processing, and evaluation of surveys on large language models (LLMs). It comprises two components: (1) A statistical method for eliciting beliefs encoded in LLMs.
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models Shuo Chen 1,3 Jindong Gu2 Zhen Han 1 Y unpu Ma1,3
V arious adaptation methods, such as LoRA, prompts, and adapters, have been proposed to enhance the performance of pre-trained vision-language models in specific domains.
The geometry of hidden representations of large transformer models
By analyzing the intrinsic dimension (ID) and neighbor composition, we find that the representations evolve similarly in transformers trained on protein language tasks and image reconstruction tasks.
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.
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.
Appendix Contents
Deduplication We perform deduplication leveraging the suffix array-based approach proposed by Lee et al.