Code is available at github.com/spcl/QuaRot .

A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.

Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated.

Reveals Large Language Models' Strengths and Biases In this supplementary materials we show additional results mentioned in the main paper. First, we give experimental details in Section A. Next, we show results for Llama 2 on the bandit task in Section B. Afterwards, we show in Section C.1 additional quantitative results for the expertise-based Section D provides additional details about the vision and language tasks. For more details on the code please refer to the README.md Section A.1) and the amount of compute required to reproduce our experiments (Section Section A.2) A.1 Prompt variations generated by meta-prompting Work done whilst visiting University of Tübingen 37th Conference on Neural Information Processing Systems (NeurIPS 2023). For all Vicuna-13B based experiments (bandit, reasoning and vision) we used a single Nvidia A100-40GB GPU.

In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles when they generate text in-context.

Co-first author order is arbitrary and may be swapped when citing this work.

This paper aims to efficiently enable Large Language Models (LLMs) to use multi-modal tools. Advanced proprietary LLMs, such as ChatGPT and GPT -4, have shown great potential for tool usage through sophisticated prompt engineering.