Sec. 3. Next, we present the detailed structure of the pre-trained encoder in Sec. 4.

Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem.

This paper proposes a cross-modal in-context learning approach, empowering the frozen LLMs to achieve multiple audio tasks in a few-shot style without any parameter update.

In ICL, a conditional generative model (CGM) is prompted with a dataset and a prediction question and asked to generate a response.

In this appendix, Section A summarizes the architecture details of HSV A. Section B provides the key HSV A is insensitive to the number of latent features per unseen class in CZSL. HSV A perform steadily on the coarse-grained dataset (e.g., A W A1) while it is sensitive to

API calls for each position identified in a piece of text. Question Answering We use the Atlas model of Izacard et al. (2022) finetuned on Natural Questions Calculator Our calculator is based on a simple Python script and only supports the operators " It does not return any result for syntactically invalid equations. "=", "equals", "equal to", "total of", "average of" followed by a number, or (iii) contain at least three English text before generating API calls. Below, we list the prompts used to sample API calls for each tool considered. Your task is to add calls to a Question Answering API to a piece of text. Input: Joe Biden was born in Scranton, Pennsylvania. Output: Joe Biden was born in [QA("Where was Joe Biden born?")] Scranton, [QA("In Output: Coca-Cola, or [QA("What other name is Coca-Cola known by?")] Coke, is Your task is to add calls to a Calculator API to a piece of text.