Prompt Learning

[TOC] Title: Toolformer: Language Models Can Teach Themselves to Use Tools 2023 Author: Timo Schick et. al. META AI research Publish Year: 9 Feb 2023 Review Date: Wed, Mar 1, 2023 url: https://arxiv.org/pdf/2302.04761.pdf Summary of paper Motivation LMs exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also struggle with basic functionality, such as arithmetic or factual lookup. Contribution In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds....

[TOC] Title: Multimodal Chain of Thought Reasoning in Language Models Author: Zhuosheng Zhang et. al. Publish Year: 2023 Review Date: Wed, Feb 8, 2023 url: https://arxiv.org/pdf/2302.00923.pdf Summary of paper Motivation LLMs have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. to elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning....

[TOC] Title: What Makes Good Examples for Visual in Context Learning Author: Yuan Zhang et. al. Publish Year: 1 Feb 2023 Review Date: Mon, Feb 6, 2023 url: https://arxiv.org/pdf/2301.13670.pdf Summary of paper Motivation in this paper, the main focus is on an emergent ability in large vision models, known. as in-context learning this concept has been well-known in natural language processing but has only been studied very recently for large vision models....

[TOC] Title: Language Models as Zero Shot Planners: Extracting Actionable Knowledge for Embodied Agents Author: Wenlong Huang et. al. Publish Year: Mar 2022 Review Date: Mon, Sep 19, 2022 Summary of paper Motivation Large language models are learning general commonsense world knowledge. so this paper, the author investigate the possibility of grounding high-level tasks, expressed as natural language (e.g., “make breakfast”) to a chosen set of action steps (“open fridge”). Contribution they found out that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training....