Foundation Models for Text Generation-Reference-Cited by-同舟云学术

Foundation Models for Text Generation

Published:2023 Issue: Volume: Page:227-311
ISSN:2365-3051
Container-title:Artificial Intelligence: Foundations, Theory, and Algorithms
language:
Short-container-title:

Author:

Paaß Gerhard,Giesselbach Sven

Abstract

AbstractThis chapter discusses Foundation Models for Text Generation. This includes systems for Document Retrieval, which accept a query and return an ordered list of text documents from a document collection, often evaluating the similarity of embeddings to retrieve relevant text passages. Question Answering systems are given a natural language question and must provide an answer, usually in natural language. Machine Translation models take a text in one language and translate it into another language. Text Summarization systems receive a long document and generate a short summary covering the most important contents of the document. Text Generation models use an autoregressive Language Model to generate a longer story, usually starting from an initial text input. Dialog systems have the task of conducting a dialog with a human partner, typically not limited to a specific topic.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-23190-2_6

Reference270 articles.

1. S. Aarohi and R. Abhinav. BIG-bench. Google, June 20, 2022. url:https://github.com/google/BIG-bench/blob/936c4a5876646966344349b28ae187c556938ec4/docs/paper/BIG-bench. pdf (visited on 06/20/2022).

2. Z. Abbasiyantaeb and S. Momtazi. “Text-Based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey”. 2020. arXiv: 2002.06612.

3. D. Adiwardana et al. “Towards a Human-like Open-Domain Chatbot”. 2020. arXiv: 2001.09977.

4. A. Aghajanyan, A. Shrivastava, A. Gupta, N. Goyal, L. Zettlemoyer, and S. Gupta. “Better Fine-Tuning by Reducing Representational Collapse”. Aug. 6, 2020. arXiv: 2008.03156.

5. F. Akhbardeh et al. “Findings of the 2021 Conference on Machine Translation (WMT21)”. In: Sixth Conf. Mach. Transl. Pp 1–88 Assoc. Comput. Linguist. (Nov. 10, 2021), p. 88.