Abstract

Large language models (LLMs) are commonly used for solving
natural language tasks like question answering or generating text. However,
their outputs can be factually incorrect, untruthful, outdated, or
otherwise limited by the knowledge encoded in the trained parameters. One
promising way to mitigate this problem is to allow LLMs to interact with
external tools, such as an IR system, a calculator, a knowledge graph, a
private database, or a code interpreter.

Visual Abstract

Llm tools

Slides

Presentation recordings

Readings

1. Parisi, Aaron & Zhao, Yao & Fiedel, Noah. (2022). TALM: Tool Augmented Language Models. doi.org/10.48550/arXiv.2205.12255. 
   
2.  Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, Thomas Scialom (2022). Toolformer: Language Models Can Teach Themselves to Use Tools. doi.org/10.48550/arXiv.2302.04761
3. https://platform.openai.com/docs/plugins/introduction
   
4.  https://python.langchain.com/en/latest/