# Augmented Language Models: a Survey

**Authors:** Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom  
**arXiv:** [2302.07842](https://arxiv.org/abs/2302.07842)  
**Date:** 2023-02-15  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2302.07842v1/

## Key points

- The paper titled "Augmented Language Models: a Survey" explores the use of Augmented Language Models (ALMs) in natural language processing tasks.
- ALMs are language models that are augmented with reasoning skills and the ability to use external tools such as code interpreters to expand their context processing ability.
- The missing token objective allows ALMs to learn to reason and use tools while still performing standard natural language tasks and even outperforming most regular LMs on several benchmarks.
- ALMs have the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues.
- ALMs can improve model performance on complex tasks by decomposing them into simpler subtasks using reasoning skills.
- ALMs can leverage external modules such as code interpreters to perform tasks beyond pure language modeling.

## Summary

The paper titled "Augmented Language Models: a Survey" is authored by Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun and Thomas Scialom. The survey explores the use of Augmented Language Models (ALMs) in natural language processing tasks. ALMs are language models that are augmented with reasoning skills and the ability to use external tools such as code interpreters to expand their context processing ability. These augmentations can be used separately or in combination via heuristics or learned from demonstrations. The missing token objective allows ALMs to learn to reason and use tools while still performing standard natural language tasks and even outperforming most regular LMs on several benchmarks. The authors review current advances in ALMs and conclude that this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency and scalability issues. They also note that ALMs have the potential to improve model performance on complex tasks by decomposing them into simpler subtasks using reasoning skills. Additionally, they discuss how ALMs can leverage external modules such as code interpreters to perform tasks beyond pure language modeling. Overall, this survey highlights the potential benefits of incorporating reasoning skills and external tools into language models for improved performance on complex natural language processing tasks. It demonstrates how these augmentations can lead to better model performance while addressing some of the common limitations of traditional LMs.