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Original Paper
Original Paper: https://arxiv.org/abs/2302.07842
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, Thomas Scialom
Abstract:
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists in calling external modules such as a code interpreter. LMs can leverage these augmentations separately or in combination via heuristics, or learn to do so from demonstrations. While adhering to a standard missing tokens prediction objective, such augmented LMs can use various, possibly non-parametric external modules to expand their context processing ability, thus departing from the pure language modeling paradigm. We therefore refer to them as Augmented Language Models (ALMs). The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks and even outperforming most regular LMs on several benchmarks. In this work, after reviewing current advance in ALMs, we conclude that this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues.
Summary Notes
Exploring Augmented Language Models: A Comprehensive Overview
In the ever-evolving field of artificial intelligence, large language models (LLMs) have been at the forefront of progress in understanding and generating human language.
However, they face challenges like producing errors that seem plausible (known as hallucinations), scalability problems, and limitations in performing reasoning tasks.
This post introduces the concept of Augmented Language Models (ALMs), which aim to overcome these issues by integrating advanced reasoning abilities and the capacity to interact with external resources.
Introduction
Large Language Models have significantly advanced machine understanding of language. Despite this, their effectiveness is often limited by inherent shortcomings. Augmented Language Models (ALMs) represent a breakthrough in AI, enhancing traditional LMs with superior reasoning skills and the ability to engage with external tools and databases, aiming to improve performance and expand the functionalities of LMs.
Enhancing Reasoning in LMs
Improving reasoning abilities is crucial for ALMs. Here's how they're advancing:
- Advanced Reasoning Techniques: ALMs use methods like chain-of-thought prompting, enabling the model to process intermediate steps in reasoning, which helps tackle complex cognitive tasks more efficiently.
- Recursive Prompting: This method breaks down complex problems into smaller, more manageable parts, greatly improving the model's problem-solving skills.
Interaction with External Tools
A key feature of ALMs is their ability to use external tools or models, such as search engines and code compilers, transforming them from static to dynamic problem-solvers capable of:
- Enhanced Functionality: Through external tools, ALMs can perform a wider range of tasks beyond traditional language processing, like running code or accessing up-to-date information.
- Informed Decision-Making: The ability to use real-world data allows ALMs to make decisions and act based on current, accurate information.
Training ALMs: Reasoning, Tool Use, and Action
Several strategies are employed to teach ALMs these advanced skills:
- Supervised Learning: Using human-annotated examples helps train ALMs to reason and utilize tools correctly.
- Reinforcement Learning from Human Feedback: This method improves ALMs by adjusting their actions based on human feedback, focusing on the outcomes.
- Self-supervised Learning: ALMs can also learn from their own predictions, enhancing their reasoning and tool-interaction abilities.
Discussion
The rise of ALMs marks a significant leap forward but also prompts discussions on their advantages and the ethical implications. They offer better accuracy, interpretability, and adaptability, yet pose ethical challenges as they interact more autonomously with the real world.
Conclusion
Augmented Language Models are pioneering a new phase in language technology, aiming to surpass traditional LMs by incorporating sophisticated reasoning skills and the ability to interface with the external world. This approach promises to create more advanced, dependable, and interactive language-based applications.
As we explore this promising yet intricate domain, it's vital to ensure that innovation is balanced with ethical considerations.
The development of ALMs should not only push the boundaries of technology but also proceed in a way that is responsible and in line with societal norms.
The journey of ALMs is just beginning, with the potential to significantly alter the landscape of natural language processing and AI.
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