A Survey of Reasoning: with Foundation Models

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation mod- els, there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, meth- ods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advance-
ments in reasoning with foundation models, e.g. Large Language Models (LLMs), and contribute to the development of AGI.

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FAQ's

- The purpose of a foundation model is to serve as a pre-trained, generalized model that can be fine-tuned for various downstream tasks in natural language processing, computer vision, and other domains. It aims to capture a broad understanding of the world's knowledge and context.

Building a foundation model requires:
- Massive datasets: Training data can include text, code, and other forms of information, often requiring significant computing power and resources.
- Powerful computing infrastructure: Training these models often involves complex algorithms and specialized hardware.
- Specific training techniques: Techniques like self-supervised learning help models learn patterns and relationships within the data without explicit human labeling.

- Generality: Foundation models are trained on diverse data, allowing them to be applied to various tasks beyond their initial training purpose.
- Adaptability: These models can be fine-tuned for specific tasks like question answering, text summarization, or code generation.
- Scalability: The knowledge gained from the massive datasets allows them to perform well on new tasks with less training data compared to traditional models.

Traditional AI models are typically trained for a specific task using a specific dataset. Foundation models, however, are:
- Pre-trained on massive datasets: This allows them to learn more generalizable representations compared to models trained on smaller, task-specific data.
- Adaptable to various tasks: Once trained, they can be fine-tuned for different downstream tasks through additional training with less data. This saves time and resources compared to training a new model from scratch for each task.

Reasoning is an active area of research in the context of foundation models. Traditionally, foundation models haven't explicitly focused on reasoning tasks like logical deduction or causal inference. However, researchers are exploring ways to:
- Integrate symbolic reasoning techniques with foundation models.
- Train foundation models on data that explicitly encourages reasoning skills.

- Improved performance: Foundation models can achieve state-of-the-art performance on various tasks, often surpassing traditional models.
- Efficiency and cost savings: Fine-tuning a foundation model for a new task requires less data and computational resources compared to training a new model from scratch.
- Faster innovation: Foundation models accelerate research and development in AI by providing a powerful pre-trained base for tackling new challenges.

- Explainability: Understanding how foundation models arrive at their results can be challenging, limiting their use in applications requiring transparency.
- Bias: Biases present in the training data can be reflected in the model's outputs, requiring careful consideration and mitigation strategies.
- Computational cost: Training and running large foundation models can be computationally expensive, requiring significant hardware resources.

Foundation models can be categorized by the type of data they are trained on:
- Language models : Trained on massive amounts of text data, these models can perform tasks like text generation, translation, and question answering.
- Vision models : Trained on large image datasets, these models can perform tasks like image classification, object detection, and image generation.
- Multimodal models: Combine training on both text and image data, allowing them to understand relationships between the two modalities.

- Data scarcity: Training models for reasoning tasks often requires large amounts of data with explicit reasoning patterns, which can be scarce.
- Computational complexity: Reasoning algorithms can be computationally expensive, especially when integrated with large foundation models.
- Evaluation metrics: Developing effective metrics to assess the reasoning abilities of these models remains an ongoing challenge.

Adding reasoning capabilities to foundation models could lead to advancements in areas like:
- Question answering systems: Providing more comprehensive and informative answers by considering context and reasoning about the query.
- Scientific discovery: Helping scientists make connections between data points and identify potential research avenues.
- Robotics: Enabling robots to reason about their environment and perform actions based on logical reasoning.

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