Edit: LRS is sleepy
This paper comprehensively reviews nearly 750 papers on "Basic Model Inference", focuses on the latest progress in various inference tasks, methodologies and benchmarks, and elaborates on the current status, technical limitations and future possibilities of large models in various inference tasks.
Reasoning, as a key competency in complex problem solving, plays a central role in a variety of real-world scenarios, such as negotiation, medical diagnosis, and criminal investigation, and is also a fundamental methodology in the field of artificial general intelligence (AGI).
With the continuous development of basic models, there is more and more attention to the ability of large models in inference tasks.
Recently, a dozen institutions jointly published a paper describing some of the groundbreaking foundational models designed or applied for inference tasks and highlighting recent advances in various inference tasks, methods, and evaluation criteria.
Address: https://arxiv.org/abs/2312.11562
Paper Repositories: https://github.com/reasoning-survey/Awesome-Reasoning-Foundation-Models
In addition, the paper delves into the potential future direction of the emergence of reasoning ability in the underlying model, and the relevance of multimodal Xi, autonomous surrogate, and super-alignment in the context of inference.
By exploring these future research directions, the researchers hope to stimulate researchers' interest in exploring this field, promote the further development of basic models in inference, and contribute to the development of AGI.
introduction
This paper provides a comprehensive overview of the current state and future potential of the underlying model in the inference task. Reasoning plays a central role in solving a variety of real-world complex problems, especially in the context of artificial general intelligence (AGI).
The researchers delve into some of the groundbreaking foundational models that have been proposed or can be used for inference, focusing on the latest advances in various inference tasks, methodologies, and benchmarks, and delving into the possible future directions of these developments.
This paper discusses the relevance of multimodal learning Xi, autonomous agents, and super-alignment in the context of reasoning, aiming to stimulate further research and development in this field.
The underlying model has shown significant results in a variety of domains, including natural language processing, computer vision, and multimodal tasks.
However, there is a growing interest in whether these models can demonstrate human-like reasoning abilities.
The dissertation aims to address this issue by providing a systematic and comprehensive investigation, focusing on recent advances in multimodal and interactive reasoning, which are closer to mimicking human reasoning styles.
This paper outlines the importance of inference in AI and the potential of foundational models to advance this field, hoping to provide a comprehensive understanding of the use of foundational models for inference, as well as their current capabilities, limitations, and future possibilities, contributing to the development of artificial general intelligence.
Background:
The dissertation defines the reasoning, which is essential to establish the scope and context of the rest of the section.
The paper discusses the multifaceted nature of inference and recognizes its role in different AI applications.
The dissertation covers various aspects of reasoning, such as philosophy, logic, natural language processing (NLP), and different types of reasoning, including deductive, retrospective, and inductive reasoning.
In addition, mathematical expressions are discussed, including propositional logic, predicate logic, set theory, graph theory, conditional probability, and formal systems.
In addition, the paper discusses basic models and their recent advances, delves into language foundation models and language cues, visual foundation models and visual cues, and the integration of these models to enhance visual tasks, and the background section also touches on multimodal foundation models, highlighting their potential applications in inference.
By providing this comprehensive context, the paper paves the way for a more detailed exploration of the implementation and further development of inference in AI, particularly through the use of foundational models. This foundational work is critical to understanding the current state and future potential of AI inference, helping to advance the broader goals of artificial intelligence (AGI).
Concept: Inference task
First, the paper explores a variety of reasoning tasks in the context of AI foundation models, including common-sense reasoning, mathematical reasoning, logical reasoning, causal reasoning, visual reasoning, auditory reasoning, multimodal reasoning, surrogate reasoning, and more, each of which represents a unique aspect of reasoning and demonstrates the diversity and complexity of the field. Here's a closer look at these inference tasks:
Common sense reasoning: involves making inferences based on everyday knowledge of the world.
Common-sense reasoning is essential for AI to interpret, predict, and act according to human expectations. The task here is to enable the model to grasp intuitive knowledge that humans would find obvious, such as social norms or physical laws.
Mathematical Reasoning: This task focuses on the ability of AI to solve mathematical problems, requiring an understanding of mathematical concepts, symbols, and the ability to perform calculations.
This is a test of the model's logical and analytical abilities, especially when it comes to solving equations, proving theorems, or interpreting graphs and data.
Logical reasoning: Logical reasoning is about applying formal rules of logic in order to reach a conclusion.
It involves tasks like syllogisms, deriving conclusions from premises and requires a deep understanding of logical structures and their proper application.
Causal Reasoning: The focus here is on understanding cause and effect. Causal reasoning is essential for predicting outcomes, understanding complex systems, and making decisions based on the possible impact of different actions. It involves identifying causal connections and understanding how changes in one aspect affect another.
Visual Reasoning: This task combines visual perception and reasoning skills. It involves interpreting and making inferences from visual data such as images or videos. This can include recognizing objects, understanding scenes, and inferring relationships or stories from visual cues.
Auditory reasoning: Similar to visual reasoning, auditory reasoning is about understanding and making inferences from auditory data. It involves tasks such as speech recognition, understanding situations and emotions in spoken language, and interpreting nonverbal auditory cues, such as pitch or rhythm.
Multimodal Reasoning: Multimodal reasoning involves integrating and understanding information from multiple modalities, such as text, images, and audio. This is essential for AI to understand and interact in a world where information comes in all its forms. It needs to be able to combine and reason sensibly across these different data types.
Proxy inference: This refers to inference performed by autonomous agents. It involves decision-making, planning, and Xi in a dynamic environment. Agent inference is critical for applications such as robotics or autonomous vehicles, where AI needs to navigate, interact with the environment, and make decisions in real-time.
Together, these inference tasks represent a wide range of cognitive abilities that are being processed in the development of AI-based models. Each task presents unique challenges and requires a different approach, reflecting the multifaceted nature of human intelligence and reasoning.
Method: Base model
The article outlines several key technologies used in the foundational model that are critical to advancing AI inference capabilities. Each technology plays a key role in improving the performance and applicability of these models.
Here's a closer look at these base model technologies:
Pre-training: Pre-training is a basic technique where models are initially trained on large datasets and then fine-tuned for specific tasks.
This process allows modelling to Xi a wide range of generic knowledge and skills, which can then be adapted to more specialized applications. Pre-training typically involves using a large corpus of text, images, or other data types to give the model a broad understanding of the world.
Fine-tuning: After pre-training, fine-tuning adjusts the model to fit a specific task or dataset. This process involves additional training, usually on smaller, task-specific datasets. Fine-tuning improves the model's performance on that task by adapting the general knowledge gained during pre-training to the nuances and needs of a particular application.
Alignment training: This technique aims to align the model's output with specific goals or values, especially those that reflect ethical standards or user preferences. Alignment training is essential to ensure that the underlying model behaves in a way that is beneficial and acceptable to humans, especially in scenarios where ethics are critical.
Expert Hybrid Model (MoE): Expert Hybrid is an approach in which different models partially focus on different tasks or data types. This technique allows for more efficient and effective processing, as each "expert" in the model can work on the aspect of the problem that it is best suited to. MoE can improve performance and computational efficiency.
Contextual Xi: Contextual Xi refers to the ability of a model to learn Xi and adapt from the new information presented from its inputs without the need for explicit retraining. This is a form of small- or zero-shot Xi where the model demonstrates flexibility and adaptability using the context provided in the query to understand and respond appropriately.
Autonomous agent: This technique involves developing a model that can operate as an autonomous agent, interacting with the environment in real-time and learning from Xi. Autonomous agents are designed to make decisions, take action, and adjust based on experience, simulating intelligent behavior in dynamic and complex environments.
Together, these technologies contribute to the versatility and effectiveness of AI's foundational models. They enable these models to Xi learn from large amounts of data, adapt to specific tasks, align with human values, focus on domains, learn Xi from context, and operate autonomously. Each technique involves different aspects of learning Xi and reasoning, making the underlying model more powerful and applicable in a wide range of scenarios.
Outlook: Challenges, Limitations, Risks and the Future
The challenges, limitations, and risks of foundational models in AI are discussed in depth. This critical analysis is essential to understanding the current boundaries and potential pitfalls of these advanced models. Here's a closer look at these aspects:
Hallucinations: A significant challenge with foundational models is their tendency to generate information that seems plausible but is actually false or meaningless, often referred to as "hallucinations." These errors are particularly problematic in applications that require high precision and reliability, such as medical diagnostics or legal advice.
Context length issues: The underlying model often struggles to handle long-form contexts. This limitation affects their ability to understand and reason about long documents or conversations, which are essential for tasks such as summarizing long passages or maintaining a coherent conversation in extended interactions.
Challenges Xi multimodality: Although basic models have shown potential for multimodality Xi (integrating text, images, audio, etc.), it is still challenging to effectively combine these different data types. The complexity of accurately interpreting and correlating cross-modal information is a significant obstacle.
Efficiency and cost: The training and deployment of the underlying model is resource-intensive, requiring a lot of computing power and energy. This raises concerns about cost, accessibility, and environmental impact, especially given the trend towards larger and larger models.
Preference alignment: Ensuring that the underlying model aligns with human values and preferences is a complex challenge. This involves not only technical considerations, but also ethical and social factors, as different cultures and individuals may have different expectations and standards.
Multilingual support: Developing foundational models that effectively support multiple languages, especially low-resource languages, is a major challenge, a limitation that affects the global applicability and fairness of these models.
Safety and reliability: Ensuring the safety and reliability of the underlying model, especially in high-risk scenarios, is a major concern. This includes preventing harmful output, ensuring resilience against hostile attacks, and maintaining robustness in a diverse and unpredictable environment.
Privacy concerns: The use of large-scale data in training base models raises privacy concerns. Ensuring data confidentiality and user privacy, especially when dealing with sensitive personal information, is paramount.
Explainability and transparency: The underlying model often operates as a "black box" with limited interpretability. Understanding how these models arrive at specific decisions or outputs is challenging, which complicates diagnosing errors, ensuring fairness, and building user trust.
Ethical and social implications: The deployment of the underlying model has a wide range of ethical and social implications, including potential job displacement, reinforced biases, and impacts on information dissemination and consumption, which are critical.
summary
This review clarifies the evolution path of the basic model in the field of inference, and shows a significant increase in complexity and effectiveness from the initial stage to the current progress. While the authors acknowledge the significant advances made by data-driven thinking, it is important to objectively understand the advantages and limitations of large models.
In this context, it is imperative to emphasize the importance of improving its interpretability and security. The authors also note that in all the papers surveyed in this paper, there is no consensus on how to continue to advance the reasoning capabilities of the underlying model to a transhuman level (e.g., winning a medal in the International Mathematical Olympiad or even solving open mathematical problems).
In conclusion, while foundational models offer exciting possibilities in inference tasks, it is crucial to look at their development and application through a critical lens. Acknowledging the challenges, limitations, and risks of large language model (LLM)-based inference is critical. By doing so, we can promote responsible and deliberate progress in this area, ensuring that robust and reliable reasoning systems are built.
Resources:
https://osf.io/preprints/osf/ac4sp