Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
ReasonFormer enhances performance across 11 datasets through modular reasoning.
Key Findings
Methodology
ReasonFormer employs a modular and compositional reasoning framework inspired by dual-process theory. It decouples representation and reasoning modules to capture different cognitive levels. Pre-trained reasoning modules focus on specific skills like logic and simple QA. Dynamic activation and composition of reasoning modules solve multiple tasks.
Key Results
- ReasonFormer significantly boosts performance across 11 datasets, e.g., a 4.2% increase on the ReClor dataset.
- Few-shot experiments show better generalization by composing pre-trained skills.
- Analysis reveals distinct reasoning skills activated for different tasks, validating modularity.
Significance
This study introduces a modular reasoning framework to address limitations in existing language models for complex reasoning tasks. By mimicking human reasoning processes, ReasonFormer demonstrates strong adaptability in multi-task environments, advancing language model applications in cognitive science.
Technical Contribution
ReasonFormer decouples representation and reasoning modules, enabling step-by-step execution of complex reasoning processes. It not only enhances multi-task performance but also offers new engineering possibilities like dynamic skill composition.
Novelty
ReasonFormer is the first to apply dual-process theory from cognitive science to AI reasoning models, innovatively achieving modular and compositional reasoning, distinct from traditional single-call language models.
Limitations
- In some complex tasks, the model may fail to activate all necessary reasoning skills, leading to performance drops.
- Pre-training reasoning modules require substantial resources, limiting application in resource-constrained environments.
Future Work
Future work may explore applying ReasonFormer to more tasks and optimizing the pre-training process of reasoning modules to reduce resource demands.
AI Executive Summary
In complex decision-making, humans typically solve problems through modular and compositional reasoning processes. However, existing language models struggle to simulate this process effectively. ReasonFormer addresses this issue by introducing a modular reasoning framework. The framework consists of representation and reasoning modules, responsible for automatic and controlled thinking respectively. Pre-trained reasoning modules focus on specific skills like logic and simple QA. Dynamic activation and composition of reasoning modules solve multiple tasks. ReasonFormer significantly boosts performance across 11 datasets, e.g., a 4.2% increase on the ReClor dataset. Few-shot experiments show better generalization by composing pre-trained skills. Analysis reveals distinct reasoning skills activated for different tasks, validating modularity. Future work may explore applying ReasonFormer to more tasks and optimizing the pre-training process of reasoning modules to reduce resource demands.
Deep Analysis
Background
In recent years, language models have shown impressive performance in natural language processing tasks. However, existing models still face limitations in complex reasoning tasks. ReasonFormer aims to address this issue by introducing dual-process theory from cognitive science.
Core Problem
Existing language models struggle to simulate human complex reasoning processes, making it difficult to effectively activate and compose different reasoning skills. This limits the model's adaptability in multi-task environments.
Innovation
ReasonFormer achieves step-by-step execution of complex reasoning processes through modular and compositional reasoning framework. It not only enhances multi-task performance but also offers new engineering possibilities like dynamic skill composition.
Methodology
- �� Representation module captures contextual understanding, serving as the foundation for reasoning.
- �� Reasoning modules are pre-trained for specific skills like logic and QA.
- �� Reasoning router decides activated skills per step and controls reasoning depth through stop gate.
Experiments
ReasonFormer is evaluated on 11 datasets, including ReClor, CSQA, and ARC. Accuracy and exact match are used as metrics, showcasing the model's performance boost in multi-task environments.
Results
ReasonFormer significantly boosts performance across multiple datasets, especially in logic reasoning and commonsense QA tasks. Few-shot experiments show better generalization by composing pre-trained skills.
Applications
ReasonFormer can be applied to complex reasoning problems in multi-task environments, particularly where dynamic composition of different reasoning skills is required.
Limitations & Outlook
The model may fail to activate all necessary reasoning skills in some complex tasks, leading to performance drops. Pre-training reasoning modules require substantial resources, limiting application in resource-constrained environments.
Plain Language Accessible to non-experts
Imagine you are preparing a lavish dinner in the kitchen. You need different tools and steps to complete this task. First, you need a basic workbench to prepare ingredients, similar to the representation module in ReasonFormer. Next, you need various kitchen tools to process ingredients, like knives and pots, akin to reasoning modules. Each tool has a specific function, such as chopping vegetables or boiling soup. You can dynamically select and combine these tools as needed to complete the cooking process. This is how ReasonFormer works, solving complex problems through modular and compositional reasoning.
ELI14 Explained like you're 14
Imagine you're playing a complex game that requires solving various puzzles. You have a toolbox with different tools, each with a specific purpose. For example, some tools help you crack codes, while others help you find hidden clues. You need to choose the right tool for each puzzle to solve the problem. This is like how ReasonFormer works, solving complex problems through modular and compositional reasoning. Each module is like a tool that helps you better understand and solve the problem.
Glossary
ReasonFormer
A modular and compositional reasoning framework that simulates human complex decision-making processes.
Used in the paper to solve multi-task reasoning problems.
Representation Module
Responsible for automatic thinking, capturing contextual representations of problems.
Used in ReasonFormer for basic problem understanding.
Reasoning Modules
Pre-trained to focus on specific reasoning skills like logic and simple QA.
Used in ReasonFormer to solve specific reasoning tasks.
Reasoning Router
Decides activated skills per reasoning step and controls reasoning depth through stop gate.
Used in ReasonFormer for dynamic skill composition.
Stop Gate
Controls the depth of the reasoning process, deciding when to stop reasoning.
Used in ReasonFormer to optimize the reasoning process.
Open Questions Unanswered questions from this research
- 1 How to effectively pre-train reasoning modules in resource-constrained environments?
- 2 How to further enhance model performance in complex tasks?
Applications
Immediate Applications
Multi-task Reasoning
ReasonFormer can be used to solve multi-task problems requiring dynamic composition of different reasoning skills.
Long-term Vision
Cognitive Science Applications
By simulating human reasoning processes, ReasonFormer can advance language model applications in cognitive science.
Abstract
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making. Inspired by dual-process theory in cognitive science, the representation module (automatic thinking) and reasoning modules (controlled thinking) are decoupled to capture different levels of cognition. Upon the top of the representation module, the pre-trained reasoning modules are modular and professional in specific and fundamental reasoning skills (e.g., logic, simple QA, etc). To mimic the controlled compositional thinking process, different reasoning modules are dynamically activated and composed in both parallel and cascaded manners to control what reasoning skills are activated and how deep the reasoning process will be reached to solve the current problems. The unified reasoning framework solves multiple tasks with a single model, and is trained and inferred in an end-to-end manner. Evaluated on 11 datasets requiring different reasoning skills and complexity, ReasonFormer demonstrates substantial performance boosts, revealing the compositional reasoning ability. Few-shot experiments exhibit better generalization ability by learning to compose pre-trained skills for new tasks with limited data, and decoupling the representation module and the reasoning modules. Further analysis shows the modularity of reasoning modules as different tasks activate distinct reasoning skills at different reasoning depths.