Investigating Generalization from Atomic Skills to Complex Reasoning Tasks

Conceptos Básicos

Atomic skills can be induced to generalize to complex reasoning tasks through hierarchical curriculum learning.

Resumen

Current language models struggle with complex reasoning tasks that require a combination of atomic skills. This study investigates the generalization of atomic skills to complex reasoning tasks. The authors propose a probing framework and a hierarchical curriculum learning strategy. Results show that atomic skills do not spontaneously generalize but can be induced through training. Skill generalization is effective in inter-domain data, and compositional tasks benefit atomic skills. Continuous training on compositional tasks prevents catastrophic forgetting of atomic skills.

Estadísticas

LLaMA-2 improves from 13.60% to 28.76% with zero-shot prompting in HARD after applied learning.
HCL results in significant improvements in arithmetic accuracy for all types of operations.
Mixture training yields similar results to individual training for skill generalization.

Citas

"We argue that skill enhancement can generalize to complex tasks, as the response format for complex tasks is a composition of atomic skills."
"Our findings offer valuable guidance for designing better training strategies for complex reasoning tasks."
"Applied learning alone cannot sufficiently enhance atomic skills, highlighting the importance of a skill training stage in hierarchical curriculum learning."

Ideas clave extraídas de

Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks

by Yuncheng Hua... a las arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09479.pdf

Consultas más profundas

How can implicit atomic skills impact the generalization process?

Implicit atomic skills, although not explicitly demonstrated in responses, play a crucial role in the generalization process of language models. These underlying skills are essential for understanding and processing complex reasoning tasks effectively. While explicit atomic skills can be directly applied to tasks, implicit skills contribute to the overall proficiency of the model in handling various scenarios. They provide a foundation for more advanced reasoning capabilities and help models adapt to new challenges by leveraging their existing knowledge base.

What are the implications of this research on improving real-world applications of language models?

The findings from this research have significant implications for enhancing the performance and applicability of language models in real-world settings. By focusing on skill generalization from atomic tasks to complex reasoning tasks, researchers can develop more robust training strategies that improve model capabilities across different domains and datasets. This approach enables language models to tackle a wider range of tasks with greater accuracy and efficiency.
Furthermore, understanding how atomic skills can be enhanced through hierarchical curriculum learning opens up possibilities for refining model training methodologies. By incorporating structured skill training followed by applied learning stages, developers can ensure that language models not only excel at individual tasks but also demonstrate proficiency in applying learned skills to more complex scenarios. This tailored approach enhances both task-specific performance and cross-domain adaptability.
Overall, this research provides valuable insights into optimizing training processes for language models, ultimately leading to improved performance in diverse real-world applications such as natural language understanding, information retrieval, question answering systems, chatbots, and more.

How can automated methodologies be developed to design a complete framework for hierarchical curriculum learning?

Automated methodologies play a crucial role in designing an efficient framework for hierarchical curriculum learning that optimizes skill enhancement and generalization processes for language models. Here are some key steps involved:

Data Generation: Automated tools can generate prerequisite data sets tailored to specific atomic skills such as arithmetic operations or unit conversions. These data sets should cover a wide range of difficulty levels and variations within each skill domain.

Skill Training Automation: Develop algorithms that automate the continuous training process focused on enhancing individual atomic skills based on generated data sets. Implement replay strategies or other techniques to prevent catastrophic forgetting during skill refinement.

Applied Learning Automation: Create mechanisms that automatically construct compositional data sets using responses from prerequisite tasks integrated into complex reasoning scenarios like math word problems or multi-step questions.

4..Evaluation Metrics Automation: Design automated evaluation metrics that assess improvements in both individual atomic skill performance as well as overall task completion accuracy after hierarchical curriculum learning stages.
By automating these key components within the framework while ensuring flexibility and adaptability across different types of tasks and domains will streamline the implementation of hierarchical curriculum learning strategies for enhancing language model capabilities efficiently.