Dynamic Skill Adaptation for Large Language Models (2412.19361v1)

Abstract: We present Dynamic Skill Adaptation (DSA), an adaptive and dynamic framework to adapt novel and complex skills to LLMs. Compared with previous work which learns from human-curated and static data in random orders, we propose to first automatically generate and organize the training data by mimicking the learning pathways of human and then dynamically tailor the training data based on the training dynamics. Specifically, inspired by the learning structures and teaching strategies in the human education system, we first construct a skill graph by decomposing complex skills into sub-skills and arranging them based on their dependencies in human syllables. For every skill, we utilize LLMs to generate both textbook-like data which contains detailed descriptions of skills for pre-training and exercise-like data which targets at explicitly utilizing the skills to solve problems for instruction-tuning. Furthermore, during the instruction-tuning, we dynamically update the training data which down-weight easy-to-learn examples, generate more complex examples, and filter out data with errors. Experiments on LLMs such as LLAMA and Mistral demonstrate the effectiveness of our proposed methods in adapting math reasoning skills and social study skills.

• The paper presents a three-phase framework that mimics human learning to enhance specialized skills in LLMs.
• It employs dynamic training that continuously updates learning data, yielding a 304% improvement in calculus tasks on Mistral-7b.
• The methodology decomposes complex tasks into structured skill graphs, enabling adaptive fine-tuning across diverse domains.

Dynamic Skill Adaptation for LLMs

The paper "Dynamic Skill Adaptation for LLMs" by Jiaao Chen and Diyi Yang presents a structured approach to enhancing the abilities of LLMs by integrating complex and novel skills through a dynamic and adaptive framework. The core innovation of this work lies in its methodology, which mimics human educational strategies to systematically train LLMs in specialized domains, thus addressing limitations in domain-specific expertise such as in mathematical reasoning or social studies.

Overview of the Methodology

The authors introduce a three-phase framework, Dynamic Skill Adaptation (DSA), designed to aid LLMs in acquiring specialized skills through structured and organized learning paths:

1. Skill Graph Construction: A disparate set of complex skills, such as those used in calculus or social studies, is decomposed into fundamental sub-skills. These are further organized into a skill graph that represents the dependencies among them. This graph mirrors human syllabi, providing a robust blueprint for the sequential learning of pre-requisite and advanced knowledge.
2. Training Data Generation: Inspired by educational techniques, two types of data are generated—textbook-like descriptions for pre-training and exercise-like problems for instruction-tuning. The textbook data offer detailed representations of skills, while exercises compel the model to apply these skills to problem-solving, paralleling the rehearsal and elaboration stages in human learning.
3. Dynamic Training: To remedy overfitting and improve efficacy of learning, a dynamic training protocol is implemented. This involves categorizing training samples based on learnability, difficulty, and error potential. Training data are continuously updated by introducing more complex examples and revising categories based on the model's learning progress, ensuring an adaptive correction process throughout training.

Empirical Validation and Results

The experimentation involved testing on LLMs such as LLAMA and Mistral across complex domains including calculus and social studies. The paper reports significant improvements—Mistral-7b trained with DSA displayed a 304% improvement in calculus-related tasks over baseline models and a 10.7% enhancement over other specialized models like DeepSeekMATH.

Specifically, the framework revealed its strength in the context of scarce domain-specific training data, demonstrating marked performance improvements both in specialized tasks (e.g., Pre-Calculus) and unforeseen tasks, indicating generalization capability fostered by DSA's human-mimicking training strategy.

Implications of the Research

This work substantiates the thesis that learning frameworks inspired by human educational mechanisms can contribute significantly to the skill adaptation of LLMs. The structured decomposition of complex tasks into manageable skill sets ensures progressive and thorough understanding, addressing limitations such as data sparsity and overfitting in specialized contexts. The dynamic updating of training data seamlessly corrects the learning trajectory and aligns with teaching strategies that interpret student comprehension levels to adjust coursework accordingly.

Future Directions

The advances proposed by this research could likely extend to other specialized domains beyond mathematics and social studies, enhancing the applicability of LLMs in various scientific and technical arenas requiring nuanced domain knowledge. Future work could explore integrating this framework with multidisciplinary skill graphs to facilitate cross-domain competence and evaluate its impact on broader AI objectives like multimodal learning and lifelong learning paradigms.

In essence, this paper provides valuable insights into the development of more adaptive, specialized AI systems, expanding the scope of LLM applications through methods that are deeply rooted in human-like learning strategies.