中文版 | English
题名

Meta-Learning with Complex Tasks

姓名
姓名拼音
JIANG Weisen
学号
12051017
学位类型
博士
学位专业
计算机
导师
张宇
导师单位
计算机科学与工程系
外机构导师
James T. Kwok
外机构导师单位
香港科技大学
论文答辩日期
2024-07-12
论文提交日期
2024-07-12
学位授予单位
香港科技大学
学位授予地点
香港
摘要

Meta-Learning aims at extracting shared knowledge (meta-knowledge) from historical tasks to accelerate learning on new tasks. It has achieved promising performance in various applications and many meta-learning algorithms have been developed to learn a meta-model that contains meta-knowledge (e.g., meta-initialization/meta- regularization) for task-specific learning procedures. In this thesis, we focus on meta- learning with complex tasks, thus, task-specific knowledge is diverse and various meta- knowledge is required.

First, we extend learning an efficient meta-regularization for linear models to nonlinear models by kernelized proximal regularization, allowing more powerful models like deep networks to deal with complex tasks. Second, we formulate the task-specific model parameters into a subspace mixture and propose a model-agnostic meta-learning algorithm to learn the subspace bases. Each subspace represents one type of meta- knowledge and structured meta-knowledge accelerates learning complex tasks more effectively than a simple meta-model. Third, we propose an effective and parameter-efficient meta-learning algorithm for natural language processing tasks. The proposed algorithm learns a pool of multiple meta-prompts to extract meta-knowledge from meta-training tasks and then constructs instance-dependent prompts as weighted combinations of all the meta-prompts by attention. Instance-dependent prompts are flexible and powerful for prompting complex tasks. The meta-prompts are meta-parameters while the language model is frozen, thus very parameter-efficient.

Next, we study the problem of verifying candidate answers using the meta-knowledge of backward reasoning by CoT prompting. We focus on mathematical reasoning problems, which are complex, and we propose combining the meta-knowledge of forward and backward reasoning together for verification. Lastly, we propose a novel question bootstrapping method to enhance the LLMs’ mathematical reasoning meta-knowledge. The original questions are augmented in two directions: in the forward direction, we rephrase the questions by few-shot prompting; in the backward direction, we mask a number in the question and create a backward question to predict the masked number when the answer is provided. LLMs finetuned on augmented data achieve excellent mathematical problem-solving ability.

关键词
语种
英语
培养类别
联合培养
入学年份
2020
学位授予年份
2024-07
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