Haitao Mao

Haitao Mao, Guangliang Liu, Yao Ma et al.

In-Context Learning (ICL) empowers Large Language Models (LLMs) with the ability to learn from a few examples provided in the prompt, enabling downstream generalization without the requirement for gradient updates. Despite encouragingly empirical success, the underlying mechanism of ICL remains unclear. Existing research remains ambiguous with various viewpoints, utilizing intuition-driven and ad-hoc technical solutions to interpret ICL. In this paper, we leverage a data generation perspective to reinterpret recent efforts from a systematic angle, demonstrating the potential broader usage of these popular technical solutions. For a conceptual definition, we rigorously adopt the terms of skill recognition and skill learning. Skill recognition selects one learned data generation function previously seen during pre-training while skill learning can learn new data generation functions from in-context data. Furthermore, we provide insights into the strengths and weaknesses of both abilities, emphasizing their commonalities through the perspective of data generation. This analysis suggests potential directions for future research.

Kaiwen Dong, Haitao Mao, Zhichun Guo et al.

Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph. Traditional approaches leverage heuristic methods based on widely observed connectivity patterns, offering broad applicability and generalizability without the need for model training. Despite their utility, these methods are limited by their reliance on human-derived heuristics and lack the adaptability of data-driven approaches. Conversely, parametric link predictors excel in automatically learning the connectivity patterns from data and achieving state-of-the-art but fail short to directly transfer across different graphs. Instead, it requires the cost of extensive training and hyperparameter optimization to adapt to the target graph. In this work, we introduce the Universal Link Predictor (UniLP), a novel model that combines the generalizability of heuristic approaches with the pattern learning capabilities of parametric models. UniLP is designed to autonomously identify connectivity patterns across diverse graphs, ready for immediate application to any unseen graph dataset without targeted training. We address the challenge of conflicting connectivity patterns-arising from the unique distributions of different graphs-through the implementation of In-context Learning (ICL). This approach allows UniLP to dynamically adjust to various target graphs based on contextual demonstrations, thereby avoiding negative transfer. Through rigorous experimentation, we demonstrate UniLP's effectiveness in adapting to new, unseen graphs at test time, showcasing its ability to perform comparably or even outperform parametric models that have been finetuned for specific datasets. Our findings highlight UniLP's potential to set a new standard in link prediction, combining the strengths of heuristic and parametric methods in a single, versatile framework.