Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues. Existing paradigm of KG-augmented LLM manually predefines the breadth of exploration space and requires flawless navigation in KGs. However, this paradigm cannot adaptively explore reasoning paths in KGs based on the question semantics and self-correct erroneous reasoning paths, resulting in a bottleneck in efficiency and effect. To address these limitations, we propose a novel self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG), which first decomposes the question into several sub-objectives and then repeats the process of adaptively exploring reasoning paths, updating memory, and reflecting on the need to self-correct erroneous reasoning paths until arriving at the answer. Specifically, three important mechanisms of Guidance, Memory, and Reflection are designed to work together, to guarantee the adaptive breadth of self-correcting planning for graph reasoning. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of PoG.

Empirical Risk Minimization based methods are based on the consistency hypothesis that all data samples are generated i.i.d. However, this hypothesis cannot hold in many real-world applications. Consequently, simply minimizing training loss can lead the model into recklessly absorbing all statistical correlations in the training dataset. It is why a well-trained model may perform unstably in different testing environments. Hence, learning a stable predictor that can simultaneously performs well in all testing environments is important for machine learning tasks. In this work, we study this problem from the perspective of Invariant Risk Minimization. Specifically, we propose a novel Self-supervised Invariant Risk Minimization method based on the fact that the real causality connections between features are consistent no matter how the environment changes. First, we propose a self-supervised invariant representation learning objective function, which aims to learn a stable representation of the consistent causality. Based on that, we further propose a stable predictor training algorithm. This algorithm aims to improve the predictor's stability using the invariant representation learned by using our proposed objective function. We conduct extensive experiments on both synthetic and real-world datasets to show that our proposal outperforms previous state-of-the-art stable learning methods. The code will be released later.

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little attention to modeling the backward network of MI (i.e., dependency from the target to the source), which is crucial to the tightness of the variational information maximization lower bound. In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target. Within this framework, the forward and backward networks are able to iteratively promote or demote each other's generated instances by comparing the real and synthetic data distributions. We also develop a latent noise sampling strategy that leverages random variations at the high-level semantic space to enhance the long term dependency in the generation process. Extensive experiments based on different text generation tasks demonstrate that the proposed AMI framework can significantly outperform several strong baselines, and we also show that AMI has potential to lead to a tighter lower bound of maximum mutual information for the variational information maximization problem.