Knowledge Graph-enhanced Large Language Models (KG-Enhanced LLMs) integrate the linguistic capabilities of LLMs with the structured semantics of Knowledge Graphs (KGs), showing strong potential in knowledge-intensive reasoning tasks. However, existing methods typically adopt query-driven iterative reasoning from a local perspective, which limits their ability to capture semantically distant but crucial information, leading to dual bottlenecks in efficiency and accuracy for complex multi-hop tasks. To address this issue, we propose MIAoG, a multi-view instructed adaptive reasoning of LLM on KG, which is designed to overcome the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. Instead of query-anchored exploration, MIAoG first prompts the LLM to generate a multi-view instruction set that outlines diverse potential reasoning paths and explicitly specifies global reasoning intentions to guide the model toward coherent and targeted reasoning. During reasoning, MIAoG integrates a real-time introspection mechanism that evaluates the alignment between the current path and the instructions, adaptively pruning inconsistent trajectories to enhance global consistency while maintaining efficiency. Extensive experiments on multiple public datasets show that MIAoG achieves state-of-the-art performance in KG-enhanced LLM reasoning, particularly excelling in complex multi-hop scenarios.

Prompt tuning for Large Language Models (LLMs) is vulnerable to backdoor attacks. Existing methods find backdoor attacks to be a significant threat in data-rich scenarios. However, in data-limited scenarios, these methods have difficulty capturing precise backdoor patterns, leading to weakened backdoor attack capabilities and significant side effects for the LLMs, which limits their practical relevance. To explore this problem, we propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios, called BCU. Specifically, BCU introduces a multi-objective machine unlearning method to capture precise backdoor patterns by forgetting the association between non-trigger data and the backdoor patterns, reducing side effects. Moreover, we design a contrastive learning strategy to enhance the association between triggers and backdoor patterns, improving the capability of backdoor attacks. Experimental results on 6 NLP datasets and 4 LLMs show that BCU exhibits strong backdoor attack capabilities and slight side effects, whether the training data is rich or limited. Our findings highlight practical security risks of backdoor attacks against LLMs, necessitating further research for security purposes. Our code is available at https://github.com/AHU-YangSJ/BCU.

Prompt transfer is a transfer learning method based on prompt tuning, which enhances the parameter performance of prompts in target tasks by transferring source prompt embeddings. Among existing methods, weighted aggregation is effective and possesses the advantages of being lightweight and modular. However, these methods may transfer redundant or irrelevant information from the source prompts to the target prompt, leading to negative impacts. To alleviate this problem, we propose Prompt Contrastive Transformation (PCT), which achieves efficient prompt transfer through prompt contrastive transformation and attentional fusion. PCT transforms the source prompt into task-agnostic embedding and task-specific embeddings through singular value decomposition and contrastive learning, reducing information redundancy among source prompts. The attention module in PCT selects more effective task-specific embeddings and fuses them with task-agnostic embedding into the target prompt. Experimental results show that, despite tuning only 0.035% of task-specific parameters, PCT achieves improvements in prompt transfer for single target task adaptation across various NLP tasks.