In this paper, we aim to improve the reasoning ability of large language models(LLMs) over knowledge graphs(KGs) to answer complex questions. Inspired by existing methods that design the interaction strategy between LLMs and KG, we propose an autonomous LLM-based agent framework, called KG-Agent, which enables a small LLM to actively make decisions until finishing the reasoning process over KGs. In KG-Agent, we integrate the LLM, multifunctional toolbox, KG-based executor, and knowledge memory, and develop an iteration mechanism that autonomously selects the tool and then updates the memory for reasoning over KG. To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG and synthesize a code-based instruction dataset to fine-tune the base LLM. Extensive experiments demonstrate that only using 10K samples for tuning LLaMA2-7B can outperform competitive methods using larger LLMs or more data, on both in-domain and out-domain datasets. Our code and data will be publicly released.

The unlearning method aims at effectively removing harmful, sensitive, or outdated knowledge without costly retraining the model. However, existing methods suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting, where aggressive unlearning degrades the model’s general capabilities. To address these challenges, we propose DirectiOn Guide unlEarning (DOGE), a novel method that enables precise knowledge erasure by identifying and leveraging a targeted “unlearning direction” in the model’s parameter space. DOGE first extracts this direction through differential analysis of representations for forgotten and retained samples, pinpointing the exact subspace associated with unwanted knowledge. It then selectively applies updates along this direction, ensuring minimal interference with retained information and general model performance. Experiments across multiple benchmarks demonstrate that Doge achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities.

Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer reasoning steps. In this paper, we propose RAG-Star, a novel RAG approach that integrates the retrieved information to guide the tree-based deliberative reasoning process that relies on the inherent knowledge of LLMs. By leveraging Monte Carlo Tree Search, RAG-Star iteratively plans intermediate sub-queries and answers for reasoning based on the LLM itself. To consolidate internal and external knowledge, we propose a retrieval-augmented verification that utilizes query- and answer-aware reward modeling to provide feedback for the inherent reasoning of LLMs. Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods. Our codes and data are publicly available at https://github.com/RUCAIBox/RAG-Star.