Using Web Mining and Content Analysis to find and understand clues from the massive amount of unstructured text online is very important for predicting future events and providing early risk warnings in important fields like finance and public safety. While Large Language Models (LLMs) exhibit potential in processing and understanding text, current text-based event prediction faces two primary challenges: first, an insufficient utilization of potential information within the text, such as causal relationships and latent associations, and second, limited predictive reliability constrained by issues like the LLM's own ability and hallucinations. To address these challenges, we propose a novel event prediction framework, Bidirectional Reasoning with Self-Correction (BRSC). BRSC comprises two complementary reasoning dimensions: temporal deductive reasoning, which analyzes the trajectory of historical events along the timeline to enable accurate trend extrapolation, and synchronic associative reasoning, which deeply mines details and latent connections from documents within a specific time window to extended semantic information. In addition, we use a self-correction mechanism that identifies and rectifies potential hallucinations and errors during the reasoning process. Extensive experiments on international relations event prediction demonstrate that BRSC achieves significant improvements over several leading LLM-based methods.

Large language models (LLMs) demonstrate remarkable capabilities in understanding complex tasks and have achieved commendable performance in graph-related tasks, such as node classification, link prediction, and subgraph classification.These tasks primarily depend on the local reasoning capabilities of the graph structure.However, research has yet to address the graph partitioning task that requires global perception abilities.Our preliminary findings reveal that vanilla LLMs can only handle graph partitioning on extremely small-scale graphs.To overcome this limitation, we propose a three-phase pipeline to empower LLMs for large-scale graph partitioning: coarsening, reasoning, and refining.The coarsening phase reduces graph complexity.The reasoning phase captures both global and local patterns to generate a coarse partition.The refining phase ensures topological consistency by projecting the coarse-grained partitioning results back to the original graph structure.Extensive experiments demonstrate that our framework enables LLMs to perform graph partitioning across varying graph scales, validating both the effectiveness of LLMs for partitioning tasks and the practical utility of our proposed methodology.

Aiming at problems such as incomplete, inconsistent, and inaccurate requirements that often occur in the process of obtaining high-end equipment functional requirements, this paper presents a requirement quality improvement method. Referring to the agile development theory of requirements engineering, the quality improvement method constructs a functional requirement model based on user stories, defines the concept of functional requirement quality, designs functional requirement quality evaluation criteria, and constructs a functional requirement quality evaluation process. A case study and sensitivity analysis of new energy vehicle requirement development are conducted to confirm the feasibility and effectiveness of the method, and the experimental results show the superiority of the proposed method in improving the quality of high-end equipment functional requirements.