Large language models(LLMS) have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes PatternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.

News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability.

Precise classification of unsound wheat grains is essential for crop yields and food security, yet most existing approaches rely on vision-only models that demand large labeled datasets, which is often impractical in real-world, data-scarce settings. To address this few-shot challenge, we propose UWGC, a novel vision-language framework designed for few-shot classification of unsound wheat grains. UWGC integrates two core modules: a fine-tuning module based on Adaptive Prior Refinement (APE) and a text prompt enhancement module that incorporates Advancing Textual Prompt (ATPrompt) and the multimodal model Qwen2.5-VL. The synergy between the two modules, leveraging cross-modal semantics, enhances generalization of UWGC in low-data regimes. It is offered in two variants: UWGC-F and UWGC-T, in order to accommodate different practical needs. Across few-shot settings on a public grain dataset, UWGC-F and UWGC-T consistently outperform existing vision-only and vision-language methods, highlighting their potential for unsound wheat grain classification in real-world agriculture.

Large language models(LLMS)have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes PatternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.

Outbreaks of stored grain pests can pose significant threats to food security. In-depth analyses of sudden outbreaks are key to achieving effective prevention and control. To address the issue of models’ insufficient reasoning capability arising from complex causal relationships in stored grain pest events, this study proposes an Event Patterns Enhancing Causal Reasoning (EPECR) method incorporating category theory. Specifically, we focus on common pests such as Sitophilus zeamais (maize weevil) and Sitotroga cerealella (Angoumois grain moth). We formally map the domain ontology—including entities like environmental factors (e.g., temperature, humidity) and control measures (e.g., fumigation)—to categories, and represent their inter-relationships (e.g., inhibition, promotion) as functors. To handle complex scenarios, we model multi-cause events (e.g., high temperature and humidity jointly accelerating pest reproduction) using functor products, and represent multi-hop events (e.g., environmental changes leading to pest outbreak and subsequent grain loss) through functor compositions. This formal expression enables Large Language Models (LLMs) to extract reliable event patterns. Based on these patterns, this study constructed 1440 structured datasets and adopted the Low-Rank Adaptation (LoRA) strategy to fine-tune the LLMs. Experiments on the domain-specific Stored Grain Pest Events Dataset (SGPE) demonstrate that EPECR achieves a reasoning accuracy of 85.9% on in-distribution data and 79.9% on out-of-distribution data, effectively identifying correct causal chains for pest logic. This method significantly outperforms the state-of-the-art domain method-Naive Augmentations (NA)-by 4.9%, providing precise decision support for the early warning and control of specific pest incidents.

Domain-specific Named Entity Recognition (NER), whose goal is to recognize domain-specific entities and their categories, provides an important support for constructing domain knowledge graphs. Currently, deep learning-based methods are widely used and effective in NER tasks, but due to the reliance on large-scale labeled data. As a result, the scarcity of labeled data in a specific domain will limit its application.Therefore, many researches started to introduce few-shot methods and achieved some results. However, the entity structures in specific domains are often complex, and the current few-shot methods are difficult to adapt to NER tasks with complex features.Taking the Chinese coal chemical industry domain as an example,there exists a complex structure of multiple entities sharing a single entity, as well as multiple relationships for the same pair of entities, which affects the NER task under the sample less condition.In this paper, we propose a Large Language Models (LLMs)-based entity recognition framework LLM-DER for the domain-specific entity recognition problem in Chinese, which enriches the entity information by generating a list of relationships containing entity types through LLMs, and designing a plausibility and consistency evaluation method to remove misrecognized entities, which can effectively solve the complex structural entity recognition problem in a specific domain.The experimental results of this paper on the Resume dataset and the self-constructed coal chemical dataset Coal show that LLM-DER performs outstandingly in domain-specific entity recognition, not only outperforming the existing GPT-3.5-turbo baseline, but also exceeding the fully-supervised baseline, verifying its effectiveness in entity recognition.