Pre-trained Large Language Models (LLMs) have demonstrated significant potential in the Natural Language to Code (NL2Code) task. However, user-provided natural language descriptions are often ambiguous or misleading, resulting in poor code quality. Additionally, LLMs have limited ability to solve complex programming tasks with multiple requirements or higher difficulty levels . To address this, we developed a collaborative code generation framework integrating intelligent agents with LLMs. This framework optimizes code generation by dividing the NL2Code task into four stages: role definition, demand optimization, code writing, and code review. Based on the task-instruction-prompts and role-definition-prompts dataset we created, we fine-tuned a BART model to develop an intelligent agent enriched with programming knowledge. This agent generates more detailed prompts to guide LLMs at each stage, enhancing code generation capabilities. We evaluated our method on multiple datasets, including HumanEval, MBPP and LLMSecEval, using various metrics such as pass@k, logical error rate, code quality score, vulnerable@k, and secure@k to assess coding ability. To further strengthen our evaluation, we compared our approach against several specialized code-focused LLMs, including CodeGeeX, CodeLlama, and DeepSeek-Coder-V2. Our method improved the pass@1 metric by 18.9% and 23.2%, compared to the GPT-3.5 baseline and reduced the logical error rate from 38.2% and 29.1% to 19.3% and 13.6%. Moreover, the integration of the intelligent agent with GPT-4 led to notable performance improvements , with a 16.9% increase in pass@1, a 8.14% increase in code quality score, and a reduction in the logical error rate from 12.5% to 8.1%. Additionally, the Secure@1 metric for GPT-4 improved from 48.6% to 55.3%, reflecting enhanced code security. These experimental results demonstrate the efficacy of our method in advancing the code generation capabilities of LLMs.

The localization of seizure onset zones from long term electrophysiological data is essential of epilepsy surgery, which demands significant effort and clinical knowledge to analysis long term electrophysiological data. In this study we proposed a Graph Neural Network (GNN)-based technique for automated localization of seizure onset zones in prolonged electrophysiological data. Initially, we established a Graph Convolutional Networks (GCN) model for epileptic stereo electroencephalography (SEEG) data classification. Then we used a GNN Explanation method to extract the magnitude of classification contribution from electrodes in the epilepsy SEEG data. The electrodes with most contribution would be considered as part of the seizure onset zone. The results revealed that the proposed method successfully identifies the primary seizure onset zones and estimates the impact of seizure foci on the other zones. This method applies graph neural networks explanation and offers novel perspectives for understanding the mechanisms of epilepsy.

Abstract A large number of norms, which express constraints on people's behaviour within a specific range, are contained in normative documents. In writing and revising normative documents, conflicts between two norms often arise. The task Norm Conflict Identification (NCI) aims to identify such conflicts. The existing NCI methods based on statistical learning are all pipelines, which cause errors to accumulate, and cannot sufficiently extract helpful information. According to the characteristics of NCI, we propose a neural network model called NeuralConflict. This end‐to‐end model can avoid the accumulation of errors and makes it easier to obtain the optimal global solution. The model sets up an auxiliary task to predict whether two norms are semantically related and shares part of the information with the task NCI. In addition, the model uses a convolutional neural network with differently sized convolution kernels to extract local semantic information from the norms. Finally, the model inputs the shared and local semantic information into a fully connected neural network to predict whether the two norms conflict. We construct a Chinese dataset and use it with an existing English dataset for the experiments. Experimental results show that NeuralConflict achieves optimum results on both datasets.

Abstract Organizing and managing policy documents (PDs) issued to the public in a good way can improve the efficiency of government employees and make it easier for the public to find the needed policy information. However, existing PDs are organized only by dates and manually defined categories; besides, PDs issued by different government branches are isolated from each other. These problems make it challenging and time‐consuming for the public to find the needed policy information. We argue that implicit links should be established between PDs based on their relevance, thus helping the public find the needed policy information efficiently. To this end, we propose an unsupervised relevance scoring method for PDs consist six modules, taking Chinese social security policies as the application case. The method combines the TextRank algorithm, TF‐IDF representation, mutual information and left–right information entropy algorithm, and BERT. The method can decrease the interference of noisy words in PDs to relevance scoring. In addition, the method can consider multiple features of PDs simultaneously so that the measure of relevance can be more comprehensive. The method is not driven by domain‐specific labelled data, hence can be easily generalized to PDs in various domains. We construct a dataset containing 5000 Chinese social security policies and then conduct experiments on it to evaluate our method. Experimental results show that our method is feasible and can bring convenience to government agencies and the public to a certain extent. Furthermore, our method achieves more than a 3% improvement in evaluation results on test tasks than the methods with a similar purpose in the legal AI community.

The following topics are dealt with: learning (artificial intelligence); neural nets; convolutional neural nets; feature extraction; pattern classification; recurrent neural nets; image classification; natural language processing; text analysis; object detection.