Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM

This study aims to develop an efficient and accurate model for detecting malicious comments, addressing the increasingly severe issue of false and harmful content on social media platforms. We propose a deep learning model that combines BERT and BiLSTM. The BERT model, through pre-training, captures deep semantic features of text, while the BiLSTM network excels at processing sequential data and can further model the contextual dependencies of text. Experimental results on the Jigsaw Unintended Bias in Toxicity Classification dataset demonstrate that the BERT+BiLSTM model achieves superior performance in malicious comment detection tasks, with a precision of 0.94, recall of 0.93, and accuracy of 0.94. This surpasses other models, including standalone BERT, TextCNN, TextRNN, and traditional machine learning algorithms using TF-IDF features. These results confirm the superiority of the BERT+BiLSTM model in handling imbalanced data and capturing deep semantic features of malicious comments, providing an effective technical means for social media content moderation and online environment purification.

A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization

This paper proposes a medical text summarization method based on LongFormer, aimed at addressing the challenges faced by existing models when processing long medical texts. Traditional summarization methods are often limited by short-term memory, leading to information loss or reduced summary quality in long texts. LongFormer, by introducing long-range self-attention, effectively captures long-range dependencies in the text, retaining more key information and improving the accuracy and information retention of summaries. Experimental results show that the LongFormer-based model outperforms traditional models, such as RNN, T5, and BERT in automatic evaluation metrics like ROUGE. It also receives high scores in expert evaluations, particularly excelling in information retention and grammatical accuracy. However, there is still room for improvement in terms of conciseness and readability. Some experts noted that the generated summaries contain redundant information, which affects conciseness. Future research will focus on further optimizing the model structure to enhance conciseness and fluency, achieving more efficient medical text summarization. As medical data continues to grow, automated summarization technology will play an increasingly important role in fields such as medical research, clinical decision support, and knowledge management.

A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks

With the development of deep learning technology, large language models have achieved remarkable results in many natural language processing tasks. However, these models still have certain limitations in handling complex reasoning tasks and understanding rich background knowledge. To solve this problem, this study proposed a T5 model fine-tuning method based on knowledge graphs, which enhances the model's reasoning ability and context understanding ability by introducing external knowledge graphs. We used the SQuAD1.1 dataset for experiments. The experimental results show that the T5 model based on knowledge graphs is significantly better than other baseline models in reasoning accuracy, context understanding, and the ability to handle complex problems. At the same time, we also explored the impact of knowledge graphs of different scales on model performance and found that as the scale of the knowledge graph increases, the performance of the model gradually improves. Especially when dealing with complex problems, the introduction of knowledge graphs greatly improves the reasoning ability of the T5 model. Ablation experiments further verify the importance of entity and relationship embedding in the model and prove that a complete knowledge graph is crucial to improving the various capabilities of the T5 model. In summary, this study provides an effective method to enhance the reasoning and understanding capabilities of large language models and provides new directions for future research.

A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models

Recent progress in large language models has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse that ``it's all been solved.'' Not surprisingly, this has in turn made many NLP researchers -- especially those at the beginning of their career -- wonder about what NLP research area they should focus on. This document is a compilation of NLP research directions that are rich for exploration, reflecting the views of a diverse group of PhD students in an academic research lab. While we identify many research areas, many others exist; we do not cover those areas that are currently addressed by LLMs but where LLMs lag behind in performance, or those focused on LLM development. We welcome suggestions for other research directions to include: https://bit.ly/nlp-era-llm