Xin Chen

Yaguang Li, Xin Chen

Generative models reliant on sequential autoregression have been at the forefront of language generation for an extensive period, particularly following the introduction of widely acclaimed transformers. Despite its excellent performance, there are always some issues that we face today. For example, problems such as hallucinations and getting trapped in a logic loop may occur. To enhance the performance of existing systems, this paper introduces a new method for generating sequences in natural language, which involves generating the targeted sentence in a tree-traversing order. The paper includes an illustration of the theoretical basis and validity of the approach, as well as a comparison of its fundamentals with the diffusion model in graphic generation. Finally, a module called SenTree is introduced for generating an approximating binary tree. It is already available at https://github.com/arklyg/sentree. Additionally, a joint training framework based on this approach is proposed, incorporating the intrinsics of generative adversarial networks.

Faren Yan, Peng Yu, Xin Chen

The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals. However, for certain NLP tasks, such as NER, the performance of LLMs still falls short when compared to supervised learning methods. In our research, we developed a NER processing framework called LTNER that incorporates a revolutionary Contextualized Entity Marking Gen Method. By leveraging the cost-effective GPT-3.5 coupled with context learning that does not require additional training, we significantly improved the accuracy of LLMs in handling NER tasks. The F1 score on the CoNLL03 dataset increased from the initial 85.9% to 91.9%, approaching the performance of supervised fine-tuning. This outcome has led to a deeper understanding of the potential of LLMs.