Argument mining has garnered increasing attention over the years, with the recent advancement of Large Language Models (LLMs) further propelling this trend. However, current argument relations remain relatively simplistic and foundational, struggling to capture the full scope of argument information. To address this limitation, we propose a systematic framework comprising 14 fine-grained relation types from the perspectives of vertical argument relations and horizontal discourse relations, thereby capturing the intricate interplay between argument components for a thorough understanding of argument structure. On this basis, we conducted extensive experiments on three tasks: argument component prediction, relation prediction, and automated essay grading. Additionally, we explored the impact of writing quality on argument component prediction and relation prediction, as well as the connections between discourse relations and argumentative features. The findings highlight the importance of fine-grained argumentative annotations for argumentative writing assessment and encourage multi-dimensional argument analysis.

With the scale of Large Language Models(LLMs) and the size of the training data continuing to expand, the computational costs required for training or tuning have significantly increased as well. In this work we propose an efficient and effective Large-Scale Data Compression (LSDC) method to substantially reduce the size of training data and thus enhance the training efficiency without compromising the performance of LLMs through a bifurcated quantization strategy. Specifically, our method first segments the dataset into multiple clusters, significantly reducing the time and memory requirements for data compression. Then, during the second phase of coreset selection, the diversity of samples is ensured by maximizing the submodular gain in order to avoid performance degradation. The comparative experiments showed that the performance of LLMs fine-tuned on a 20% compressed subset of the Alpaca dataset using LSDC outperformed those on the full dataset. Moreover,on a domain-specific instruction dataset of millions of samples, the LLMs fine-tuned on a 10% compressed dataset using LSDC outperformed those on the entire dataset, which dramatically enhances the domain-adaption capabilities of LLMs. This provides a promising potential of LSDC in training bigger LLMs from scratch and supervised fine-tuning as well.

Topic relevance of an essay demands that the composition adheres to a clear theme and aligns well with the essay prompt requirements, a critical aspect of essay quality evaluation. However, existing research of Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback, while Automatic Essay Comment Generation (AECG) faces much complexity and difficulty. Additionally, current Large Language Models, including GPT-4, often make incorrect judgments and provide overly impractical feedback when evaluating topic relevance. This paper introduces TOREE (Topic Relevance Evaluation), a comprehensive dataset developed to assess topic relevance in Chinese primary and middle school students’ essays, which is beneficial for AES, AECG and other applications. Moreover, our proposed two-step method utilizes TOREE through a combination of Supervised Fine-tuning and Preference Learning. Experimental results demonstrate that TOREE is of high quality, and our method significantly enhances models’ performance on two designed tasks for topic relevance evaluation, improving both automatic and human evaluations across four diverse LLMs.

This paper introduces the Chinese Essay Argument Mining Corpus (CEAMC), a manually annotated dataset designed for argument component classification on multiple levels of granularity. Existing argument component types in education remain simplistic and isolated, failing to encapsulate the complete argument information. Originating from authentic examination settings, CEAMC categorizes argument components into 4 coarse-grained and 10 fine-grained delineations, surpassing previous simple representations to capture the subtle nuances of argumentation in the real world, thus meeting the needs of complex and diverse argumentative scenarios. Our contributions include the development of CEAMC, the establishment of baselines for further research, and a thorough exploration of the performance of Large Language Models (LLMs) on CEAMC. The results indicate that our CEAMC can serve as a challenging benchmark for the development of argument analysis in education.