A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2
arXiv:2603.19253v1 Announce Type: cross Abstract: Argument mining (AM) is an interdisciplinary research field focused on the automatic identification and classification of argumentative components, such as claims and premises, and the relationships between them. Recent advances in large language models (LLMs) have significantly improved the performance of argument classification compared to traditional machine learning approaches. This study presents a comprehensive evaluation of several state-of-the-art LLMs, including GPT-5.2, Llama 4, and DeepSeek, on large publicly available argument classification corpora such as Args.me and UKP. The evaluation incorporates advanced prompting strategies, including Chain-of- Thought prompting, prompt rephrasing, voting, and certainty-based classification. Both quantitative performance metrics and qualitative error analysis are conducted to assess model behavior. The best-performing model in the study (GPT-5.2) achieves a classification accuracy of
arXiv:2603.19253v1 Announce Type: cross Abstract: Argument mining (AM) is an interdisciplinary research field focused on the automatic identification and classification of argumentative components, such as claims and premises, and the relationships between them. Recent advances in large language models (LLMs) have significantly improved the performance of argument classification compared to traditional machine learning approaches. This study presents a comprehensive evaluation of several state-of-the-art LLMs, including GPT-5.2, Llama 4, and DeepSeek, on large publicly available argument classification corpora such as Args.me and UKP. The evaluation incorporates advanced prompting strategies, including Chain-of- Thought prompting, prompt rephrasing, voting, and certainty-based classification. Both quantitative performance metrics and qualitative error analysis are conducted to assess model behavior. The best-performing model in the study (GPT-5.2) achieves a classification accuracy of 78.0% (UKP) and 91.9% (Args.me). The use of prompt rephrasing, multi-prompt voting, and certainty estimation further improves classification performance and robustness. These techniques increase the accuracy and F1 metric of the models by typically a few percentage points (from 2% to 8%). However, qualitative analysis reveals systematic failure modes shared across models, including instabilities with respect to prompt formulation, difficulties in detecting implicit criticism, interpreting complex argument structures, and aligning arguments with specific claims. This work contributes the first comprehensive evaluation that combines quantitative benchmarking and qualitative error analysis on multiple argument mining datasets using advanced LLM prompting strategies.
Executive Summary
This article presents a comprehensive study on the application of large language models (LLMs) in argument classification, evaluating the performance of GPT-5.2, Llama 4, and DeepSeek on publicly available argument classification corpora. The study employs advanced prompting strategies to improve model performance and robustness, achieving high classification accuracy rates of up to 91.9%. However, qualitative analysis reveals systematic failure modes across models, including difficulties in detecting implicit criticism and complex argument structures. This work contributes to the argument mining field by providing the first comprehensive evaluation combining quantitative benchmarking and qualitative error analysis.
Key Points
- ▸ The study evaluates the performance of GPT-5.2, Llama 4, and DeepSeek on argument classification corpora using advanced prompting strategies.
- ▸ The use of prompt rephrasing, multi-prompt voting, and certainty estimation improves model performance and robustness.
- ▸ Qualitative analysis reveals systematic failure modes across models, including difficulties in detecting implicit criticism and complex argument structures.
Merits
Comprehensive evaluation framework
The study provides a comprehensive evaluation framework that combines quantitative benchmarking and qualitative error analysis, offering a more complete understanding of LLM performance in argument classification.
Demerits
Limited generalizability
The study's findings may not be generalizable to other argument classification tasks or domains, limiting the applicability of the results.
Complexity of argument structures
The study highlights the challenges posed by complex argument structures, which may require additional research to develop more effective LLM-based solutions.
Expert Commentary
The study makes a significant contribution to the argument mining field by providing a comprehensive evaluation framework that combines quantitative benchmarking and qualitative error analysis. The findings demonstrate the effectiveness of advanced LLM prompting strategies in improving model performance and robustness. However, the study also highlights the challenges posed by complex argument structures, which may require additional research to develop more effective LLM-based solutions. The implications of the study are far-reaching, with potential applications in areas such as legal analysis and decision support, as well as policy-making and decision-making processes.
Recommendations
- ✓ Further research is needed to develop more effective LLM-based solutions for complex argument structures.
- ✓ The development of more comprehensive evaluation frameworks that combine quantitative benchmarking and qualitative error analysis can inform the development of more effective LLM-based argument classification systems.
Sources
Original: arXiv - cs.AI
