A Comprehensive Study of LLM-Based Argument Classification: From LLAMA Through GPT-4o to DeepSeek-R1

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2024-11-14

Introduction
What is Argument Mining?
The Role of Large Language Models in Argument Mining
Review of Benchmarks and Datasets: Args.me and UKP
LLMs Evaluated in the Study
Methodology: Prompting and Reasoning Enhancements
GPT-4o: Top Performer in Standard Argument Classification
DeepSeek-R1: Best-in-Class for Reasoning-Enhanced Tasks
Comparative Results and Analysis
Common Error Types Observed Across LLMs
Chain-of-Thought Prompting: Strengths and Limitations
Dataset Weaknesses and Recommendations for Improvement
Insights from Args.me Dataset
Insights from UKP Dataset
Practical Implications: LegalTech, Education, Policy Debate
Opportunities for Prompt Engineering Improvements
The Future of Argument Mining with LLMs
Limitations of the Study
Conclusion
References and Further Reading

1. Introduction

Argument classification is a crucial task in understanding how people reason, justify their claims, and communicate persuasively. As debates, legal proceedings, and online forums grow increasingly digitized, the need to automatically extract structured arguments has become more pressing. Large Language Models (LLMs) like GPT, LLaMA, and DeepSeek now offer the potential to perform this task at scale — provided their capabilities are understood and fine-tuned properly.

This study evaluates how different LLMs perform in classifying arguments using real-world datasets and various prompt techniques. It represents one of the first in-depth comparative analyses of state-of-the-art LLMs on public argument mining datasets.

2. What is Argument Mining?

Argument Mining (AM) refers to the automatic identification and extraction of argumentative components (like premises, claims) and the relationships between them (such as support or contradiction). AM draws from diverse fields, including:

Linguistics: Language structure and semantics
Logic and Philosophy: The nature of valid reasoning
Computer Science: AI and NLP for implementation
Psychology: Understanding cognitive reasoning patterns
Law and Rhetoric: Real-world argumentative frameworks

Tasks in AM typically include:

Component classification: Distinguishing between claims, premises, and non-argumentative text
Relation classification: Determining support or attack relationships between statements
Stance detection: Identifying the author's attitude or bias

3. The Role of Large Language Models in Argument Mining

Traditionally, AM relied on hand-crafted features, syntactic parsing, and classical machine learning. The introduction of transformer-based architectures — particularly LLMs — revolutionized the field by:

Learning contextualized semantics
Understanding nuances in logical structure
Generalizing across domains (e.g., legal vs. social media arguments)
Integrating with prompt-based reasoning strategies (e.g., Chain-of-Thought)

LLMs can now classify, summarize, or even generate arguments from scratch, making them powerful tools for researchers, educators, and legal analysts.

4. Review of Benchmarks and Datasets: Args.me and UKP

Two major datasets were used in the study:

a. Args.me Dataset

Extracted from debate portals
Includes argument pairs (claim-premise)
Annotated for relevance, stance, and logical relation
Challenges: Often noisy and informal language

b. UKP Dataset

Highly structured academic-style arguments
Categorized into stance, claim, premise, rebuttal
Often used in benchmark papers
Considered “cleaner” but less diverse

Both datasets test models’ ability to perform across different domains and argument styles.

5. LLMs Evaluated in the Study

The study compares several leading models, grouped into standard and reasoning-enhanced versions:

Standard Models

GPT-3.5 / GPT-4o
LLaMA 2
DeepSeek-R1 Base

Reasoning-Enhanced Models

Same models, but with Chain-of-Thought (CoT) or custom prompting frameworks applied

These models differ in parameter scale, training data, architecture, and tokenizer handling — all of which influence argument classification accuracy.

6. Methodology: Prompting and Reasoning Enhancements

Chain-of-Thought Prompting

Rather than asking for direct answers, the prompt encourages the model to “think aloud” and explain intermediate reasoning steps:

Example Prompt:

“Here is a sentence: ‘Healthcare is a human right because it ensures dignity for all.’
Please identify whether this is a claim or a premise. Explain your reasoning step-by-step.”

This method improves interpretability and often boosts accuracy, particularly on harder tasks.

7. GPT-4o: Top Performer in Standard Argument Classification

GPT-4o showed the highest overall performance across standard prompting, thanks to:

Vast pretraining on structured and informal arguments
Strong generalization to diverse domains (debate, legal, Reddit)
High coherence in explanations (even without CoT prompts)

On the Args.me dataset, GPT-4o achieved >90% accuracy in identifying claims and premises. On UKP, it reached >88% in correctly assigning relations (e.g., support/attack).

8. DeepSeek-R1: Best-in-Class for Reasoning-Enhanced Tasks

DeepSeek-R1, when used with Chain-of-Thought prompts, outperformed all other models in reasoning-intensive tasks:

Classifying complex multi-sentence arguments
Identifying subtle logical relations (e.g., analogy, cause-effect)
Explaining classification rationale clearly

This suggests DeepSeek-R1’s RL-based training gives it an edge when reasoning chains are essential to task completion.

9. Comparative Results and Analysis

Model	Args.me Accuracy	UKP Accuracy	CoT Gain (%)
GPT-4o	91.2%	88.1%	+4.5%
DeepSeek-R1 (CoT)	89.5%	89.7%	+7.8%
LLaMA-2-70B	86.4%	84.3%	+3.2%
GPT-3.5	85.1%	82.5%	+3.5%
DeepSeek-R1 (Standard)	83.8%	85.9%	—

10. Common Error Types Observed Across LLMs

Despite high performance, all models exhibited systematic errors:

Over-classification: Labeling non-argumentative text as claims
Confusion between premise and claim: Especially in one-liners
Missing contextual cues: When argument spans multiple sentences
Incorrect stance detection: Due to sarcasm or negation
Prompt misunderstanding: Over-reliance on shallow keywords

These highlight the fragility of prompt-based models in nuanced cases.

11. Chain-of-Thought Prompting: Strengths and Limitations

Strengths:

Improved transparency
Reduced hallucination
More robust in noisy data (e.g., Reddit threads)

Limitations:

Adds latency during inference
Sometimes leads to verbose but incorrect answers
Fails on implicit reasoning unless trained for it

12. Dataset Weaknesses and Recommendations for Improvement

Args.me:

Contains overlapping claims/premises
Lacks clarity in annotation guidelines
Improvement: Filter for linguistic clarity and contradiction types

UKP:

Heavily curated, less domain-diverse
Improvement: Introduce multilingual and real-world debates

Better datasets will lead to less bias and stronger generalization.

13. Insights from Args.me Dataset

GPT-4o handled informal logic and rhetorical questions better
DeepSeek-R1 excelled in cross-topic generalization
CoT prompts significantly helped models filter irrelevant details

14. Insights from UKP Dataset

Tasks like stance relation saw marked improvement with CoT
DeepSeek-R1 demonstrated strongest recall in rebuttal detection
LLaMA models struggled with complex rebuttals or implicit premises

15. Practical Implications: LegalTech, Education, Policy Debate

LLMs capable of reliable argument classification can be used for:

Legal Brief Analysis: Distinguish claims and evidential support
Debate Scoring: Automatically evaluate student arguments
Online Moderation: Filter inflammatory vs. constructive claims
Journalism Tools: Highlight bias or weak premises

16. Opportunities for Prompt Engineering Improvements

This study suggests new prompt strategies:

Multi-turn prompting: “What would a critic say about this claim?”
Dialogic framing: Model a debate between AI personas
Emotion-neutralization: Strip affective language before classification
Prompt-chaining: Classify → explain → verify

17. The Future of Argument Mining with LLMs

Upcoming directions include:

Few-shot + retrieval-augmented argument classification
Multimodal argument mining (text + image + video)
Cross-lingual argument detection
Agentic LLMs that can participate in live debates and justify claims

LLMs like DeepSeek-R1 and GPT-4o may soon transform automated reasoning.

18. Limitations of the Study

Only English datasets were used
Evaluations rely on preexisting annotations (which may have bias)
Cost and latency of GPT-4o/DeepSeek inference not measured
Prompt tuning was manual, not learned

19. Conclusion

This comprehensive study demonstrates that LLMs are effective tools for argument classification, but their performance varies based on model architecture, prompt design, and task complexity.

GPT-4o leads in out-of-the-box classification.
DeepSeek-R1, with reasoning enhancements, dominates in complex tasks.
Prompt engineering remains a key lever for further improvement.

By identifying strengths, weaknesses, and opportunities, this paper lays the groundwork for better integration of LLMs in education, legal analysis, journalism, and AI agents for critical thinking.