Efficient Argument Classification with Compact Language Models and ChatGPT-4 Refinements

Centrala begrepp

Deep learning models like BERT+ChatGPT-4 enhance argument classification efficiency.

Sammanfattning

This paper explores the efficiency of deep learning models in argument mining, focusing on argument classification. It introduces an ensemble model based on BERT architecture and ChatGPT-4 for improved results. Comparative studies were conducted on various datasets, showcasing the superiority of BERT+ChatGPT-4 over other Transformer-based and LSTM-based models. The research aims to provide insights into enhancing argument classification models and developing prompt-based algorithms to reduce errors.
Structure:

Introduction to Argument Mining
Decomposition of Argument Mining Tasks
Importance of Transformer-Based Models in NLP Research
Methodology: Fine-Tuning Language Models and ChatGPT-4 Refinement Algorithm
Results and Discussion: Evaluation on Various Datasets (US2016, UKP)
Conclusions and Future Works

Statistik

The observed improvement is, in most cases, greater than 10%
US2016 dataset has approximately 12.5 thousand arguments.
The Args.me dataset consists of 48,798 arguments from debate portals.

Citat

"I came to this conclusion because the thesis statement argues that Tunisia should not rely on tourism for economic growth." - ChatGPT-4 error analysis
"The responses provided by ChatGPT-4 were predominantly accurate, enhancing the performance quality of the BERT model." - Positive impact of ChatGPT-4
"Future work will focus on large language models like LLAMA-2." - Future research direction mentioned in conclusions

Viktiga insikter från

Efficient argument classification with compact language models and ChatGPT-4 refinements

by Marc... på arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15473.pdf

Djupare frågor

How can large language models like LLAMA-2 further enhance argument classification beyond ChatGPT-4?

Large language models like LLAMA-2 can further enhance argument classification by leveraging their extensive training data and complex architectures. LLAMA-2, with its advanced capabilities, can offer more nuanced understanding of arguments by delving deeper into contextual relationships and subtle nuances within the text. Additionally, LLAMA-2's ability to process a vast amount of information allows for a more comprehensive analysis of arguments across different domains and datasets. Furthermore, LLAMA-2's sophisticated algorithms and attention mechanisms enable it to capture intricate patterns in arguments that may be challenging for other models to discern accurately.

What are the limitations of compact language models like BERT in argument mining compared to larger models?

Compact language models like BERT have certain limitations in argument mining when compared to larger models. One key limitation is their restricted capacity to capture fine-grained details and complex relationships within arguments due to their smaller parameter sizes. This limitation may result in less accurate classifications or interpretations of arguments that require deep contextual understanding. Moreover, compact models like BERT may struggle with processing lengthy texts or handling diverse datasets effectively, leading to potential performance bottlenecks in tasks requiring extensive reasoning abilities.

How can prompting techniques like Tree of Thoughts improve reasoning capabilities in language models?

Prompting techniques such as Tree of Thoughts can significantly enhance reasoning capabilities in language models by providing structured guidance for generating responses based on logical frameworks. By incorporating hierarchical prompts that guide the model through a series of interconnected steps or branches, Tree of Thoughts enables the model to follow a coherent thought process similar to human reasoning. This approach helps organize information hierarchically, facilitating better comprehension and synthesis of complex ideas within the context provided. Ultimately, prompting techniques like Tree of Thoughts empower language models to engage in more sophisticated reasoning tasks with improved accuracy and coherence.