Comparing LLMs for Text Summarization and Question Answering

This article explores the capabilities of four prominent Large Language Models (LLMs): BERT, DistilBERT, BART, and T5, focusing on their application in text summarization and question answering. Each model possesses unique architectural strengths, impacting performance and efficiency. The comparative analysis utilizes the CNN/DailyMail dataset for summarization and the SQuAD dataset for question answering.

Learning Objectives: Participants will learn to differentiate between these LLMs, understand the core principles of text summarization and question answering, select appropriate models based on computational needs and desired output quality, implement these models practically, and analyze results using real-world datasets.

Text Summarization: The article contrasts BART and T5. BART, a bidirectional and autoregressive transformer, processes text bidirectionally to grasp context before generating a left-to-right summary, combining BERT's bidirectional approach with GPT's autoregressive generation. T5, a text-to-text transfer transformer, produces abstractive summaries, often rephrasing content for conciseness. While T5 is generally faster, BART may exhibit superior fluency in certain contexts.

Question Answering: The comparison focuses on BERT and DistilBERT. BERT, a bidirectional encoder, excels at understanding contextual meaning, identifying relevant text segments to answer questions accurately. DistilBERT, a smaller, faster version of BERT, achieves comparable results with reduced computational demands. While BERT offers higher accuracy for complex queries, DistilBERT's speed is advantageous for applications prioritizing rapid response times.

Code Implementation and Datasets: The article provides Python code utilizing the transformers and datasets libraries from Hugging Face. The CNN/DailyMail dataset (for summarization) and the SQuAD dataset (for question answering) are employed. A subset of each dataset is used for efficiency. The code demonstrates pipeline creation, dataset loading, and performance evaluation for each model.

Performance Analysis and Results: The code includes functions to analyze summarization and question-answering performance, measuring both accuracy and processing time. Results are presented in tables, comparing the summaries and answers generated by each model, alongside their respective processing times. These results highlight the trade-off between speed and output quality.

Key Insights and Conclusion: The analysis reveals that lighter models (DistilBERT and T5) prioritize speed, while larger models (BERT and BART) prioritize accuracy and detail. The choice of model depends on the specific application's requirements, balancing speed and accuracy. The article concludes by summarizing key takeaways and answering frequently asked questions about the models and their applications.

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