Exploring the Limits of T5: Multitask Fine- Tuning for Reasoning, Summarization, Sentiment Analysis and Figurative Language Understanding

This paper presents a comprehensive study on fine-tuning the T5-small model across multiple natural language processing (NLP) tasks using diverse datasets, including SNLI (Stanford Natural Language Inference), MRPC (Microsoft Research Paraphrase Corpus), SST2 (Stanford Sentiment Tree-bank), XSUM (Extreme Summarization), and IMPLI (Idiomatic and Metaphoric Paired Language Inference). Our experiments focus on optimizing model performance through various fine-tuning strategies and parameter adjustments. Initially, we explored the use of multiple prefixes in comparison to the introduction of special tokens to determine which strategy enhances input flexibility more effectively, facilitating user-specific customization. Then, we fine-tuned the model on all datasets except MRPC, followed by a subsequent fine-tuning exclusively on MRPC to investigate the phenomenon of catastrophic forgetting. To evaluate the model's understanding of figurative language using IMPLI, we conducted specific tests and subsequently fine-tuned the model to improve its reliability in handling figurative language. Our evaluation encompassed various generation techniques to assess the model's behavior post-fine-tuning. We employed distinct metrics for each task: accuracy for SNLI and SST2, accuracy and F1 for MRPC, ROUGE for XSUM, and accuracy for IMPLI. The results demonstrate the impact of different fine-tuning methodologies on the model's performance, providing insights into effective strategies for multi-task learning with T5-small.