Improving Answer Extraction in Context-based Question Answering Systems Using LLMs
Researchers have developed a new method to improve answer extraction in question answering systems that use large language models. The approach involves fine-tuning a pre-trained LLM on the SQuAD1.1 dataset to enhance its ability to understand context and extract precise answers. Experiments showed that a fine-tuned Roberta-base model achieved high scores in ROUGE-L, BLEU, and BERTScore, demonstrating improved accuracy and relevance. AI
IMPACT Enhances the precision and reliability of LLM-based question answering, potentially improving user experience and data extraction capabilities.