Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

Researchers propose an improved question answering system using fine-tuned large language models on the SQuAD dataset, achieving strong performance metrics (ROUGE-L: 86.84%, BERTScore: 95.38%). The work addresses limitations in current LLM-based QA systems' ability to extract accurate answers from given contexts, demonstrating that targeted fine-tuning substantially enhances reliability and precision.

This academic research tackles a fundamental challenge in natural language processing: extracting accurate answers from textual contexts using large language models. The study demonstrates that general-purpose LLMs, while powerful, require specialized fine-tuning to reliably handle context-based question answering tasks. The researchers achieved this by training a RoBERTa-base model on SQuAD 1.1, a benchmark dataset containing high-quality annotated examples, resulting in impressive performance metrics across multiple evaluation frameworks.

The broader context involves the rapid evolution of LLM capabilities and their increasing deployment in enterprise and consumer applications. As organizations integrate LLMs into production systems, accuracy and consistency become critical. This work contributes to the growing body of evidence that pre-trained models benefit significantly from task-specific fine-tuning, even when those models already demonstrate strong general capabilities.

For the AI industry, this research validates an important principle: domain-specific optimization matters. Practitioners building QA systems can leverage these findings to improve their implementations. The high BERTScore suggests the model generates contextually appropriate answers, while ROUGE-L and BLEU scores indicate strong factual accuracy and linguistic quality. This has implications for developers building search, customer service, and knowledge management applications.

Looking forward, the field will likely see increased emphasis on efficient fine-tuning methods that reduce computational costs while maintaining performance gains. The paper's success with RoBERTa-base suggests smaller, domain-optimized models may rival or exceed larger general-purpose models for specific tasks, influencing deployment strategies and infrastructure requirements across the AI ecosystem.

• →Fine-tuned RoBERTa-base achieves 86.84% ROUGE-L and 95.38% BERTScore on context-based question answering tasks
• →Task-specific fine-tuning substantially improves LLM accuracy and answer relevance compared to general-purpose models
• →SQuAD dataset fine-tuning demonstrates that supervised training on quality data directly enhances contextual comprehension
• →Smaller models with targeted optimization can deliver enterprise-grade performance for QA applications
• →The research validates that domain-specific adaptation remains essential despite advances in general LLM capabilities