Improving Answer Extraction in Context-based Question Answering Systems Using LLMs
Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs). However, they still face challenges in accurately extracting and generating precise answers from given contexts, particularly when dealing with complex or ambiguous queries. Existing approaches often struggle with contextual understanding, answer consistency, and generalization across diverse domains. In this work, we propose a question answering system based on large language models, where the input consists of a textual context and a corresponding question, and the output is a concise and accurate answer. The motivation behind this research lies in addressing the limitations o...
| Authors | Hafez Abdelghaffar, Ahmed Alansary, Ali Hamdi |
|---|---|
| Primary category | cs.CL |
| Published | 4 June 2026 |
| Last updated at source | 19 July 2026 |
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