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

arXiv: https://arxiv.org/abs/2606.06197 · HTML: https://mlsanity.com/paper/2606.06197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 24 of 24)

- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2305.06984/) (2023)
- [Rationale-Guided Retrieval Augmented Generation for Medical Question Answering](https://mlsanity.com/paper/2411.00300/) (2024)
- [Never Lost in the Middle: Mastering Long-Context Question Answering with Position-Agnostic Decompositional Training](https://mlsanity.com/paper/2311.09198/) (2023)
- [LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models](https://mlsanity.com/paper/2501.05464/) (2024)
- [Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities](https://mlsanity.com/paper/2505.20099/) (2025)
- [NativQA: Multilingual Culturally-Aligned Natural Query for LLMs](https://mlsanity.com/paper/2407.09823/) (2024)
- [Context-Aware Answer Extraction in Question Answering](https://mlsanity.com/paper/2011.02687/) (2020)
- [TrustUQA: A Trustful Framework for Unified Structured Data Question Answering](https://mlsanity.com/paper/2406.18916/) (2024)
- [Desiderata for the Context Use of Question Answering Systems](https://mlsanity.com/paper/2401.18001/) (2024)
- [LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems](https://mlsanity.com/paper/2403.00982/) (2024)
- [LLM-DaaS: LLM-driven Drone-as-a-Service Operations from Text User Requests](https://mlsanity.com/paper/2412.11672/) (2024)
- [Hybrid Graphs for Table-and-Text based Question Answering using LLMs](https://mlsanity.com/paper/2501.17767/) (2025)
- [LLM-SEM: A Sentiment-Based Student Engagement Metric Using LLMS for E-Learning Platforms](https://mlsanity.com/paper/2412.13765/) (2024)
- [RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts](https://mlsanity.com/paper/2412.15254/) (2024)
- [ERPA: Efficient RPA Model Integrating OCR and LLMs for Intelligent Document Processing](https://mlsanity.com/paper/2412.19840/) (2024)
- [How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering](https://mlsanity.com/paper/2401.05777/) (2024)
- [Optimized Quran Passage Retrieval Using an Expanded QA Dataset and Fine-Tuned Language Models](https://mlsanity.com/paper/2412.11431/) (2024)
- [Cross-Language Approach for Quranic QA](https://mlsanity.com/paper/2501.17449/) (2025)
- [Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models](https://mlsanity.com/paper/2410.12444/) (2024)
- [Few-Shot Optimized Framework for Hallucination Detection in Resource-Limited NLP Systems](https://mlsanity.com/paper/2501.16616/) (2025)
- [Few-Shot Prompting for Extractive Quranic QA with Instruction-Tuned LLMs](https://mlsanity.com/paper/2508.06103/) (2025)
- [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction](https://mlsanity.com/paper/2508.06971/) (2025)
- [How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?](https://mlsanity.com/paper/2509.21732/) (2025)
- [MSA at SemEval-2025 Task 3: High Quality Weak Labeling and LLM Ensemble Verification for Multilingual Hallucination Detection](https://mlsanity.com/paper/2505.20880/) (2025)

## Abstract (arXiv)

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 of current QA systems, particularly their tendency to produce irrelevant or imprecise responses despite having access to the correct context. Our methodology involves fine-tuning a pre-trained LLM on a benchmark QA dataset to improve its contextual comprehension and answer extraction capabilities. Specifically, we utilize the Stanford Question Answering Dataset (SQuAD1.1), which provides high-quality context-question-answer triplets for supervised training and evaluation. Experimental results show that the fine-tuned Roberta-base model achieves the highest performance, attaining a ROUGE-L score of 86.84%, a BLEU score of 28.24%, and a BERTScore of 95.38%. These results indicate strong accuracy and answer relevance, demonstrating the effectiveness of the proposed approach for context-based question answering tasks. Furthermore, the findings confirm that targeted fine-tuning substantially improves the reliability and precision of QA systems.