Improving Answer Extraction in Context-based Question Answering Systems Using LLMs
📰 ArXiv cs.AI
Improve answer extraction in QA systems using LLMs for better contextual understanding and consistency
Action Steps
- Implement LLMs in QA systems to enhance contextual understanding
- Use fine-tuning techniques to adapt LLMs to specific domains
- Evaluate answer consistency across diverse domains
- Apply transfer learning to improve generalization
- Test and refine the QA system using real-world queries
Who Needs to Know This
NLP engineers and researchers can benefit from this approach to enhance their QA systems, while product managers can utilize it to improve customer experience
Key Insight
💡 LLMs can significantly improve answer extraction in QA systems by enhancing contextual understanding and consistency
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🤖 Improve QA systems with LLMs for better answer extraction! 📚
Full Article
Title: Improving Answer Extraction in Context-based Question Answering Systems Using LLMs
Abstract:
arXiv:2606.06197v1 Announce Type: cross Abstract: 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
Abstract:
arXiv:2606.06197v1 Announce Type: cross Abstract: 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
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