arXiv:2606.06197v1 Announce Type: new 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 answering system based on large language models, where the input consists of a textual context and a
The continuous evolution of large language models (LLMs) and the increasing complexity of information necessitate improved methods for accurate and context-aware answer extraction.
Enhanced answer extraction directly impacts the reliability and utility of AI systems, particularly in critical applications requiring accurate information retrieval and synthesis.
The ability of question answering systems to provide precise, contextually relevant answers will improve, reducing ambiguity and increasing user trust in AI-generated information.
- · AI software developers
- · Information services
- · Research institutions
- · Legal and medical sectors
- · Platforms with inaccurate QA systems
Increased efficiency in information retrieval and knowledge management processes across various industries.
Accelerated development of more sophisticated AI agents capable of nuanced understanding and interaction.
Potential for a new class of AI applications that synthesize and present highly refined, validated information, shifting traditional knowledge work.
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