The Future of Question Answering: Large Language Models Analyze, Retrieve, and Reason

Interaction with computers through natural language has made enormous progress in recent years. A central aspect of this development is automated question answering (QA). While earlier systems often relied on predefined rules and rigid knowledge bases, Large Language Models (LLMs) open up new possibilities for more complex and flexible QA systems.

A promising approach that fully exploits the potential of LLMs is the combination of analysis, retrieval, and reasoning – a process referred to as ARR (Analyzing, Retrieving, Reasoning). This approach allows questions to be answered more comprehensively and precisely by combining the strengths of LLMs with external information sources.

Analyzing the Question: The First Step to a Precise Answer

The ARR process begins with the analysis of the incoming question. Here, the LLM uses its capabilities in the field of Natural Language Understanding (NLU) to understand the user's intent. The question is broken down to identify keywords, entities, and the type of information sought. This analysis forms the basis for the subsequent steps.

Retrieving Relevant Information: Expanding the Knowledge Horizon

In the next step, the system retrieves relevant information from various sources. These can be structured databases, knowledge graphs, or unstructured text documents. The previously extracted keywords and entities serve as search terms to find the most relevant information. The selection of appropriate information sources is crucial for the quality of the final answer.

Reasoning and Generating the Answer: The Art of Synthesis

After the relevant information has been retrieved, the LLM combines the acquired knowledge with its own knowledge and derives an answer. This step requires complex reasoning abilities to integrate the various pieces of information and generate a coherent and precise answer. The answer can be presented in various formats, such as text, tables, or diagrams.

Advantages of the ARR Approach: Precision, Flexibility, and Scalability

The ARR approach offers several advantages over traditional QA systems. By combining analysis, retrieval, and reasoning, more complex questions can be answered that go beyond the knowledge stored in the LLM. The flexibility of the system allows the integration of various information sources and adaptation to different application areas. Furthermore, the approach is scalable and can handle growing amounts of data.

Application Areas: From Chatbots to Expert Systems

The potential of ARR-based QA systems is enormous. They can be used in various areas, from customer service chatbots to specialized expert systems. In medicine, for example, they can assist with diagnosis support by retrieving relevant information from medical databases and specialist literature. In the field of education, they can provide personalized learning content and answer students' questions.

Future Developments: Improving Reasoning Abilities and Transparency

Despite the promising results, there are still challenges to overcome. Improving the reasoning capabilities of LLMs is a central aspect of future research. Furthermore, the transparency of the system is important to ensure the traceability of the answers. The development of methods to explain the reasoning processes is therefore another important research focus.

Bibliography: - Pan, Y., et al. (2023). Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models. *ICCV 2023 Workshops*. - Jain, P. (n.d.). Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering. - Liu, P., et al. (2021). Beyond Boundaries: A Human-like Approach for Out-of-Distribution Generalization. *Transactions of the Association for Computational Linguistics*, *9*, 1512-1527. - Perez, E., et al. (2025). ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning. *arXiv preprint arXiv:2502.04689v1*. - Sumit, R. (2024, August 24). *Tweet*. Twitter. - Wu, C., et al. (2024). A Survey on Large Language Model based Question Answering. *arXiv preprint arXiv:2312.10997*. - Yang, Z., et al. (2024). Enhancing Large Language Models with Tool Use for Complex Question Answering. *OpenReview*. - Zhang, L., et al. (2025). A Comprehensive Survey on Large Language Models for Question Answering. *Springer*. - Zhao, W., et al. (2025). Image Question Answering with Large Language Models. *ScienceDirect*.