A Survey of Large Language Model Agents for Question Answering (2503.19213v1)

Abstract: This paper surveys the development of LLM-based agents for question answering (QA). Traditional agents face significant limitations, including substantial data requirements and difficulty in generalizing to new environments. LLM-based agents address these challenges by leveraging LLMs as their core reasoning engine. These agents achieve superior QA results compared to traditional QA pipelines and naive LLM QA systems by enabling interaction with external environments. We systematically review the design of LLM agents in the context of QA tasks, organizing our discussion across key stages: planning, question understanding, information retrieval, and answer generation. Additionally, this paper identifies ongoing challenges and explores future research directions to enhance the performance of LLM agent QA systems.

A Survey of LLM Agents for Question Answering

The paper presents an exhaustive survey of LLM agents designed specifically for Question Answering (QA) tasks, addressing the evolution, challenges, and future directions in this rapidly changing landscape of artificial intelligence. Traditional QA systems have faced limitations in data requirements and adaptability across varied contexts, often struggling to generalize efficiently. In contrast, LLM-based agents utilize the comprehensive capabilities of LLMs as their central mechanism for reasoning, offering noteworthy improvements over existing models by enabling effective interaction with their environment.

Overview and Contributions

The paper provides a structured taxonomy to facilitate deeper insights into the architecture and function of LLM-based agents. It categorizes the process of QA using LLM agents into key stages: planning, question understanding, information retrieval, and answer generation. Each stage is examined to highlight how LLM agents are designed to optimize performance, leveraging the unique capacity of LLMs to interact dynamically with multiple informational contexts and environments. The survey also identifies ongoing challenges in the field and outlines prospective areas for research enhancement.

Methodological Insights

1. Planning: LLM-based QA systems advance over prior frameworks by incorporating more sophisticated planning mechanisms. They leverage the embedded knowledge within LLMs to make decisions on-the-fly regarding information retrieval and processing, dynamically adapting strategies based on the specific context of a query.
2. Question Understanding: The inherent linguistic capabilities of LLMs streamlines interpreting complex queries, allowing LLMs to replace traditional, task-specific models for understanding the semantics of user inquiries.
3. Information Retrieval: The paper emphasizes a two-pronged approach comprising retrieval (using both dense and sparse methods) and ranking, wherein LLMs, though not ideal retrievers, excel in ranking tasks, often outperforming traditional methods by understanding nuanced query-document relationships.
4. Answer Generation: By integrating tools and adopting advanced prompting strategies, LLMs can enhance their reasoning. This extends their capacities to interact with external aids like code interpreters or domain-specific models, optimizing answer accuracy and relevance.
5. Follow-up Interaction: LLM agents must engage in iterative interactions with users, using advanced dialogue techniques to refine their responses, effectively managing multi-turn conversations and ensuring clarity.

Challenges and Future Directions

The paper acknowledges the existing gap in developing systems that address hallucinatory tendencies pervasive in LLMs, analogous to uncertainties that affect their accuracy in domain-specific knowledge areas. Additionally, it underscores the need for robust calibration and confidence measures within LLMs to enhance trustworthiness in real-world applications.

Furthermore, there is a compelling call to enhance reasoning abilities by exploring diverse reasoning paths during training, adopting causal inference frameworks for improved logical structuring and coherence.

The review also points to potential for developing LLM-generated document indexing schemas, an area ripe for exploration following enhanced storage and retrieval capabilities. This development could revolutionize contemporary practices in information retrieval by allowing more intuitive, semantic interpretation of data.

Implications

The survey's exploration of LLM QA agents implies significant advances in how AI systems can autonomously interact with and process external data, potentially transforming how we approach tasks ranging from conversational agents to sophisticated analytical tools in various domains such as medicine, law, and finance. By articulating these foundational aspects and potential improvements, it sets a path towards creating more intelligent, context-aware systems that can navigate complex information landscapes more proficiently.

Conclusion

In summary, this paper serves as a detailed map of the current state and potential future of LLM-based QA systems. It offers a critical analysis that not only highlights the efficiencies already achieved through LLM agents but also directs attention to the challenges that must be addressed to further augment their capabilities. As developments continue, the strategies and frameworks articulated will be crucial for guiding advancements in LLM-relayed QA methodologies, impacting a broad array of applications in AI and computational linguistics.