USimAgent: Large Language Models for Simulating Search Users (2403.09142v2)

Abstract: Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes such as learning, reasoning, and planning. Recently, LLMs have demonstrated remarked potential in simulating human-level intelligence and have been used in building autonomous agents for various tasks. However, the potential of using LLMs in simulating search behaviors has not yet been fully explored. In this paper, we introduce a LLM-based user search behavior simulator, USimAgent. The proposed simulator can simulate users' querying, clicking, and stopping behaviors during search, and thus, is capable of generating complete search sessions for specific search tasks. Empirical investigation on a real user behavior dataset shows that the proposed simulator outperforms existing methods in query generation and is comparable to traditional methods in predicting user clicks and stopping behaviors. These results not only validate the effectiveness of using LLMs for user simulation but also shed light on the development of a more robust and generic user simulators. The code and data are accessible at https://github.com/Meow-E/USimAgent.

• The paper introduces USimAgent, a unified LLM framework that simulates full user search sessions by modeling queries, clicks, and stopping behaviors.
• The methodology employs context-driven reasoning to integrate past interactions, enabling realistic simulation of complex search behaviors.
• Empirical results show that USimAgent outperforms traditional methods in query generation and achieves competitive accuracy in click and stop predictions.

Introducing USimAgent: Advancing User Simulation with LLMs

Overview of USimAgent

User simulation, while a promising method for evaluating information retrieval systems, has consistently faced the challenge of replicating complex user search behaviors authentically. This paper introduces USimAgent, a LLM-based framework designed to simulate user search behaviors such as querying, clicking, and stopping. By incorporating the recent advancements in LLMs, USimAgent aims to achieve a more accurate and comprehensive simulation of a full user search session, thereby addressing the limitations of existing simulation methodologies.

Technical Foundations and Innovations

The paper's methodology section outlines the foundational aspects of user simulation, highlighting the complex and dynamic nature of user search behavior which includes generating queries, interacting with search engine results pages (SERP), and deciding when to conclude a session. Unlike previous approaches that often rely on disjointed models for simulating different aspects of search behavior, USimAgent employs a unified LLM to simulate the entire search session dynamically. This approach takes advantage of LLMs’ capabilities in natural language understanding, zero-shot/few-shot learning, multi-tasking, and coherent action planning.

A key innovation in USimAgent is its use of context-driven reasoning to simulate the cognitive processes underpinning users' search activities. By integrating context from previous interactions within a session, USimAgent can generate more realistic queries, predict clicks with comparable accuracy to traditional methods, and determine when to end a session. This is a significant step toward capturing the intricate cognitive processes that influence real-world user behavior in search contexts.

Empirical Validation

Experimental results presented in the paper underscore USimAgent’s superior performance in generating queries when compared against traditional simulation methods. By evaluating the model on a public user behavior dataset, the authors demonstrate that USimAgent not only outperforms existing query generation methods but also achieves comparable results in simulating clicking and stopping behaviors. This validation not only attests to the potential of using LLMs for user simulation but also indicates areas for further improvement, particularly in enhancing the model’s predictive capabilities for clicks and stopping decisions.

Implications and Future Directions

The development and validation of USimAgent broaden the horizons for research into information retrieval evaluation. It opens up new possibilities for utilizing LLMs in simulating user behaviors, potentially leading to more robust, efficient, and user-centric evaluation methodologies. This could have far-reaching implications for the design and improvement of information retrieval systems, making them more attuned to real-world user needs and behaviors.

However, the paper also identifies areas necessitating further research, notably in combining LLM-based simulation methods with more extensive datasets to improve predictive accuracy. Future developments could also explore the integration of advanced LLMs and the refinement of context and reasoning mechanisms within the simulation framework.

Conclusion

This paper's contribution, USimAgent, represents a notable advance in user simulation for information retrieval systems evaluation, leveraging the power of LLMs to simulate complex user search behaviors more effectively. While showcasing the potential of LLMs in this domain, the paper also lays a foundation for future research aimed at enhancing the fidelity and accuracy of user simulations, ultimately contributing to the development of more user-centric search technologies.