Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM (2312.15450v1)

Abstract: Search engines are crucial as they provide an efficient and easy way to access vast amounts of information on the internet for diverse information needs. User queries, even with a specific need, can differ significantly. Prior research has explored the resilience of ranking models against typical query variations like paraphrasing, misspellings, and order changes. Yet, these works overlook how diverse demographics uniquely formulate identical queries. For instance, older individuals tend to construct queries more naturally and in varied order compared to other groups. This demographic diversity necessitates enhancing the adaptability of ranking models to diverse query formulations. To this end, in this paper, we propose a framework that integrates a novel rewriting pipeline that rewrites queries from various demographic perspectives and a novel framework to enhance ranking robustness. To be specific, we use Chain of Thought (CoT) technology to utilize LLMs as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness. Our extensive experimentation on both public and industrial datasets assesses the efficacy of our query rewriting approach and the enhanced accuracy and robustness of the ranking model. The findings highlight the sophistication and effectiveness of our proposed model.

• The paper presents Agent4Ranking, showing how multi-agent LLMs personalize query rewriting to improve ranking robustness.
• It employs a two-stage methodology that integrates Chain-of-Thought LLM rewriting with a Multi-gate Mixture of Experts architecture using a hybrid loss function.
• Experimental results confirm enhanced semantic consistency and personalized search performance across diverse demographic query variations.

An Analytical Overview of "Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM"

This paper addresses a pressing concern in information retrieval systems — the need to enhance the robustness and effectiveness of search engine ranking models in handling semantically similar queries that are reformulated by users of different demographic backgrounds. The authors present a novel framework, Agent4Ranking, which incorporates a query rewriting pipeline leveraging LLMs and a robust model architecture integrated with a hybrid loss function.

Core Components and Methodology

The paper introduces a two-stage methodology. The initial stage involves employing LLMs to simulate various demographic profiles for query rewriting. This aims to tackle the query diversity problem that arises when different demographic groups express identical information needs differently. Leveraging Chain of Thought (CoT) technology, LLMs operate as agents to rewrite original queries into multiple variants. Rigorous query evaluation is performed to correct potential semantic deviations — a common challenge associated with LLMs hallucinating irrelevant outputs.

In the subsequent stage, the researchers develop a robust Multi-gate Mixture of Experts (MMoE) architecture. This is complemented by a hybrid loss function incorporating Jensen-Shannon divergence to ensure consistent ranking outcomes across the spectrum of rewritten queries. The MMoE setup dynamically identifies and capitalizes on shared semantic content inherent in the varied queries produced by the LLM agents, thus enhancing the ranking model's resilience.

Experimental Evaluation

Extensive experimental validation was conducted using both public and industrial datasets. The results demonstrate the efficacy of the proposed LLM-driven rewriting approach and the robustness enhancements achieved by the novel ranking model framework. Quantitative assessments reveal that incorporating demographic-aware query rewriting and MMoE architecture yields consistent ranking performance across semantically similar input variations.

Implications and Future Directions

The integration of diverse demographic perspectives via LLMs, as demonstrated in Agent4Ranking, highlights significant implications for personalized information retrieval. This approach opens avenues for employing LLMs in multi-agent settings to tackle domain-specific challenges in search engines and beyond. The robust MMoE architecture, coupled with targeted loss functions, contributes to a broader understanding of how robustness in ranking models can be systematically achieved and measured.

Future research could focus on refining the LLM agent frameworks to further minimize hallucination effects and improve prompt engineering strategies. Additionally, the application of such robust ranking systems can be explored in other domains, such as recommender systems or conversational agents, where the same semantic variations present similar robustness challenges.

Overall, the proposed research enriches the domain of information retrieval by integrating cutting-edge language generation techniques with robust modeling strategies, steering towards robust and adaptable search systems capable of handling the nuanced diversity of user inputs.