Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting

• URL: http://arxiv.org/abs/2408.09439v2
• Date: Fri, 06 Dec 2024 12:09:15 GMT
• Title: Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
• Authors: Zeyuan Chen, Haiyan Wu, Kaixin Wu, Wei Chen, Mingjie Zhong, Jia Xu, Zhongyi Liu, Wei Zhang,
• Abstract summary: This study proposes leveraging user interactions recorded in search logs to yield insights into users' implicit search intentions.<n>We propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with Large Language Models.
• Score: 23.61061000692023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditional models only rely on the semantic congruence between queries and items to ascertain relevance. However, this approach represents merely one aspect of the relevance judgement, and is insufficient in isolation. Even powerful Large Language Models (LLMs) still cannot accurately judge the relevance of a query and an item from a semantic perspective. To augment LLMs-driven relevance modeling, this study proposes leveraging user interactions recorded in search logs to yield insights into users' implicit search intentions. The challenge lies in the effective prompting of LLMs to capture dynamic search intentions, which poses several obstacles in real-world relevance scenarios, i.e., the absence of domain-specific knowledge, the inadequacy of an isolated prompt, and the prohibitive costs associated with deploying LLMs. In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively. Specifically, we perform the user-driven behavior neighbors retrieval from the daily search logs to obtain domain-specific knowledge in time, retrieving candidates that users consider to meet their expectations. Then, we guide LLMs for relevance modeling by employing advanced prompting techniques that progressively improve the outputs of the LLMs, followed by a progressive aggregation with comprehensive consideration of diverse aspects. For online serving, we have developed an industrial application framework tailored for the deployment of LLMs in relevance modeling. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance.

Related papers

• Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce [61.03081096959132]
  We propose a context-aware reasoning-enhanced generative search framework for better textbfunderstanding the complicated context.<n>Our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
  arXiv  Detail & Related papers  (2025-10-19T16:46:11Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• LESER: Learning to Expand via Search Engine-feedback Reinforcement in e-Commerce [6.294743632371883]
  User queries in e-commerce search are often vague, short, and underspecified.<n>Existing methods, including neural query expansion and prompting-based LLM approaches, fall short in real-world settings.<n>We propose Learning to Expand via Search Engine Reinforcement (LESER), a novel framework that fine-tunes a context-aware LLM using real-time search engine feedback.
  arXiv  Detail & Related papers  (2025-09-06T02:54:13Z)
• AI Guided Accelerator For Search Experience [4.832123045961485]
  We propose a novel framework that explicitly models transitional queries--intermediate reformulations occurring during the user's journey toward their final purchase intent.<n>This approach allows us to model a user's shopping funnel, where mid-journey transitions reflect exploratory behavior and intent refinement.<n>Our contributions include (i) the formal identification and modeling of transitional queries, (ii) the introduction of a structured query sequence mining pipeline for intent flow understanding, and (iii) the application of LLMs for scalable, intent-aware query expansion.
  arXiv  Detail & Related papers  (2025-07-25T23:26:00Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• CLEAR-KGQA: Clarification-Enhanced Ambiguity Resolution for Knowledge Graph Question Answering [13.624962763072899]
  KGQA systems typically assume user queries are unambiguous, which is an assumption that rarely holds in real-world applications. We propose a novel framework that dynamically handles both entity ambiguity (e.g., distinguishing between entities with similar names) and intent ambiguity (e.g., clarifying different interpretations of user queries) through interactive clarification.
  arXiv  Detail & Related papers  (2025-04-13T17:34:35Z)
• How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective [64.00022624183781]
  Large language models (LLMs) can assess relevance and support information retrieval (IR) tasks. We investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability.
  arXiv  Detail & Related papers  (2025-04-10T16:14:55Z)
• Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning [14.224921308101624]
  We propose a novel Distribution-Aware Robust Learning framework (DaRL) for relevance modeling. DaRL has been deployed online to serve the Alipay's insurance product search.
  arXiv  Detail & Related papers  (2024-12-17T03:10:47Z)
• CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search [34.08551439233784]
  CPRM is a framework designed for the continual pre-training of large language models (LLMs) Our framework includes three modules: 1) employing both queries and multi-field item to jointly pre-train for enhancing domain knowledge, 2) applying in-context pre-training, and 3) conducting reading comprehension on items to produce associated domain knowledge and background information.
  arXiv  Detail & Related papers  (2024-12-02T08:35:54Z)
• LLM-assisted Explicit and Implicit Multi-interest Learning Framework for Sequential Recommendation [50.98046887582194]
  We propose an explicit and implicit multi-interest learning framework to model user interests on two levels: behavior and semantics. The proposed EIMF framework effectively and efficiently combines small models with LLM to improve the accuracy of multi-interest modeling.
  arXiv  Detail & Related papers  (2024-11-14T13:00:23Z)
• Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models [1.3980986259786221]
  This paper examines the integration of Large Language Models (LLMs) within existing systems. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries.
  arXiv  Detail & Related papers  (2024-09-24T16:31:33Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation [38.98478510165569]
  This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation process. LLMs are used to generate a vast array of simulated users (LLM agents), enabling the exploration of a much broader range of user needs.
  arXiv  Detail & Related papers  (2024-04-04T17:36:29Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications [0.7832189413179361]
  Large Language Models (LLMs) excel in comprehending and generating human-like text. This paper explores strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems.
  arXiv  Detail & Related papers  (2023-11-21T02:01:01Z)
• Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL [62.824464372594576]
  We aim to enhance arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization. We introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data.
  arXiv  Detail & Related papers  (2023-09-13T01:12:52Z)
• Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning [25.356999988217325]
  Relevance modeling aims to locate desirable items for corresponding queries. auxiliary query-item interactions extracted from user historical behavior data could provide hints to reveal users' search intents further. Our model builds multi-level co-attention for distilling coarse-grained and fine-grained semantic representations from both neighbor and target views.
  arXiv  Detail & Related papers  (2023-08-10T06:52:53Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.