RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems

• URL: http://arxiv.org/abs/2501.18056v1
• Date: Wed, 29 Jan 2025 23:41:12 GMT
• Title: RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems
• Authors: Duy A. Nguyen, Rishi Kesav Mohan, Van Yang, Pritom Saha Akash, Kevin Chen-Chuan Chang,
• Abstract summary: We propose a novel hybrid pipeline for QR that balances efficiency and effectiveness. Our approach combines offline knowledge distillation to create a lightweight but efficient student model with online reinforcement learning (RL) to refine query rewriting dynamically using real-time feedback. Experimental results on Amazon ESCI dataset demonstrate significant improvements in query relevance, diversity, and adaptability.
• Score: 19.674493253615235
• License:
• Abstract: Query rewriting (QR) is a critical technique in e-commerce search, addressing the lexical gap between user queries and product descriptions to enhance search performance. Existing QR approaches typically fall into two categories: discriminative models and generative methods leveraging large language models (LLMs). Discriminative models often struggle with natural language understanding and offer limited flexibility in rewriting, while generative LLMs, despite producing high-quality rewrites, face high inference latency and cost in online settings. These limitations force offline deployment, making them vulnerable to issues like information staleness and semantic drift. To overcome these challenges, we propose a novel hybrid pipeline for QR that balances efficiency and effectiveness. Our approach combines offline knowledge distillation to create a lightweight but efficient student model with online reinforcement learning (RL) to refine query rewriting dynamically using real-time feedback. A key innovation is the use of LLMs as simulated human feedback, enabling scalable reward signals and cost-effective evaluation without manual annotations. Experimental results on Amazon ESCI dataset demonstrate significant improvements in query relevance, diversity, and adaptability, as well as positive feedback from the LLM simulation. This work contributes to advancing LLM capabilities for domain-specific applications, offering a robust solution for dynamic and complex e-commerce search environments.

Related papers

• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Multi-Agent Conversational Online Learning for Adaptive LLM Response Identification [31.120233988281328]
  This paper introduces textitMACO (underlineMulti-underlineAgent underlineConversational underlineOnline Learning for Adaptive LLM Response Identification) A novel conversational mechanism is proposed to adaptively conduct conversations for soliciting user preferences. cadi significantly outperforms the current state-of-the-art in online LLM response identification.
  arXiv  Detail & Related papers  (2025-01-03T14:59:38Z)
• 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)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Reliable, Adaptable, and Attributable Language Models with Retrieval [144.26890121729514]
  Parametric language models (LMs) are trained on vast amounts of web data. They face practical challenges such as hallucinations, difficulty in adapting to new data distributions, and a lack of verifiability. We advocate for retrieval-augmented LMs to replace parametric LMs as the next generation of LMs.
  arXiv  Detail & Related papers  (2024-03-05T18:22:33Z)
• 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)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.