Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers

• URL: http://arxiv.org/abs/2504.14839v1
• Date: Mon, 21 Apr 2025 03:40:43 GMT
• Title: Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers
• Authors: Xinjie Shen, Zhichao Geng, Yang Yang,
• Abstract summary: Existing sparse retrieval models rely on FLOPS regularization for sparsification.<n>Previous attempts to adapt FLOPS for inference-free scenarios have been limited to rule-based methods.<n>We show that our method achieves state-of-the-art performance among inference-free sparse retrieval models.
• Score: 4.682757367266358
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With increasing demands for efficiency, information retrieval has developed a branch of sparse retrieval, further advancing towards inference-free retrieval where the documents are encoded during indexing time and there is no model-inference for queries. Existing sparse retrieval models rely on FLOPS regularization for sparsification, while this mechanism was originally designed for Siamese encoders, it is considered to be suboptimal in inference-free scenarios which is asymmetric. Previous attempts to adapt FLOPS for inference-free scenarios have been limited to rule-based methods, leaving the potential of sparsification approaches for inference-free retrieval models largely unexplored. In this paper, we explore $\ell_0$ inspired sparsification manner for inference-free retrievers. Through comprehensive out-of-domain evaluation on the BEIR benchmark, our method achieves state-of-the-art performance among inference-free sparse retrieval models and is comparable to leading Siamese sparse retrieval models. Furthermore, we provide insights into the trade-off between retrieval effectiveness and computational efficiency, demonstrating practical value for real-world applications.

Related papers

• Constrained Auto-Regressive Decoding Constrains Generative Retrieval [71.71161220261655]
  Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network.<n>In this paper, we examine the inherent limitations of constrained auto-regressive generation from two essential perspectives: constraints and beam search.
  arXiv  Detail & Related papers  (2025-04-14T06:54:49Z)
• Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets [15.549852480638066]
  We propose a novel paradigm for re-ranking called online relevance estimation. Online relevance estimation continuously updates relevance estimates for a query throughout the ranking process. We validate our approach on TREC benchmarks under two scenarios: hybrid retrieval and adaptive retrieval.
  arXiv  Detail & Related papers  (2025-04-12T22:05:50Z)
• Supervised Optimism Correction: Be Confident When LLMs Are Sure [91.7459076316849]
  We establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning. We show that the widely used beam search method suffers from unacceptable over-optimism. We propose Supervised Optimism Correction, which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations.
  arXiv  Detail & Related papers  (2025-04-10T07:50:03Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  We investigate how model size, training data scale, and inference-time compute jointly influence generative retrieval performance.<n>Our experiments show that n-gram-based methods demonstrate strong alignment with both training and inference scaling laws.<n>We find that LLaMA models consistently outperform T5 models, suggesting a particular advantage for larger decoder-only models in generative retrieval.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• Unifying Generative and Dense Retrieval for Sequential Recommendation [37.402860622707244]
  We propose LIGER, a hybrid model that combines the strengths of sequential dense retrieval and generative retrieval.<n> LIGER integrates sequential dense retrieval into generative retrieval, mitigating performance differences and enhancing cold-start item recommendation.<n>This hybrid approach provides insights into the trade-offs between these approaches and demonstrates improvements in efficiency and effectiveness for recommendation systems in small-scale benchmarks.
  arXiv  Detail & Related papers  (2024-11-27T23:36:59Z)
• Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers [6.773411876899064]
  inference-free sparse models lag far behind in terms of search relevance when compared to both sparse and dense siamese models. We propose two different approaches for performance improvement. First, we introduce the IDF-aware FLOPS loss, which introduces Inverted Document Frequency (IDF) to the sparsification of representations. We find that it mitigates the negative impact of the FLOPS regularization on search relevance, allowing the model to achieve a better balance between accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-11-07T03:46:43Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• RAEE: A Training-Free Retrieval-Augmented Early Exiting Framework for Efficient Inference [20.250550771195726]
  This paper proposes RAEE, a training-free Retrieval-Augmented Early Exiting framework for efficient inference. Experimental results demonstrate that the proposed RAEE can significantly accelerate inference. RAEE also achieves state-of-the-art zero-shot performance on 8 classification tasks.
  arXiv  Detail & Related papers  (2024-05-24T04:01:24Z)
• Lexically-Accelerated Dense Retrieval [29.327878974130055]
  'LADR' (Lexically-Accelerated Dense Retrieval) is a simple-yet-effective approach that improves the efficiency of existing dense retrieval models. LADR consistently achieves both precision and recall that are on par with an exhaustive search on standard benchmarks.
  arXiv  Detail & Related papers  (2023-07-31T15:44:26Z)
• Learning to Rank in Generative Retrieval [62.91492903161522]
  Generative retrieval aims to generate identifier strings of relevant passages as the retrieval target. We propose a learning-to-rank framework for generative retrieval, dubbed LTRGR. This framework only requires an additional learning-to-rank training phase to enhance current generative retrieval systems.
  arXiv  Detail & Related papers  (2023-06-27T05:48:14Z)
• Fine-grained Retrieval Prompt Tuning [149.9071858259279]
  Fine-grained Retrieval Prompt Tuning steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompt and feature adaptation. Our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.
  arXiv  Detail & Related papers  (2022-07-29T04:10:04Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)

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.