The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles
Pith reviewed 2026-05-09 18:14 UTC · model grok-4.3
The pith
Pre-training Expanded-SPLADE models on general text with higher learning rates produces stronger retrieval results after fine-tuning on web titles.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Fine-tuned models of higher retrieval effectiveness at both unpruned and most strict pruned settings are mostly pre-trained on a general corpus, and pre-trained with a higher learning rate, showing lower MLM accuracies. In the most strict pruned setting, those models show higher-level retrieval cost and a higher variance in the length of the individual postings list. The repetition of the general pre-training dataset does not have much effect on retrieval effectiveness.
What carries the argument
Expanded-SPLADE (ESPLADE) models that reuse the masked language modeling layer at fine-tuning time to generate sparse vector representations for retrieval.
Load-bearing premise
Differences in retrieval effectiveness and cost arise mainly from the choice of pre-training corpus and learning rate rather than from fine-tuning details, model size, or traits of the web document titles dataset.
What would settle it
A controlled experiment that pre-trains otherwise identical models on the same or different corpora while varying only the learning rate, then applies identical fine-tuning and evaluates retrieval metrics plus cost across pruning levels to check whether the reported effectiveness patterns hold.
Figures
read the original abstract
Masked Language Modeling (MLM) pre-training is one of the primary ways to initialize Neural Information Retrieval (IR) models prior to retrieval fine-tuning. However, studies show that MLM pre-trained models have limited readiness and transfer learning issues for fine-tuning them into Neural Bi-Encoder models. This paper studies the effect of different pre-training datasets and pre-training options on the MLM pre-trained models for retrieval fine-tuning. The study focuses on the SPLADE-style model, which uses the MLM layer also at fine-tuning time. More specifically, we experimented with Expanded-SPLADE (ESPLADE) models, a specific instance of SPLADE models, and in-house web document titles are used as datasets. Pre-training, fine-tuning, and evaluation with optional test-time pruning of sparse vectors are conducted. Our observations are three-fold: First, fine-tuned models of higher retrieval effectiveness at both unpruned and most strict pruned settings are mostly pre-trained on a general corpus, and pre-trained with a higher learning rate, showing lower MLM accuracies. Second, in the most strict pruned setting, those models show higher-level retrieval cost and a higher variance in the length of the individual postings list. Third, the repetition of the general pre-training dataset does not have much effect on retrieval effectiveness. The experimentation empirically identifies the potential limitations for aligning MLM pre-training to ESPLADE fine-tuning. Also, the experimentation provides an empirical observation that, at most strict pruned settings, the retrieval effectiveness is better maintained by the higher-level retrieval cost, showing the trade-off relationship between the two in our setting.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents an empirical study examining the impact of pre-training corpus choice (general vs. in-house web document titles) and learning rate on Expanded-SPLADE (ESPLADE) models. After MLM pre-training and retrieval fine-tuning, the authors report that higher-effectiveness models at both unpruned and strictly pruned settings are predominantly those pre-trained on general corpora with higher learning rates (despite lower MLM accuracy). In the strictest pruning regime these models also exhibit higher retrieval cost and greater variance in individual postings-list lengths. A third observation is that repeating the general pre-training data has negligible effect on downstream effectiveness. The work highlights potential misalignment between MLM pre-training and ESPLADE fine-tuning objectives together with an effectiveness-cost trade-off under aggressive pruning.
Significance. If the reported patterns survive proper controls and statistical validation, the observations would supply useful empirical guidance for initializing sparse retrieval models and would underscore the imperfect transfer from MLM pre-training to fine-tuned bi-encoder performance. The explicit documentation of the effectiveness-cost trade-off at strict pruning levels is a concrete, actionable finding. At present the evidential basis is too preliminary for strong claims about causality or generalizability.
major comments (3)
- [Abstract] Abstract: the central claim that 'fine-tuned models of higher retrieval effectiveness ... are mostly pre-trained on a general corpus, and pre-trained with a higher learning rate' is presented without any quantitative support (number of configurations tested, exact proportions, or selection criteria), rendering the qualifier 'mostly' unverifiable and load-bearing for the paper's main conclusion.
- [Experimental description] Experimental description (as summarized in the abstract and methods outline): no statement confirms that fine-tuning hyperparameters (learning rate, epochs, batch size, optimizer, random seeds) were held constant across the different pre-training corpora and learning-rate conditions. Without such controls the attribution of effectiveness differences to pre-training corpus and LR cannot be isolated from confounding factors.
- [Results] Results and observations: the three-fold findings are reported as directional observations without error bars, multiple independent runs, or any statistical significance tests. This absence directly undermines the reliability of the reported differences in retrieval effectiveness, cost, and postings-list variance, especially under the 'most strict pruned setting'.
minor comments (2)
- [Abstract] The abstract introduces 'Expanded-SPLADE (ESPLADE)' and 'SPLADE-style model' without a concise definition or citation to the original SPLADE work, which would aid readers unfamiliar with the architecture.
- [Abstract] Quantitative descriptors such as 'higher-level retrieval cost' and 'higher variance in the length of the individual postings list' would benefit from explicit units or relative percentages to make the trade-off observation more precise.
Simulated Author's Rebuttal
We thank the referee for the constructive comments on our empirical study of Expanded-SPLADE pre-training. We address each major comment below, indicating where revisions will be made to improve clarity, controls, and evidential support.
read point-by-point responses
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Referee: [Abstract] Abstract: the central claim that 'fine-tuned models of higher retrieval effectiveness ... are mostly pre-trained on a general corpus, and pre-trained with a higher learning rate' is presented without any quantitative support (number of configurations tested, exact proportions, or selection criteria), rendering the qualifier 'mostly' unverifiable and load-bearing for the paper's main conclusion.
Authors: We agree that the abstract would be strengthened by explicit quantitative support for the claim. The experiments evaluated a range of pre-training configurations combining corpus choice and learning rates, with the results section detailing which yielded higher effectiveness. In the revision we will update the abstract to state the total number of configurations tested and the proportion (or exact count) that align with the reported pattern, replacing the qualifier 'mostly' with a precise description such as 'the majority of' or a specific fraction. revision: yes
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Referee: [Experimental description] Experimental description (as summarized in the abstract and methods outline): no statement confirms that fine-tuning hyperparameters (learning rate, epochs, batch size, optimizer, random seeds) were held constant across the different pre-training corpora and learning-rate conditions. Without such controls the attribution of effectiveness differences to pre-training corpus and LR cannot be isolated from confounding factors.
Authors: Fine-tuning hyperparameters were held constant across all pre-training conditions to isolate the effects of corpus and learning rate. The same values were used for learning rate, number of epochs, batch size, optimizer, and random seeds in every fine-tuning run. We will add an explicit paragraph in the revised Experimental Setup section stating these fixed hyperparameters and confirming they were identical for all models. revision: yes
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Referee: [Results] Results and observations: the three-fold findings are reported as directional observations without error bars, multiple independent runs, or any statistical significance tests. This absence directly undermines the reliability of the reported differences in retrieval effectiveness, cost, and postings-list variance, especially under the 'most strict pruned setting'.
Authors: We acknowledge that the results are presented without error bars or formal statistical tests. Each configuration was run once due to the substantial computational cost of pre-training on large corpora. The directional patterns were consistent across pruning regimes and multiple retrieval metrics. In the revision we will add a limitations paragraph noting the single-run design and the absence of statistical significance testing, while emphasizing the consistency of trends; we will also explore adding limited additional runs with varied seeds for the key configurations if resources permit. revision: partial
Circularity Check
Purely empirical study with no derivation chain or self-referential predictions
full rationale
This paper is an empirical investigation of pre-training effects on Expanded-SPLADE models using in-house web document titles. It reports three observations drawn from experimental comparisons of pre-training corpora, learning rates, and dataset repetition, with no equations, first-principles derivations, or predictions that reduce to fitted parameters by construction. No self-citations are invoked as load-bearing uniqueness theorems, and no ansatz or renaming of known results occurs. The central claims rest on direct experimental outcomes rather than any closed logical loop, making the study self-contained against external benchmarks.
Axiom & Free-Parameter Ledger
free parameters (1)
- pre-training learning rate
axioms (1)
- domain assumption Masked language modeling pre-training supplies a useful initialization for SPLADE-style retrieval fine-tuning.
Reference graph
Works this paper leans on
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[1]
love" can be compounded by two WordPiece subword vocabulary
to sparsify query and document vectors while minimizing the ranking loss. The ESPLADE models (Dudek et al., 2023) propose a joint FLOPS regularization loss, which sparsifies the intersection of query and document, which are more directly related to the retrieval efficiency. (Yang et al., 2021) presents ways to learning more sparsified representations by f...
work page 2023
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[2]
Due to the diverse nature of queries and documents that we possess, the dataset is multilingual, but centric primarily to Korean. There have been concepts in quantitative linguistics, such as Zipf’s law, and recent work (e.g., Zoph et al. (2016); Johnson et al. (2017); Feng et al. (2020)) in the NLP community identifies the similarity in the latent struct...
work page 2016
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[3]
Rough example can be found in Table 2 and Table 1 show respectively. 27 Kim, Lee and Won Appendix E. Statistics of EMLM Logit Vectors doc-score-avg topk doc-score-std topk logit- score-std non-neg- terms-avg non-neg- terms-stdemlm model name 10 100 all 10 100 all emlm-ptd-overlap-repeat-lr-l 11.9609 8.4609 -1.4414 2.66 1.63 2.54 5.82 42,791 36,406 emlm-pt...
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[4]
Currently, we expect this is due to overfitting, as repetition in the corpus can make predictions easier, the model can have a higher margin to positive logit and other logits on the basis of classification loss, with multiple steps of optimization in a similar input context. This can decrease entropy in score values and entropy of non-negative scored log...
work page 2016
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[5]
The pre-training dataset of emlm-ptd-overlap-repeat-* is overlapped with trainset, which has a similar distribution to the validset. One possible interpretation of metrics is that the MLM model gives higher logit-score-std, non-neg-terms-avg, and non-neg-terms-std when the input data is in-fine-tuning. For a speculation, we can assume that masking the sam...
work page 2016
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[6]
This can be interpretable as the higher learning rate promotes higher variance on the use of logit indices in terms of scores, and even more when the pre-training corpus is general. For all models, compared to the models with a lower learning rate, logit-score-std is increased; however, non-neg-terms-avg and non-neg-terms-std are decreased. A possible con...
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[7]
This can be related to the result of both in-fine-tuning pretraining and in-fine-tuning validation data, where such alignment can increase the absolute scale of scores
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[8]
This can be related to the combination of a higher learning rate and repeated and general pre-training data
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[9]
30 The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles Appendix F
This might represent that diverse prediction labels from non-repeated pre-training data are helpful for increasing the score scale, even in the data is general. 30 The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles Appendix F. The Effect of Pre-training Steps emlm-ptd-indep-uniq-lr-h (9.65) 0 0.5 1 1.5 2 2.5 3 ·106 1 1.2 1.4 1.6 1.8 2...
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[10]
Kim et al. (2025) shows that the effect of such distributions results in low retrieval effectiveness when pruned. 35 Kim, Lee and Won Appendix H. Postings List Length Variances and Retrieval Effectiveness, Efficiency Section 4.1.4 shows that the relationship of higher variances of postings list length to higher retrieval effectiveness and decreased retrie...
work page 2025
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[11]
Zipfian-like semantic structure of a general natural language corpus, where literatures characterize them as "Small World Structures" whose structures have strong local clustering and low distances, having hubs that have a high number of connections (e.g., Steyvers and Tenenbaum (2005); Cancho and Solé (2001)). Although our learned sparse representations ...
work page 2005
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[12]
where is the capital of Canada?
For example, the representation of "where is the capital of Canada?" should be similar to "where is the capital of Japan?", and can be less similar to "Ottawa" in the MLM pre-training, as the typical MLM does not map Q and D together, only learns the overall context of individual sentences. Some similarity can also be provided between "where is the capita...
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[13]
This understanding led us to think about a method to evaluate whether the model is underfitting for the retrieval downstream task (i.e., by too much regularization from too much MLM pre-training) as well as evaluate whether the model will be overfit in the retrieval downstream task (i.e., by too little regularization from too little MLM pre-training). For...
work page 2019
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[14]
This can be viewed as an additional bottleneck layer of the model, in addition to the interactional bottleneck of Q, D representations compared to the cross-encoder models (Nogueira and Cho, 2019; Luan et al., 2021)
work page 2019
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[15]
For this purpose, we can think of the role of sparse neural matching as a decomposer, or technically, a hash function that divides common concepts into fine-grained concepts. 38 The Pre-Training Study of Expanded-SPLADE Models on Web Document Titles of using larger output vocabularies instead, which is directly related to the informativeness of output spa...
work page 2025
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[16]
Whether it is big or small, such a trade-off can exist as a similarity component in the loss changes
However, there is a possibility that the models tend to output more terms in this case to maintain informativeness of representations where cosine similarity only concerns angle, as different scales of scores with the same angle are indistinguishable. Whether it is big or small, such a trade-off can exist as a similarity component in the loss changes. 40 ...
work page 2009
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[17]
e.g., Google Revisits 15% Unseen Queries Statistic In Context Of AI Search. Search Engine Journal. 2025. 41 Kim, Lee and Won forces can mainly focus on covering instances of Q and D generalizable from its training set. We expect the above hypothesis can be related to a spurious correlation caused by less generalizable input. In a practical case, we observ...
work page 2025
discussion (0)
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