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arxiv: 2606.02814 · v1 · pith:Y7AXTEYZnew · submitted 2026-06-01 · 💻 cs.IR · cs.AI· cs.CL

Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors

Francisco Valentini , Edgar Altszyler , Martin Fajcik This is my paper

Pith reviewed 2026-06-28 12:19 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.CL
keywords neural retrieversrelevance priorsannotation biasfindability gapbi-encoderinformation retrievalsupervised dense retrieval
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The pith

Supervised neural retrievers encode query-independent relevance priors from annotation biases that create a findability gap for less favored documents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates whether neural retrievers learn document-level preferences independent of any query. It demonstrates that these priors generalize to unseen documents and remain consistent across different supervised models. The priors arise as a side effect of training on annotated pairs where annotation protocols favor certain document types. This produces a findability gap in which genuinely relevant documents with lower prior scores are harder to retrieve. The effect is pronounced in dense retrievers but weaker in BM25, and LLM analysis links the bias to preferences for comprehensive mainstream content over niche material.

Core claim

Supervised bi-encoder retrievers implicitly learn a document-level relevance prior encoded in their representation space as a side effect of training on annotated data. This prior is estimated by training simple classifiers on frozen document embeddings, generalizes to unseen documents, and proves consistent across models and benchmarks. It creates a findability gap where documents with lower prior are systematically harder to retrieve even when relevant, an effect that persists under matched-document controls. LLM-based explanations show that judged-relevant documents tend to be comprehensive self-contained summaries of mainstream topics while niche or technical content is often left unjudg

What carries the argument

The query-independent relevance prior, isolated by training classifiers on frozen document embeddings from the retriever representation space.

If this is right

  • Documents with lower relevance prior are systematically ranked lower even when genuinely relevant.
  • The bias appears consistently across multiple state-of-the-art supervised retrievers and IR benchmarks.
  • The effect is weaker and less consistent in BM25 than in dense retrievers.
  • Annotation selection favors comprehensive mainstream documents over niche or fragmentary ones.
  • Retrievers rank documents according to these learned features independently of actual query relevance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future training pipelines could mitigate the gap by deliberately including more diverse unjudged documents in annotation pools.
  • Standard IR evaluation may need separate metrics for document findability to isolate this effect from relevance.
  • The same annotation-induced priors could appear in other supervised embedding models outside retrieval.
  • Long-tail or technical queries may suffer disproportionately from the findability gap.

Load-bearing premise

Training simple classifiers on frozen document embeddings isolates a query-independent relevance prior caused by annotation selection bias rather than other embedding properties.

What would settle it

A test in which documents matched for relevance but differing in prior score show no systematic difference in retrieval rank across the models would falsify the claim.

Figures

Figures reproduced from arXiv: 2606.02814 by Edgar Altszyler, Francisco Valentini, Martin Fajcik.

Figure 1
Figure 1. Figure 1: UMAP projections of E5 document embeddings for a random sample of 1k positive and 1k negative [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Model findability versus prior scores from [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: AUC of the BGE-based prior model on held￾out documents across datasets. Error bars show 95 % bootstrap confidence intervals. Refer to App. B for details on dataset counts and AUC values. 2024), NV-Embed-v25 (Lee et al., 2024), and gte￾Qwen2-7B-instruct6 (Li et al., 2023) (hereafter BGE, NV-EMBED, and GTE). We used BGE as our anchor model, because its exact training data is publicly available7 , and trained… view at source ↗
Figure 4
Figure 4. Figure 4: Average findability vs. average relevance prior across datasets. Bins contain equal numbers of documents [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average findability difference between high [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prior score distributions for test documents [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Model findability versus the spurious [X] condition for test documents. Error bars represent 95 % confidence intervals over documents within each bin. B Prior Model and Findability [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Cross-model agreement of prior scores from models trained on different retriever embeddings. Each point [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of relevance prior scores across [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
read the original abstract

Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs. Yet annotation protocols may not purely reflect relevance: they select only a subset of documents for labeling, and this selection can favor certain document types over others. We investigate whether supervised bi-encoder retrievers implicitly learn a document-level relevance prior: a query-independent signal encoded in their representation space as a side effect of training on annotated data. We estimate this prior by training simple classifiers on frozen document embeddings and evaluate three state-of-the-art retrievers across multiple IR benchmarks. We find that supervised neural retrievers encode relevance priors that generalize to unseen documents and are consistent across models. These priors create a findability gap: documents with lower prior are systematically harder to retrieve, even when genuinely relevant. This effect appears in supervised dense retrievers but is weaker and less consistent in BM25, and it persists under controlled matched-document comparisons. Using LLM-based explanations, we find that judged-relevant documents tend to be comprehensive, self-contained summaries of mainstream topics, while niche, fragmentary, or highly technical content is often left unjudged. Retrievers internalize this bias, ranking documents with these favored features higher than documents that lack them, independently of their actual relevance. Our findings expose a structural limitation of supervised retrieval: models trained on annotated data do not just learn relevance, but also the implicit document preferences in their training data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript claims that supervised bi-encoder retrievers implicitly learn query-independent relevance priors as a side effect of training on annotated data, where annotation selection favors certain document types. These priors are recovered by training simple classifiers on frozen document embeddings, generalize to unseen documents, are consistent across three state-of-the-art retrievers, and produce a measurable findability gap (documents with lower prior scores are harder to retrieve even when relevant). The effect is weaker and less consistent for BM25, persists under matched-document controls, and is supported by LLM-based qualitative analysis showing preference for comprehensive, mainstream documents over niche or technical ones.

Significance. If the empirical findings hold, the work is significant for information retrieval because it identifies a structural limitation of supervised retrieval: models internalize annotation biases rather than learning pure relevance. This has implications for fairness, robustness, and evaluation practices. Strengths include the multi-retriever, multi-benchmark design, matched-document controls that hold other properties fixed, cross-model consistency checks, and the combination of quantitative retrieval metrics with qualitative LLM explanations. The approach avoids circularity by using frozen embeddings and simple classifiers.

minor comments (3)
  1. [Methods / Experiments] The abstract and described method indicate use of held-out documents and matched controls, but the manuscript should explicitly report the exact data splits, number of documents per benchmark, and any statistical tests (e.g., significance of the findability gap) to allow full assessment of generalizability.
  2. [Evaluation] Clarify the precise definition and computation of the 'findability gap' metric (e.g., how retrieval performance is compared for high- vs. low-prior documents under matched conditions) to ensure the quantitative claim is unambiguous.
  3. [Qualitative Analysis] The LLM-based explanations are presented as supporting evidence; include inter-annotator agreement or validation steps for the qualitative categories (comprehensive vs. niche) to strengthen that component.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their thorough and supportive review, which accurately captures the core claims, methodology, and implications of our work. The recommendation for minor revision is appreciated. Since no specific major comments were raised, we have no point-by-point rebuttals to provide and will incorporate minor clarifications in the revised manuscript where they improve readability.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper presents an empirical investigation that estimates a query-independent relevance prior by training simple classifiers on frozen document embeddings from supervised bi-encoders, then measures consistency across models, generalization to held-out documents, and a findability gap under matched-document controls. These steps rely on standard probing accuracy, retrieval metrics (e.g., ranking performance differences), and qualitative LLM explanations rather than any derivation, equation, or self-citation that reduces a claimed result to a fitted parameter defined from the target quantity itself. The central findings are falsifiable via external benchmarks and do not invoke uniqueness theorems or ansatzes from prior author work as load-bearing premises.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the validity of the probing classifier method as a measure of learned priors and on the representativeness of the chosen IR benchmarks and retrievers; no free parameters, axioms, or invented entities are introduced beyond standard supervised learning assumptions.

pith-pipeline@v0.9.1-grok · 5785 in / 1143 out tokens · 32251 ms · 2026-06-28T12:19:05.776878+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

16 extracted references · 5 canonical work pages · 2 internal anchors

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    What are the main features of Class X documents?

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    What are the main features of Class Y documents?

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    What do they have in common?

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    Class X” (low-prior or unjudged) and “Class Y

    What are the main differences? Keep your answer clear and well-organized. The goal is to help someone distinguish between Class X and Class Y documents based on their characteristics. Table 9: Prompt used for LLM-based explanation gen- eration (M1). Class labels are anonymized as “Class X” (low-prior or unjudged) and “Class Y” (high-prior or relevant) to ...

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    The following is the filmography of

    Main features of Class X documents * Short and Fragmented: They are typically very brief, often consisting of just one or two sentences, a single bullet point, or even just a section heading. * Highly Specific or Trivia-focused: They tend to focus on hyper-specific details, minor trivia, or raw data (e.g., specific census demographics, a single quote from...

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    * Narrative and Descriptive: They are written in a continuous, narrative style that flows logically from one sentence to the next

    Main features of Class Y documents * Longer and Well-Developed: They are generally much longer, consisting of fully fleshed-out, multi-sentence paragraphs. * Narrative and Descriptive: They are written in a continuous, narrative style that flows logically from one sentence to the next. * Comprehensive Context: They provide substantive background informati...

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    Topic Name. Text goes here

    What they have in common * Source and Tone: Both classes are clearly excerpts from an encyclopedia (specifically Wikipedia). They share a neutral, informative, and objective tone. * Formatting: Both classes follow the exact same formatting convention: they begin with the title or subject of the article, followed by a period, and then the text (e.g., *"Top...

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    Take a look at your syslog configuration,

    Main differences * Completeness: Class Y documents are complete, self-contained thoughts that explain a topic, whereas Class X documents are often incomplete fragments, list items, or isolated facts pulled out of a larger text. * Length: Class Y documents are consistently longer and denser in word count compared to the brief, stub-like nature of Class X. ...