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Retrieval Sensitivity to Identity Signals in Queries

Andrew Tang, Nicholas Deas, Kathleen McKeown, Vishal Misra

arXiv:2609.36534v1cs.CLcs.CYcs.IR

TL;DR

This paper asks whether political ideology and dialect expressed in queries affect what retrievers return, a question left open by evaluations that mostly use neutral queries. It evaluates paired synthetic and naturalistic queries in political news and consumer health; across retrievers, results favor the questioner’s political lean and WME over AAL health queries.

  • Problem

    Prior retrieval-bias studies mostly examine neutral queries, leaving open how identity signals in queries shape which documents are returned and through what channel.

    Source: §1 · p. 2

  • Method

    The authors evaluate political-news and health retrieval using controlled paired synthetic queries and naturalistic queries, then test lexical asymmetry and query embeddings for identity-related signals.

    Source: §1 · p. 2, §4 · p. 7

  • Results

    Across five dense retrievers and a sparse baseline, every retriever favors documents aligned with the query’s political lean and ranks answers lower for AAL than WME health queries.

    Source: §1 · p. 2

  • Takeaways & Limitations

    These retrieval shifts risk contributing to political polarization and health disparities faced by AAL speakers.

    Source: §6 · p. 9

    Synthetic queries may not faithfully represent real user language, and synthetic artifacts may inflate measured biases.

    Source: Limitations · p. 9

Abstract

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Dense retrievers decide which documents reach users and the language models that use them, yet they are typically evaluated with neutral queries. We ask whether the identity signals that real users express in their queries---political ideology and dialect---bias what a retriever returns. We design evaluations in two domains, political news and consumer-health questions, each pairing a controlled synthetic set that varies only the identity signal with naturalistic queries. Across five dense retrievers and a sparse baseline, every retriever (i) retrieves articles that align with the query's own political lean and (ii) performs worse for questions written in African American Language (AAL) than in White Mainstream English (WME). Two analyses tie these gaps to queries' identity signals beyond surface vocabulary: partialling out an aggregate lexical-asymmetry score leaves the synthetic gaps largely intact, and linear probes recover lean and dialect from the retrievers' query embeddings beyond token-level features. Left unaddressed, such retrieval biases risk contributing to polarization and reinforcing the health disparities already faced by AAL speakers. Code is available at https://github.com/Andrewtcr/bias-ret.

1 Introduction

This paper asks how political ideology and dialect expressed in queries affect which documents retrievers return. It evaluates political news and consumer-health retrieval, finding own-lean political retrieval and lower-quality results for AAL health queries.

1 Introduction

  • Neutral-query evaluations leave open how users’ political and sociodemographic signals in real queries shape retrieval.Similarity-based retrieval may be sensitive to such cues, but that alone does not predict which retrievers are affected, how much, or through which channel.Source: §1 · p. 2
  • The study examines political-lean signals in news queries and AAL versus WME in health queries.The authors connect political alignment with potential polarization and lower-quality health retrieval with allocational harm to AAL-speaking users.Source: §1 · p. 2
  • The evaluation pairs controlled synthetic queries that vary identity signals with unpaired naturalistic user queries across both domains.It tests five dense retrievers and a sparse baseline.Source: §1 · p. 2
  • Every evaluated retriever favors politically aligned documents and ranks answers lower for AAL than WME health queries; both gaps persist beyond surface vocabulary.The analyses partial out aggregate lexical asymmetry and probe query embeddings for lean and dialect beyond token-level features.Source: §1 · p. 2

2 Political Bias

Across paired synthetic and naturalistic political-query datasets, every retriever exhibits a significant own-lean tendency. The paired design controls topic and frame, and naturalistic results agree with it.

2.1 Data

  • The paired AllSidessynth queries share a topic and frame, while Redditnat supplies naturally occurring, unpaired queries.AllSidessynth pairs are generated from the same article and frame; Redditnat queries come from r/AskALiberal and r/AskConservatives and are retained when two classifiers agree on ideology.Source: §2.1 · p. 3

2.2 Methods

  • Political lean measures the difference between right- and left-leaning articles in a query’s top-k retrieval set; Redditnat lean is measured over relevant articles.AllSidessynth uses paired gold-filtered queries; Redditnat uses two judges to identify relevant retrieved articles, and the authors report that the gap is no smaller without relevance filtering.Source: §2.2 · pp. 3–4

2.3 Results

  • Every retriever shows significant own-lean tendency on both AllSidessynth and Redditnat, at retrieval depths k ∈ {5, 10, 20, 50, 100}.On AllSidessynth, BM25 and OpenAI have about 2× the gap of the four open-weight models; on Redditnat, BM25 and Nemotron have about 2× the gap of BGE, Qwen, and Octen.Source: §2.3 · p. 4, Figure 2 · p. 4
  • Agreement between the controlled and naturalistic results supports attributing the own-lean gap to query political identity rather than topic or frame.The dense-retriever gap does not require exact token matching; BM25’s pattern is affected by vocabulary overlap.Source: §2.3 · pp. 4–5

3 AAL vs. WME in Health Queries

The health-query evaluation compares retrieval for AAL and WME questions using paired synthetic rewrites and naturalistic queries. Every retriever ranks answers lower for AAL queries in both settings.

3.1 Data

  • HCMagicsynth pairs each WME question with three synthetic AAL rewrites, holding content fixed while varying dialectal features.The dataset starts from 4,999 WME queries and yields 14,997 AAL rewrites; HCMagicnat instead uses naturally occurring AAL queries and a similar number of real-WME queries.Source: §3.1 · p. 5

3.3 Results

  • Every retriever has higher MRR for WME than AAL queries on both datasets; the difference is significant for all six retrievers on HCMagicsynth and five on HCMagicnat.BGE is marginal on HCMagicnat (p=0.10), and the gap is stable across retrieval depth.Source: Figure 3 · p. 6, §3.3 · p. 6

3.2 Methods

  • MRR averages reciprocal rank of the gold doctor response across the full 112,165-document corpus, without gold-filtering.The synthetic comparison uses paired differences; the naturalistic comparison reports the between-group MRR difference.Source: §3.2 · p. 6

3.3 Results

  • The paired result attributes the direction of the synthetic gap to dialect, while the unpaired naturalistic comparison mixes dialect with content and topic difficulty.The authors say synthetic rewrite noise may make the paired estimate an overestimate; the naturalistic gap tends to be about 1.7× larger, and the disadvantage AAL users face may be larger still in real deployments.Source: §3.3 · p. 6

4 Bias gap analysis

The analysis separates lexical asymmetry from identity information in query embeddings, finding that synthetic retrieval gaps largely persist after lexical control. Naturalistic results vary by dataset, while probing shows identity signals remain recoverable beyond token-level features.

4.1 Method

  • The analysis tests lexical and non-lexical contributions by partialling out per-query lexical asymmetry and probing embeddings for identity information beyond token-level features.For paired synthetic data, the regression uses paired lexical-asymmetry differences; for unpaired data, it includes per-query asymmetry and a group indicator.Source: §4 · p. 7, §4.1 · p. 7
  • Lexical asymmetry sums group-distinguishing token scores within a query, and partialling it out uses the regression intercept as the gap not explained by that score.The slope measures how the gap moves with lexical asymmetry; a zero residual would mean that asymmetry explains the gap.Source: §4.1 · p. 7

4.2 Results

  • On both synthetic datasets, residual gaps stayed close to raw gaps and significant across retrievers after lexical-asymmetry control.Table 3 reports per-retriever raw and residual gaps; its significance stars test residuals against zero.Source: §4.2 · p. 8, Table 3 · p. 8
  • In naturalistic data, lexical control erased significance only for BGE and Qwen3 on Redditnat; HCMagicnat residuals remained but were confounded by patient-topic words.The HCMagicnat residual is not a clean non-lexical measure because its distinctive tokens reflect differences between patient query pools.Source: §4.2 · p. 8
  • Lean and dialect are linearly recoverable from dense-retriever query embeddings, with conditional probes finding added identity information at most layers.The reported probe F1 values are approximately 85% and 99%; positive conditional gains are absent for BGE on AllSides.Source: Figure 4 · p. 8, §4.2 · p. 9

5 Related Work

Related work examines identity signals in language models and bias in retrieval, but often uses neutral or scenario queries. This paper makes the query’s identity signal the variable of interest and measures shifts in retrieved documents.

5 Related Work

  • Prior work reports sociodemographic representation in encoders and language-model responses to AAL features and political cues.Related work also reports stereotyping of AAL speakers (Deas et al., 2025, 2024; Hofmann et al., 2024).Source: §5 · p. 9
  • Retrieval bias research covers spurious ranking features, social representation in retrieved sets, fair ranking, and bias propagation through RAG.A separate line treats misspellings and reformulations as query noise to address.Source: §5 · p. 9
  • Closest prior studies use neutral or scenario queries, or measure end-to-end accuracy; this paper instead measures how retrieved sets shift with the asker’s lean or dialect (Kim et al., 2025; Klisura et al., 2025).Source: §5 · p. 9

6 Conclusion

The paper concludes that retrievers respond to identity signals in queries, with political own-lean retrieval and lower answer rankings for AAL than WME health queries. It cautions that the evidence uses coarse identity proxies and synthetic contrasts, alongside unpaired naturalistic data.

6 Conclusion

  • Across paired synthetic and naturalistic settings, retrievers shift toward the questioner’s political lean and rank answers lower for AAL than WME queries; the authors identify polarization and health-disparity risks.The conclusion also reports that synthetic gaps survive lexical-asymmetry adjustment and that lean and dialect are recoverable from query embeddings beyond token-level features.Source: §6 · p. 9

Limitations

  • Identity is reduced to binary contrasts with imperfect proxies, while synthetic queries may not represent real language and health ranking does not assess retrieved documents’ medical usefulness or safety.Naturalistic comparisons are unpaired and confound identity with content and topic; neither design alone is decisive, so the authors rely on their agreement.Source: Limitations · pp. 9–10

Ethical considerations

  • The authors use synthetic AAL only as a controlled diagnostic, not an authentic sample, and caution that identity-sensitivity analyses could be misused to serve groups biased results.They also withhold Reddit post text and classifier rationales from the release to protect privacy.Source: Ethical considerations · p. 10

A Query dataset statistics

  • The four query datasets include paired synthetic and unpaired naturalistic sets; HCMagicsynth uses three AAL paraphrases per WME source, yielding 14,997 AAL queries.AllSidessynth and HCMagicsynth are paired; Redditnat and HCMagicnat are unpaired.Source: A Query dataset statistics · p. 14, Table 4 · p. 15

B Query-side pairing check

  • Same-article, same-frame L/R queries have higher cosine similarity than same-article, different-frame pairs, supporting tighter topical anchoring for the paired design.Median paired differences range from +0.075 to +0.139, with confidence intervals nowhere crossing zero, although individual pairs overlap.Source: B Query-side pairing check · p. 14, Figure 5 · p. 14

C Labeling Political Frames

  • The synthetic political queries use 15 issue-agnostic Boydstun frames; the pipeline extracts salient frames from articles and generates neutral, left-leaning, and right-leaning queries for each frame.The neutral query is generated but unused in the paper’s left/right contrasts.Source: C Labeling Political Frames · p. 15

D Reddit Data Collection

Redditnat ideology labels are assigned from post text while the evaluated question titles are withheld from classification. The study compares dense encoders and BM25, and uses a two-judge relevance gold for Redditnat retrievals.

D Reddit Data Collection

  • Two LLMs label Reddit post authors’ ideology from post text excluding the question; only posts where both agree enter the query dataset.The question title used in embedding evaluations is withheld from the classifiers to avoid potential data leakage.Source: D Reddit Data Collection · p. 15

E Model Details

  • The evaluation includes five dense retrievers and BM25, with embeddings L2-normalized so dense retrieval ranks by cosine similarity.Inputs are capped at 512 tokens; model-specific query/document conventions are used.Source: E Model Details · pp. 15–16, Table 5 · p. 16

F Redditnat LLM-judged gold-filter

  • For Redditnat, two independent judges grade top-10 query–article pairs, and a pair enters the primary gold set only when both judges mark it relevant.The judges agree at κ = 0.50 and 89.2% raw agreement; the both-judge filter retains 7,047 pairs, or 6.87% of the pool.Source: F Redditnat LLM-judged gold-filter · p. 17

G Gold-filtering robustness

The own-lean political retrieval gap appears under multiple relevance definitions and even without filtering. Its visibility in Redditnat’s full top-10 is diluted by off-topic results, while the relevant-document gap remains detectable.

G Gold-filtering robustness

  • Without relevance filtering, every AllSidessynth retriever and five of six Redditnat retrievers show an own-lean gap; filtering sharpens rather than creates the signal.BM25 is the Redditnat exception in the full top-10, while the relevance-filtered comparison detects its gap.Source: G Gold-filtering robustness · p. 17, Table 6 · p. 18, Figure 9 · p. 19
  • The own-lean gap persists across relevance definitions: it is significant on all six retrievers under the AllSidessynth LLM-judge gold, and remains under both that gold and editorial gold despite their low agreement.The LLM-judge gap is +0.06 to +0.09 versus +0.02 to +0.05 under editorial gold; agreement is κ = 0.17.Source: G Gold-filtering robustness · pp. 17–18, Figure 8 · p. 18, Figure 10 · p. 19
  • Applying a full-top-10 mean to Redditnat leaves only three of six retrievers significant; most articles are off-topic and carry no consistent lean, making the relevant-subset metric more appropriate there.Source: G Gold-filtering robustness · pp. 17–18

H Significance tests

  • Paired datasets use sign-flip permutation and paired Wilcoxon tests, whereas unpaired naturalistic datasets use a two-sided Mann–Whitney U test.The paired sign-flip test uses 50,000 resamples; the unpaired test compares whether one group’s per-query measure is stochastically larger.Source: H Significance tests · pp. 18–19

I Per-retriever gold-filtered own-lean tendency

Gold-filtered political comparisons show own-lean retrieval tendencies, with companion analyses reporting stability across retrieval cutoffs. The section also describes the health example, input filtering, dialect comparison setup, and translation k-sweep pattern.

I Per-retriever gold-filtered own-lean tendency

J Political k-sweep companions

K HealthCareMagic corpus example

  • The worked health examples score queries against real doctor responses; synthetic WME/AAL queries share one gold response, whereas naturalistic examples come from different patients with separate gold responses.The examples are ranked against a common 112,165-document corpus.Source: K HealthCareMagic corpus example · p. 20, Table 9 · p. 21

L HCMagic input filter and dropped rows

  • The HCMagic naturalistic input filter removes nine AAL rows, while the synthetic set drops one row with empty AAL paraphrases.The naturalistic paired contrast uses 647 pairs; HCMagicsynth uses 4,999 paired samples.Source: L HCMagic input filter and dropped rows · pp. 20–21

M Per-retriever dialect gap

  • The dialect contrast measures whether the WME-form query ranks its answer document higher than the AAL form; the reported BGE HCMagicnat comparison is the only one that does not clear significance.The contrasts use paired reciprocal-rank differences for HCMagicsynth and an unpaired between-group MRR gap for HCMagicnat.Source: M Per-retriever dialect gap · p. 21

N Dialect k-sweep companions

  • For GPT-translated WME versus original AAL queries, the two-camp split among retrievers persists across Recall@k cutoffs from 1 to 100.Qwen, Octen, and OpenAI te-3-large overlap, while BGE, Nemotron, and BM25 separate at every swept k.Source: N Dialect k-sweep companions · p. 22

O HCMagicsynth augmentation quality

The synthetic AAL paraphrases show substantial surface changes, but the augmentation pipelines were validated on other corpora rather than independently on HealthCareMagic, and some artifacts may inflate the measured gap.

O HCMagicsynth augmentation quality

  • Both paraphrase pipelines were validated with AAL-speaker reviewers on other source corpora, but not independently revalidated on HealthCareMagic outputs.The authors report quantitative and qualitative spot-checks of these outputs.Source: O HCMagicsynth augmentation quality · p. 22
  • Median AAL/WME length ratio was 1.06, median character-level edit ratio was 0.58, and only 19/14,997 paraphrases had edit ratios below 0.05.Just 2/14,997 paraphrases were identical to their WME source.Source: O HCMagicsynth augmentation quality · p. 22
  • Samples showed recognizable AAL features alongside recurring generator artifacts, including pronoun drift, substitutions, cleft structures, and occasional sub-word garbling.The spot-check identifies features such as “y’all” and “might can,” as well as artifacts such as “she” replacing first-person “I.”Source: O HCMagicsynth augmentation quality · p. 22
  • The authors say artifacts likely inflate the synthetic HCMagicsynth gap; a translation contrast bounds the natural-AAL penalty for strong encoders at ±0.015 MRR.This bound is reported for the strong encoders, not as a general estimate for every retriever.Source: O HCMagicsynth augmentation quality · p. 23

P Translation-mitigation experiment

The authors test whether back-translating natural AAL health queries into WME improves retrieval. It helps some retrievers, but does not close the disparity and is not a clean dialect contrast.

P Translation-mitigation experiment

  • The experiment back-translates 647 natural AAL queries to WME with gpt-5-mini and compares retrieval of each query’s shared gold document using paired tests.The paired contrast is ∆rank = RRtrans-WME − RRAAL.Source: P Translation-mitigation experiment · p. 23
  • Because translation may add or drop medical content, the authors treat it as a practical intervention rather than a clean dialect contrast.They also describe the test as stringent given prior findings that LLMs struggle to rewrite and interpret AAL.Source: P Translation-mitigation experiment · p. 23
  • Back-translation measurably improves BGE-large, Llama-Nemotron-8B, and BM25, but not Qwen3-Emb-8B, Octen-Emb-8B, or OpenAI te-3-large.The latter three have |∆rank| ≤0.005 with all p ≥0.22; apart from BGE, which had no significant gap initially, no retriever closes the gap.Source: P Translation-mitigation experiment · p. 23

Q Bias-gap method details

These details specify how lexical asymmetry is measured and how uncertainty is handled. The accompanying translation comparison finds improvement only for some retrievers.

Q Bias-gap method details

  • The lexq channel uses lowercase alphanumeric regex tokenization, keeps apostrophe suffixes attached, drops punctuation, and avoids any retriever’s model tokenizer.This defines the lexical measure on raw query text.Source: Q Bias-gap method details · p. 23
  • The regressions use HC1 heteroskedasticity-consistent robust standard errors with cluster adjustment for paired synthetic datasets; the qualitative pattern also holds under HC0–HC3.Unpaired Redditnat and HCMagicnat data have no within-cluster structure.Source: Q Bias-gap method details · pp. 23–24
  • Back-translation improves BGE, Llama-Nemotron, and BM25 significantly, while Qwen, Octen, and OpenAI te-3-large show confidence intervals spanning zero; no gap closes fully.This is the paired k=10 comparison of natural AAL queries translated to WME.Source: Table 13 · p. 24

R Lexical-residual regressions

Lexical-residual regressions leave substantial identity-associated gaps in several settings, though the residual varies by dataset and retriever. The probing details specify how hidden representations are extracted and tested.

R Lexical-residual regressions

  • On paired AllSidessynth queries, only OpenAI te-3-large has a measurable lexical slope; after partialling, its residual lies within the open-weight residual band.BM25’s residual magnitude exceeds every open-weight residual.Source: R Lexical-residual regressions · pp. 24–25, Table 14 · p. 26
  • On Redditnat, all five dense retrievers have positive significant lexical slopes; BGE’s residual gap collapses, while Llama, Octen, and OpenAI retain significant non-lexical components and Qwen is marginal.This regression uses unfiltered top-k retrieval at k = 20; a gold-filtered robustness check finds residuals for Llama-Nemotron and OpenAI, but not BGE or Qwen, with Octen positive but not significant.Source: R Lexical-residual regressions · p. 25, Table 15 · p. 26, Table 16 · p. 27
  • On HCMagicnat, the WME–AAL residual gap remains detectable for five of six retrievers after lexical partialling, while no retriever has a detectable within-group lexical slope.BGE is marginal at p = 0.21; the authors caution that lexical partialling does not remove content-difficulty differences.Source: R Lexical-residual regressions · p. 26, Table 18 · p. 27
  • The HCMagicnat residual is defined as the WME–AAL gap after partialling out per-query vocabulary asymmetry, but that adjustment does not control for content difficulty.AAL-distinctive tokens can include content words such as “she,” “her,” “periods,” and “baby.”Source: R Lexical-residual regressions · p. 26

S Probing Details

  • For encoder-only models, probing uses mean-pooled outputs from each layer with standard scaling; OpenAI te-3-large is excluded because its API exposes no intermediate layers.Linear logistic-regression probes use default sklearn hyperparameters and a maximum of 5,000 iterations.Source: S Probing Details · p. 26

R Lexical-residual regressions

  • On HCMagicsynth, lexical-slope estimates are positive but not statistically distinguishable from zero, and the implied lexical contribution is at most 0.005 MRR.Residual-gap rankings closely match raw-gap rankings, with only two micro-swaps within noise.Source: Table 17 · p. 27

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