FindMyText: Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora

Lars Henry Berge Olsen; Mark Anderson; Martin Jullum; Pierre Lison

REVIEW 4 major objections 4 minor 299 references

FindMyText: Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora

T0 review · 4 major / 4 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read FindMyText claims that grouping position-offset-aligned fingerprint matches into chains detects text containment at ~0.99 AUC-ROC across Wikipedia, ArXiv, and a 50.7M-document web crawl, at ~450 ms per query.

desk verdict The chain-clustering score is a clean, working idea that beats its baselines on its own benchmark, but the benchmark never gives a positive a gap inside the copied fragment—so the robustness claims for real crawled corpora are overreaching. read the letter →

arxiv 2607.10020 v2 pith:M3S3TIBB submitted 2026-07-10 cs.CL cs.AI

Lars Henry Berge Olsen , Pierre Lison , Martin Jullum , Mark Anderson This is my paper · ORCID

classification cs.CLcs.AI

keywords textcontainmentdocumentfingerprintingwinnowingnear-duplicatedetectioninvertedindexpositionalclusteringcopyrightweb-scalesearch

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents FindMyText, a method to decide whether a query text appears—in whole or in part—inside a large text corpus, a task that matters for verifying whether copyrighted material made it into an LLM's training data. The central claim is that this 'text containment' can be detected far more reliably by looking for chains of matching fingerprints than by counting shared fingerprints or comparing embeddings. Each shared fingerprint is placed at a point (position in the query, offset relative to the corpus document); connected clusters of such points indicate a contiguous reused passage, and the size of the largest cluster is the containment score. On a synthetic benchmark built with a Smith-Waterman oracle, this chain score achieves AUC-ROC around 0.99 on Wikipedia, ArXiv, and a 50.7-million-document web crawl, while semantic-similarity baselines perform near chance. The method runs in about 450 ms per query on the largest corpus using a disk-based inverted index.

What carries the argument

The central mechanism is the fingerprint-chain score s_chain(q,d_i)=max_k |C_k|, built from winnowed, position-stamped hashes. Winnowing selects the minimum hash in each sliding window, guaranteeing that any shared substring of length k+w−1 yields a common fingerprint. The novelty is clustering shared fingerprints in (query position, offset) space with tolerance thresholds (τ_pos, τ_off), which converts a set of independent coincidental hashes into evidence of a contiguous, aligned copy.

What would settle it

Create a synthetic or real example where a query shares two consecutive copied blocks with an inserted 100-character paragraph between them (so Smith-Waterman still scores above the positive threshold but no single fingerprint chain connects the blocks). If FindMyText's chain score for this pair falls below its positive threshold, the method's robustness claim is contradicted.

Watch

Extended reading notes

Core claim

The core discovery is that an explicit, geometrically coherent sequence of shared fingerprints—rather than a raw count—separates true near-verbatim reuse from mere topical similarity. For a query q and a corpus document d_i, FindMyText winnows both texts into k-gram hashes, records character-level positions, and for each shared hash h computes the offset δ(h) = p_{d_i}(h) − p_q(h). Shared hashes are plotted as points (p_q(h), δ(h)); a reused passage appears as a nearly horizontal run of points. Two points are connected if their query positions differ by at most τ_pos = 30 characters and their offsets differ by at most τ_off = 10 characters; clusters of at least κ = 5 fingerprints are kept, a

Load-bearing premise

The reported accuracy transfers to real corpora only if the synthetic benchmark's edit menu and Smith-Waterman thresholds faithfully represent the noise actually introduced by web crawling and cleaning.

Editorial extensions

If this is right

If the chain score is right, near-verbatim reuse can be detected at web scale with a disk-based inverted index: ~450 ms per query on 50.7M documents.
The method distinguishes containment from similarity: BM25 and dense retrieval score near chance on the benchmark, while the chain method stays above 0.98 AUC-ROC.
Robustness covers common cleaning edits—case changes, hyphenation, ligatures, boilerplate insertion—but not paraphrase or large reorderings.
The approach yields localizable evidence (which passages match), which is useful for copyright-compliance audits of training corpora.
The synthetic benchmark itself is a contribution: a reproducible recipe for evaluating containment methods with Smith-Waterman as oracle.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The (position, offset) clustering is essentially a Hough-transform–style vote for a linear alignment; a natural extension is to return multiple disjoint chains and their boundaries, giving a full containment map rather than a single max score.
The tight tolerances (τ_pos=30, τ_off=10 characters) suggest the method is tuned for clean text; OCR-laden or heavily segmented web content with larger local insertions might need adaptive thresholds or multi-scale winnowing—a testable hypothesis.
Because negatives in the benchmark are constructed adversarially (permute, paraphrase, boilerplate), the reported AUC may understate real-world ease if actual non-contained documents are less similar; conversely, if real copies undergo heavier edits, performance could degrade—this asymmetry is worth probing.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear X,

Quick take: this is a solid engineering paper with a genuinely useful scoring function. The chain detection—grouping shared winnowing fingerprints in (query position, offset) space with thresholds τ_pos=30 and τ_off=10, then taking the largest cluster size—is a neat, well-specified mechanism that clearly separates the synthetic positives from negatives (AUC ≈ 0.99 across Wikipedia, ArXiv, and 50M HPLT docs). That result is real for the benchmark as constructed, and the SW-oracle benchmark itself is a reasonable, disclosed way to generate hard positives and negatives. The open-source release and the scale demonstration (~450 ms per query on 50M documents) are concrete assets.

The soft spots are about generalization, not about the core method being broken. The main one: the positive instances in the benchmark are all contiguous fragments that have gone through light character-level edits (case, hyphenation, ligatures) and then been concatenated with filler. No positive ever has an insertion or deletion inside the copied span. Because the chain tolerance is 30 characters of position gap and 10 characters of offset drift, a single 100-character insertion, a deleted paragraph, or a layout-induced reordering will split the chain and can push the largest cluster below κ=5. The paper explicitly motivates OCR, boilerplate removal, and segmentation as the kinds of noise it is built for, but the benchmark never exercises those. The stress-test note is right: the headline numbers do not transfer to that noise distribution.

Two smaller issues. The dense-retrieval baseline reports an AUC of 0.000 on Wikipedia—a perfect anti-correlation. That went unexplained in the paper, and it either signals a bug or makes the comparison look worse than it is. The authors should fix or explain it. Also, the hyperparameters are tuned on a validation set generated by the same procedure as the test set, and the evaluation uses a single benchmark draw; "the methods are deterministic" doesn't justify omitting error bars over the random generation. This is minor for an engineering paper, but it does cap the strength of the quantitative claims.

The citation gap on plagiarism/text-reuse work with positional fingerprint filtering is real; the 'novel mechanism' framing overshoots. The chain clustering is still a clean addition.

Bottom line: the paper is worth a serious referee. It solves a real problem—efficient near-verbatim containment detection for training-dataset audits—and it does what it claims on its own benchmark. The revisions should add a stress test with internal gaps and a real-corpus validation (even a small one with hand-labeled positives), and clean up the baseline anomaly. It is not a paradigm shift, but it is a competent contribution that belongs in the conversation.

Best, [You]

Referee Report

4 major / 4 minor

Summary. The paper presents FindMyText, an open-source Python toolkit for detecting whether a query text appears wholly or partially in a corpus. The method extracts winnowed character-level fingerprints, indexes them in a disk-based inverted index, and scores each candidate document by the size of the largest position-coherent 'chain' of shared fingerprints (Eq. 2). This chain score is compared with shared-fingerprint counts, BM25, and dense retrieval on a synthetic benchmark built from Wikipedia, ArXiv, and the HPLT web corpus. The central claim is that the chain-based method discriminates positive from negative instances with AUC-ROC around 0.99 and Precision@Recall=90% above 0.98 on all three corpora, while baselines fail (Table 1). The authors also provide a web interface and release the toolkit under an MIT license.

Significance. If the reported results transfer to real-world settings, FindMyText would be a practically valuable tool for copyright-compliance screening and training-data provenance analysis, because it scales to tens of millions of documents with sub-second queries and explicitly identifies contiguous reused passages rather than merely scoring overall similarity. The paper ships an open-source implementation and a reproducible synthetic benchmark, and the core chain-clustering idea is a natural, well-motivated extension of winnowing. The main limitation is that the evaluation's ground truth is generated by a synthetic procedure whose positive instances are, by construction, a single lightly edited contiguous fragment. Therefore the headline numbers demonstrate that the method detects exactly the kind of near-verbatim segment the benchmark instantiates, but they do not yet establish robustness to the full range of preprocessing transformations that the paper motivates in Sec. 1 (OCR noise, boilerplate insertion/removal, segmentation, and reordering).

major comments (4)

[§5.1, Eq. (2)] The benchmark's positive-instance construction is the load-bearing element for the headline AUC≈0.99, and it excludes exactly the chain-breaking cases that matter for real corpora. A positive is created by taking one consecutive fragment, applying small character-level edits until the Smith-Waterman score exceeds T_pos=1000, and concatenating unrelated filler around it. Because the chain score connects fingerprints only when gaps are ≤ τ_pos=30 characters and the offset drift is ≤ τ_off=10 characters, a single inserted paragraph, a deleted sentence block, or an OCR-induced gap inside the copied segment will split the chain and can push the largest cluster below κ=5. The benchmark contains no positive instance with such discontinuities, so the reported P@R=90% of 0.998–1.00 does not demonstrate robustness to the very transformations (boilerplate removal, document segmentation, cleaning) d
[Table 1, Dense Retrieval row] An AUC-ROC of exactly 0.000 for dense retrieval on Wikipedia is a degenerate result that cannot support the paper's conclusion that the baselines 'are unable to disentangle' positives and negatives. AUC=0 means every positive is ranked below every negative, which is not a typical failure mode of a document-embedding retriever; it indicates that the baseline is being applied in a way that is systematically inverted for this benchmark (e.g., using whole-document similarity when the benchmark negatives are topic-matched near-paraphrases and the positives are a short copied span embedded in unrelated filler). Since the dense-retriever results are unavailable for ArXiv and HPLT, the comparison is also incomplete. The authors should either configure the dense baseline to operate on passage-level or windowed embeddings with a proper thresholding/ranking protocol, or explicitly state that the ba
[§5.2, §3.2, Table 1] The chain parameters τ_pos=30, τ_off=10, and κ=5 are calibrated on a validation set generated by the same synthetic procedure used for the test set, and no sensitivity analysis is reported. Because the positive examples are constructed to be nearly contiguous, the chain score is essentially measuring whether the input has a long unbroken run of shared fingerprints; the benchmark does not provide evidence about how the method degrades when τ_pos or κ are mismatched to the actual noise level. In particular, the paper does not report a null model or empirical distribution of chain scores for unrelated documents, so it is unclear how the method avoids false positives in a corpus with, say, reused boilerplate or common long n-grams. I ask the authors to add (i) a sweep over τ_pos, τ_off, and κ on the validation set, (ii) a false-positive analysis on random non-overlapping documents from the s
[§4, §5.4] The scalability claim is supported only by query latency (≈450 ms for 50.7M documents). Index construction time, disk footprint, memory-mapped index size, and query throughput under concurrency are not reported. Since the paper's stated contribution includes 'scaling to large web-crawled datasets,' the lack of index-construction measurements makes it difficult for a reader to judge whether the approach is practical at the intended scale. Please report wall-clock index building time for each corpus, index size on disk, and, if possible, the peak memory usage.

minor comments (4)

[§1, Abstract] Several typos and spacing issues: 'FindMyTextextends' (missing space), 'thiry-fourth' in the Broder reference, and inconsistent use of 'd′i' vs 'd_i'. A light proofreading pass is recommended.
[Table 1, note] The dense retrieval baseline is listed as '/' for ArXiv and HPLT. If the authors retain the baseline, they should either compute embeddings for those corpora or explicitly mark the baseline as not evaluated, rather than leaving an ambiguous slash.
[Appendix C] The benchmark examples are useful, but the 'positive' example appears to contain a very long insertion of garden-blog content before the copied Simpsons paragraph. This is actually a good stress case, yet it is not representative of the automated generation described in §5.1. Clarify whether such long prepended unrelated spans are part of the standard positive generation or a hand-constructed illustrative example, and whether the chain method still detects the copied span when the unrelated content is longer than the copied span.
[Appendix A] Embedding a live password ('EMNLP2026') in the paper is not standard practice; if the web interface is meant to be publicly accessible for review, provide an anonymous/unauthenticated link or a clearly temporary password. Also, the GitHub URL is given but no repository DOI or version number is provided; a versioned release would improve reproducibility.

Circularity Check

1 steps flagged · score 6.0 of 10

Benchmark positives are generated as consecutive, lightly edited fragments and negatives by operations that break chains; the chain score (Eq. 2) is defined to detect exactly that structure, so the 0.99 AUC is largely a construction match rather than an independent validation.

fitted input called prediction [Sec. 3.2 (Eq. 2), Sec. 5.1 (Positive/Negative instances), Sec. 5.2 (Hyper-parameters), Sec. 5.4 (Table 1)]
"we first sample a document d_i from C and then select a consecutive fragment d′_i ⊂ d_i ... Those edits are repeated until we obtain a fragment d′_i that is right above a given threshold T_pos. Finally, the fragment d′_i is concatenated with some other text content ... The pair (d′′_i, d_i) is a positive example as the two documents share by construction a common text region according to the oracle ... we consider two shared fingerprints h_i and h_j as connected if |p_q(h_i)−p_q(h_j)| ≤ τ_pos and |δ(h_i)−δ(h_j)| ≤ τ_off ... Hyper-parameters – such as the τ_pos and τ_off thresholds of the chain"

The benchmark's positive instances are by construction a single consecutive fragment of d_i, edited only by normalizations and then concatenated with filler; the chain score s_chain(q,d_i)=max_k|C_k| connects shared fingerprints with query-position gaps ≤ τ_pos=30 and offset drift ≤ τ_off=10, which is exactly the structure such consecutive, lightly edited fragments produce. Negatives are generated by permutations, paraphrase, and boilerplate insertion — operations designed to destroy long consecutive shared spans, which is precisely what the chain criterion measures. Thus the near-perfect AUC-ROC/P@R in Table 1 is a match between Eq. (2) and the label-generation process, not an independent test of whether chain score recovers Smith-Waterman-defined containment. Since τ_pos and τ_off were t

full rationale

No self-citation chain, imported uniqueness, or ansatz-via-citation is present: the winnowing backbone is grounded in Schleimer et al. (2003), and the method is self-contained. However, the central experimental claim — that the chain-based method 'discriminates much more effectively between positive and negative examples, achieving an AUC-ROC around 0.99' — is supported only by a benchmark whose labels were generated to have exactly the contiguous, small-offset shared-span property that Eq. (2) detects. Positives are constructed by selecting a consecutive fragment and editing it only until the Smith-Waterman oracle barely exceeds T_pos=1000, so the shared region is guaranteed to form a long fingerprint chain; negatives are constructed by operations that break such chains. The thresholds τ_pos/τ_off are then tuned on a validation set from the same generator. The performance numbers therefore reduce partly to the benchmark construction: they demonstrate that the implementation finds fingerprint chains when the benchmark has placed a chain in every positive and removed chains from every negative, rather than independently establishing robustness to arbitrary SW-positive containment. This is a partial, evaluation-level circularity — the derived score and the label definition are not identical, but the generative process makes them nearly equivalent by construction. The scaling, indexing, and runtime results are independent and not affected by this circularity.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The paper's own contribution rests on winnowing's exact-match guarantee from Schleimer et al. 2003 (adopted, not re-derived) plus seven hand-set or empirically calibrated parameters (k, w, τ_pos, τ_off, κ, T_pos, T_neg). There is no null model for random fingerprint coincidences: κ=5 and the tolerance windows are justified only by validation-set calibration. No invented entities are introduced — chains are derived objects. The free-parameter count is the honest measure of what the benchmark evaluation required the authors to tune by hand.

free parameters (8)

k (shingle length) = 4 words
Hand-chosen in Sec. 4; sets the minimum granularity of detectable shared fragments; not swept.
w (winnowing window) = 6 hashes
Hand-chosen in Sec. 4; controls fingerprint density together with k.
τ_pos (maximum gap between connected fingerprints) = 30 characters
Calibrated empirically on a validation set synthesized per Sec. 5.1 (Sec. 5.2); directly controls chain connectivity.
τ_off (maximum offset drift) = 10 characters
Calibrated empirically on the same validation set; bounds robustness to local insertions/deletions.
κ (minimum cluster size) = 5 fingerprints
Hand-set in Sec. 3.2; filters spurious chains; no null model justifies the value.
T_pos (positive-instance SW-score threshold) = 1000
Arbitrary benchmark threshold (Sec. 5.1); the 'half a page' equivalence is asserted, not measured.
T_neg (negative-instance SW-score threshold) = 100
Arbitrary benchmark threshold (Sec. 5.1); defines absence of any shared segment longer than 1-2 sentences.
LLM paraphrase settings for negative generation = Qwen2-7B-Instruct, ≈400 char chunks, temperature 0.8
Generation-time choice (Sec. 5.1) that controls how hard the negatives are; stochastic.

assumptions (5)

standard math Winnowing guarantee: any shared substring of length ≥ k+w−1 yields at least one common fingerprint (Schleimer et al., 2003)
Adopted from prior literature in Sec. 3.1; not re-derived; the whole method's recall floor depends on it.
domain assumption Word-level k-gram tokenization that ignores punctuation preserves shingle identity under the benchmark's edit operations
Sec. 4 sets k=4 'ignoring punctuation'; the ligature and de-hyphenation edits in Appendix C change characters inside words, which only survives because hashing is on word shingles.
domain assumption Smith-Waterman local alignment score with thresholds T_pos=1000 / T_neg=100 is a valid oracle for 'shared text segment' containment
Sec. 5.1 defines benchmark labels via this oracle; no calibration or independent validation of the 'half a page' equivalence is given.
standard math Fingerprint hash collisions are negligible at the scales used
Unstated in Sec. 3.1; standard for 64-bit hashing but relied on for correctness of the inverted index.
ad hoc to paper A connected cluster of ≥κ=5 fingerprints within τ_pos=30 / τ_off=10 is evidence of reuse, with no null model for chance chains
Sec. 3.2 sets these thresholds without a random-coincidence model, so false-positive behaviour on varied corpora is unquantified.

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Cite this review

Pith. "Pith review of FindMyText: Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora." pith.science (2026). https://pith.science/paper/M3S3TIBB

@misc{pith2026260710020,
  author       = {Pith},
  title        = {Pith review of: FindMyText: Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M3S3TIBB}},
  note         = {Machine review of arXiv:2607.10020}
}

read the original abstract

We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus. The tool builds on prior techniques for document fingerprinting, but extends them with a novel mechanism to explicitly capture sequences of matching fingerprints. By identifying such chains, the tool can more reliably detect near-verbatim copies of a given text rather than mere textual similarities. This makes FindMyText particularly suited for verifying the presence of copyrighted material in a corpus. Leveraging a distributed, disk-based indexing framework, the system scales to large web-crawled datasets. Using a new benchmark for evaluating text containment methods, we show that FindMyText outperforms alternative approaches across three datasets (ArXiv papers, Wikipedia, and generic web content).

Figures

Figures reproduced from arXiv: 2607.10020 by the authors.

**Figure 1.** General sketch of the FindMyText approach. The reliability of those attacks have, however, been called into question (Meeus et al., 2024; Zhang et al., 2024a; Liu et al., 2025), particularly for production LLMs that are carefully designed to avoid generating copyrighted content. Transparency obligations introduced by recent regulations such as the EU AI Act (European Parliament and Council, 2024) may in the future… view at source ↗

**Figure 2.** Illustration of the method for identifying fin [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** FindMyText web interface. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

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Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.

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