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This paper claims that standard embedding-based similarity metrics systematically misclassify semantic opposition, that cosine distance—not the learned vectors—causes much of the code-domain failure, and that LLM judges discriminate semanti

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2026-08-04 23:28 UTC pith:7CU7ZXWN

load-bearing objection The main claim about Euclidean distance beating cosine on code embeddings is mathematically impossible under the paper's own Table 3, and the category taxonomy is internally contradictory; the benchmark idea is useful but the results as presented are not interpretable. the 5 major comments →

arxiv 2509.09714 v1 pith:7CU7ZXWN submitted 2025-09-08 cs.CL cs.AI

How Small Transformation Expose the Weakness of Semantic Similarity Measures

Serge Lionel Nikiema , Alb\'erick Euraste Djire , Abdoul Aziz Bonkoungou , Micheline B\'en\'edicte Moumoula , Jordan Samhi , Abdoul Kader Kabore , Jacques Klein , Tegawend\'e F. Bissyande This is my paper
classification cs.CL cs.AI
keywords semantic similaritycode clone detectionembedding metricscosine similarityEuclidean distanceLLM-as-a-judgesemantic oppositioncontrolled transformation benchmark
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper sets out to show that when embedding-based similarity metrics go wrong, the failure often sits in the distance function, not in the learned vector representation. It builds a controlled benchmark of 5,068 text and code pairs organised into explicit semantic relationships—equivalent, negated, antonymous, unrelated, cross-language—and runs 18 metrics across them. The headline results are that cosine-based embeddings systematically treat semantic opposites and unrelated content as highly similar: CodeBERT reaches a 99.9% false-positive rate and BERTScore rates opposites above synonyms, while switching to Euclidean distance on identical code embeddings improves difference detection by 24-66%, and LLM judges score genuinely different content low (0.00-0.29) where embeddings score it high (0.82-0.99). If these results hold, many software engineering tools that rely on cosine similarity over code embeddings can be made materially more reliable without retraining a model.

Core claim

On the paper's own terms, the central discovery is a systematic, transformation-based diagnosis of where similarity metrics fail. The authors find three regularities. First, embedding models trained on code and text are largely semantic-difference blind: CodeBERT assigns a similarity of 1.00 to code pairs whose meaning has been altered, BERTScore assigns about 0.95 to negated, antonymous, shuffled, and unrelated sentences, and transformer embeddings average 96.2% false positives across difference categories. Second, a large share of the code-domain failure is a measurement artefact: replacing cosine similarity with Euclidean distance on identical CodeBERT vectors cuts the semantic-difference

What carries the argument

The load-bearing device is a controlled transformation protocol: every evaluation pair is generated by a known edit—synonym or antonym substitution, negation insertion, word reordering, translation, semantic-preserving or semantic-altering code mutation, cross-language port—so the true semantic relationship is fixed by construction rather than inferred. That allows the authors to compute false-positive rates per category and to isolate representation quality from distance computation by holding embeddings fixed and changing only the distance metric (cosine, Euclidean, dot product, Jaccard, Pearson, angular). The Euclidean-versus-cosine comparison is the mechanism that produces the paper's ma

Load-bearing premise

The load-bearing premise is that every subset really instantiates the semantic relationship its name says (equivalent, negated, antonymous, unrelated, cross-language), since all false-positive rates and improvement percentages are computed against those labels.

What would settle it

Recompute the central metrics after re-labelling the text subsets with one consistent mapping (the paper currently gives two different definitions for S4 and reads S2/S3 differently in results), and publish the per-pair labels. If BERTScore's ~0.95 false-positive rates on negated and antonymous pairs drop materially, or the 0.82-0.99 versus 0.00-0.29 gap narrows, the semantic-blindness claim fails; if the numbers hold, it is robust.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Existing code-similarity and clone-detection tools that score CodeBERT or GraphCodeBERT with cosine similarity can improve semantic-difference detection by 24-72% simply by using Euclidean distance on the same vectors, with no retraining.
  • High cosine scores should not be read as evidence of semantic equivalence: for embedding models, scores of 0.82-0.99 are routinely assigned to negated, antonymous, shuffled, and unrelated texts, so threshold-based tools will inherit false positives.
  • LLM-based similarity judges, especially with simple prompts, give a credible low range (0.00-0.29) for genuinely different content, so they offer a materially different regime from embedding metrics for semantic-difference detection.
  • Prompting strategy matters for LLM judges: simple prompts maximise difference detection (average 0.19-0.23), while few-shot prompts maximise consistency (0.84-0.89); temperature in 0.0-1.0 has little effect.
  • Lexical and n-gram metrics such as BLEU and TF-IDF are unreliable for opposition because high token overlap makes negated text look similar; they are safer for syntactic variation than for semantic judgment.

Where Pith is reading between the lines

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

  • Editorial inference: the paper measures pairwise similarity thresholds, so the 24-66% Euclidean gain may not transfer automatically to ranking workloads such as code search; a testable extension is to compare cosine versus Euclidean embeddings on retrieval metrics such as recall@k and mean reciprocal rank.
  • Editorial inference: because the paper's own text taxonomy contains inconsistent definitions (S4 is described both as synonym substitution and as antonym substitution, and the discussion reads Table 2 under different category names), the most valuable next artifact would be a release of all labelled pairs under one canonical mapping; the headline percentages should be read as conditional on those
  • Editorial inference: LLM judges' moderate scores (0.62-0.78) on minimal-edit semantic changes could be used as a calibrated uncertainty signal—systems could route middle-band pairs to human review instead of thresholding everything—though the paper does not itself propose this.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper proposes a diagnostic framework for evaluating semantic similarity measures by applying controlled transformations to natural language and code and testing 18 metrics. The main claims are that embedding-based metrics are 'semantically blind' (e.g., CodeBERT 99.9% false positives, BERTScore rating opposites above synonyms), that changing from cosine to Euclidean distance improves code embedding performance by 24-66% (37-72% for GraphCodeBERT), that LLM-based judges are far better at detecting semantic differences, and that simple prompts and low temperatures give stable LLM evaluations. The framework includes five code and six/seven text semantic categories, automated test-case validation for code, and human annotation for text.

Significance. The intended contribution---a benchmark that isolates failure modes of similarity metrics---is timely and, if the results were sound, would be practically useful for code search, clone detection, and LLM-based evaluation. The paper has credible strengths: it evaluates a wide range of metrics, applies automated test-based validation for code transformations, and reports human annotation for natural-language pairs. However, the load-bearing claims are currently undermined by (i) contradictory category definitions and inconsistent use of category labels in the analysis, and (ii) a distance-metric claim that is mathematically inconsistent with the reported numbers. The 'distance metric breakthrough' and the quantitative false-positive/false-negative claims cannot be accepted as reported.

major comments (5)
  1. [Section 3.2.2 / Section 4.1] The natural-language taxonomy is internally inconsistent and the analysis applies different labels from the definitions. Section 3.2.2 states six subsets but concludes 'seven well-defined semantic subsets'; S4 is defined twice, once as T_synonym and once as T_antonym; and Section 4.1 assigns S2=negation, S3=antonym, S4=shuffle, S6=unrelated, while the formal definitions assign S2=unrelated, S3=negation, S4=antonym, S6=translation. Because Table 2 and Finding 1 are computed under these labels, every text-domain score, accuracy, and false-positive rate reported for these categories is uninterpretable until the mapping is fixed and the evaluation rerun.
  2. [Section 4.2 / Table 3] The headline Euclidean-distance improvement is inconsistent with the table's own numbers. Dot Product and Cosine Similarity are identical for every row (e.g., CodeBERT S2: 0.999/0.999; S3: 0.966/0.966). This identity holds only for L2-normalized embeddings, for which ||u-v||^2 = 2 - 2cos(u,v), so Euclidean distance is a strictly decreasing function of cosine similarity. Any evaluation invariant to monotone score transformations---correlation, AUC, rank-based metrics, or threshold-calibrated classification---must therefore give identical discrimination for the two. The reported 24-66% 'improvement' can only be a scale/threshold artifact. This claim is the basis of the abstract, Finding 2, and RQ-2, so the central result is unsupported as stated.
  3. [Section 4.1 / Finding 1] The paper reports false-positive rates and F1 scores without defining the decision procedure. Table 1 reports F1 and the text reports '99.9% false positive rates' for CodeBERT and '96.2%±1.8%' for transformer embeddings, but no threshold, classifier, or ROC/AUC analysis is specified anywhere in Section 3.4 or 4.1. A high similarity score alone does not constitute a false positive; a threshold or ranking evaluation is required. The headline numbers therefore lack the methodological support needed to sustain the 'catastrophic failure' claim.
  4. [Equation (1) / Tables 3-4] The evaluation metric defined in Eq. (1) is a correlation with the gold standard, yet Tables 3 and 4 report raw similarity scores and percentage 'improvements' relative to cosine. No correlation values, confidence intervals, or threshold-calibration details are given. It is thus unclear whether the reported 'improvement' is in correlation, in some thresholded accuracy, or in raw score magnitude; these are very different claims. The manuscript should report the actual evaluation measure used for each claim.
  5. [Section 3.2.1 / Section 4.1 (code categories)] The code subset S2 is defined as pairs differing by semantic-altering transformations (operator and control-flow mutations), but Section 4.1 repeatedly calls this 'semantic opposition' and treats it as an opposition-detection task. These are not the same phenomenon: altered behavior is not the same as logical opposition. The interpretation of Table 1's S2 column, and the 'opposition' language in Finding 1, rest on this conflation and need to be either justified or corrected.
minor comments (6)
  1. [Abstract] The abstract's phrasing of the main distance-metric result is ambiguous/contradictory: it first says 'switching from Euclidean distance to cosine similarity improved results by 24 to 66 percent,' while the full abstract and Section 1 say Euclidean improves over cosine. Please correct the direction.
  2. [Section 3.2.2] Five transformations are listed (synonym, negation, antonym, reordering, translation), but the text says 'After applying all four transformations.'
  3. [Section 4.2] The text says 'seven distinct distance metrics' but enumerates six: Cosine, Euclidean, Dot Product, Jaccard, Pearson, Angular.
  4. [Section 4.4] The fourth experiment's finding is numbered 'Finding 3', duplicating the numbering in Section 4.3; the fourth finding should be numbered 4.
  5. [Tables 2 and 4] Table 4's headers S2(Antonym), S3(Negation), S4(Shuffle) use the same mislabeled mapping as Section 4.1 and contradict the definitions in Section 3.2.2.
  6. [References] Some references are duplicated (e.g., [5] and [6] are the same Cha 2007 survey) and should be merged.

Circularity Check

1 steps flagged

Euclidean-vs-cosine 'breakthrough' reduces by construction: Table 3 shows Dot=Cosine, so embeddings are normalized and Euclidean distance is a monotone transform of cosine similarity.

specific steps
  1. self definitional [Section 4.2 (Experiment 2), Table 3; Eq. (1); text-domain analysis paragraph]
    "Euclidean distance and dot product yield identical results (as expected for normalized embeddings), providing negligible improvements (0-3%) over cosine similarity. ... Table 3: Cosine Similarity 0.998 0.992 0.999∗ 0.966∗ 0% / Euclidean Distance 0.688 0.595 0.758 0.331 24-66% / Dot Product 0.998 0.992 0.999∗ 0.966∗ 0%"

    Table 3 reports Dot Product and Cosine Similarity as numerically identical for every model and category, which holds only for L2-normalized embeddings. For normalized vectors, ||u-v||^2 = 2 - 2*cos(u,v), so Euclidean distance is a strictly decreasing function of cosine similarity. Any monotonicity-invariant evaluation (Spearman correlation, AUC, or threshold-calibrated accuracy) must rank pairs identically for both metrics. Eq. (1) defines performance as correlation with the gold standard, which cannot change under a monotone transform of the score. The reported 24-66% improvement is therefore a scale artifact: Euclidean distance scores and cosine similarity scores are compared on different scales, not a property of the distance metric. If the embeddings were not normalized, Dot=Cosine wou

full rationale

No fit-to-prediction circularity is present: the evaluated metrics are scored against independently constructed gold labels, and the LLM-versus-embedding comparisons are empirical and would remain meaningful even if the distance-metric claim failed. There is no load-bearing self-citation; the authors' own prior work (e.g., [40]) is cited only as a method reference, not as justification for a central premise. Constructing gold categories via transformation pipelines is standard benchmark practice and is not circular per se. However, the paper's central 'Distance Metric Breakthrough' is an equivalence-by-construction claim: the paper's own Table 3 shows Dot Product = Cosine Similarity, implying normalized embeddings, under which Euclidean distance is a monotone transformation of cosine similarity. Hence the claimed 24-66% improvement cannot be a genuine property of the distance metric; it is a consequence of comparing raw scores on incompatible scales. Additional internal inconsistencies (six vs seven text subsets, contradictory S4 definitions, and swapped S2/S3/S4 labels in Section 4.1) are correctness and validity threats rather than circularity, but they compound the reliability of the headline numbers. Overall score 6: one central result reduces by construction, while the rest of the evaluation is not circular.

Axiom & Free-Parameter Ledger

1 free parameters · 3 axioms · 0 invented entities

The paper introduces no fitted model and no new physical or theoretical entity. Its numerical claims are experimental. The only hand-set constants are interpretation thresholds. The central assumptions are about the validity of test-suite validation and augmentation-based labels, which is where the paper's inconsistencies concentrate.

free parameters (1)
  • Similarity score thresholds = 0.3 (low), 0.7 (catastrophic), 0.8 (high)
    Hand-set in Section 4.1 and Table 1 notes to label score behavior as acceptable or catastrophic. The F1, accuracy, and 'false positive' interpretations depend on these chosen thresholds.
axioms (3)
  • domain assumption Code that passes its associated test suite after a transformation is functionally equivalent to the original; code that fails is semantically altered.
    Sections 3.2.1 and 3.2.3. Test coverage may be incomplete, so pass/fail does not prove the intended semantic relationship for every pair.
  • domain assumption Text augmentations (synonym, negation, antonym, reordering, translation) produce the intended semantic relationships for all pairs.
    Section 3.2.2. Only 163 pairs per subset are human-annotated, so unannotated pairs inherit the augmenter's intended label without individual verification.
  • domain assumption Human annotation of 163 pairs per category at 95% confidence and 10% margin yields a valid gold standard.
    Section 3.2.3. The sample size calculation is not shown, and a sampling margin does not by itself establish label correctness.

reviewed 2026-08-04 · how reviews work

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Cite this review

Pith. "Pith review of How Small Transformation Expose the Weakness of Semantic Similarity Measures." pith.science (2026). https://pith.science/paper/7CU7ZXWN

@misc{pith2026250909714,
  author       = {Pith},
  title        = {Pith review of: How Small Transformation Expose the Weakness of Semantic Similarity Measures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7CU7ZXWN}},
  note         = {Machine review of arXiv:2509.09714}
}
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read the original abstract

This research examines how well different methods measure semantic similarity, which is important for various software engineering applications such as code search, API recommendations, automated code reviews, and refactoring tools. While large language models are increasingly used for these similarity assessments, questions remain about whether they truly understand semantic relationships or merely recognize surface patterns. The study tested 18 different similarity measurement approaches, including word-based methods, embedding techniques, LLM-based systems, and structure-aware algorithms. The researchers created a systematic testing framework that applies controlled changes to text and code to evaluate how well each method handles different types of semantic relationships. The results revealed significant issues with commonly used metrics. Some embedding-based methods incorrectly identified semantic opposites as similar up to 99.9 percent of the time, while certain transformer-based approaches occasionally rated opposite meanings as more similar than synonymous ones. The study found that embedding methods' poor performance often stemmed from how they calculate distances; switching from Euclidean distance to cosine similarity improved results by 24 to 66 percent. LLM-based approaches performed better at distinguishing semantic differences, producing low similarity scores (0.00 to 0.29) for genuinely different meanings, compared to embedding methods that incorrectly assigned high scores (0.82 to 0.99) to dissimilar content.

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This paper was first reviewed by deepseek-v4-flash on August 4, 2026.