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REVIEW 3 major objections 6 minor 21 references

Reversing Arrows in Large Language Models

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims that instruction-tuned LLMs classify inverse relations such as mother/child in a direction-dependent way, with a significant head-to-tail vs tail-to-head gap on the FewRel-derived benchmark but not on the TekGen-derived one

desk verdict A useful new benchmark for sentence-level inverse-relation directionality, but the headline FewRel asymmetry may be a lexical-overlap artifact rather than a genuine directional effect. read the letter →

arxiv 2608.03512 v1 pith:3JPUNUHZ submitted 2026-08-04 cs.AI

Sefika Efeoglu , Adrian Paschke This is my paper · ORCID
classification cs.AI
keywords inverserelationsrelationdirectionalityconverselargelanguagemodelsclassificationentityperturbationFewRelTekGen
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 tries to establish that instruction-tuned language models do not reliably track the direction of inverse relations—pairs like mother/child where swapping the two entities flips the meaning. It builds a benchmark of 5,457 sentence-level instances spanning 27 verified inverse relation pairs, queries five open models with a three-way multiple-choice prompt, and perturbs entities to separate web familiarity from true relation reasoning. Its headline finding is a systematic asymmetry: on the FewRel-derived partition every model classified the same facts differently when asked head-to-tail versus tail-to-head, while on the TekGen partition that asymmetry mostly disappeared. A careful reader would care because knowledge-graph construction, question answering, and ontology learning all rely on the model keeping the arrow of a relation straight.

What carries the argument

The central object is the inverse (converse) relation pair—two relations R and R⁻¹ with R⁻¹(x,y) ⇔ R(y,x)—evaluated as a three-way multiple-choice classification where the answer options are the target relation, its inverse, and an unrelated negative. The argument is carried by the benchmark-construction pipeline: entity pairs are extracted from two existing relation-extraction datasets, every direction is verified against a public knowledge base, and direction-specific labels (head-to-tail vs tail-to-head) are assigned only for consistent pairs. Statistical load is carried by paired Wilcoxon signed-rank tests comparing the same model on the same inverse pair in the two directions.

What would settle it

Ask annotators to judge whether the target inverse relation still holds in a random sample (say, 100) of the synthetic- and masked-entity sentences; if a substantial share no longer support the relation, the familiarity interpretation is an artifact. As a direct check on the asymmetry claim, rerun the benchmark with the two inverse labels' positions swapped and see whether the head-to-tail vs tail-to-head gap persists.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that inverse relation directionality is not a solved subskill of relation classification: statistical tests show that the direction you query the relation in systematically changes accuracy on the FewRel-derived benchmark. Specifically, paired Wilcoxon tests over 70 observations (7 inverse pairs × 2 description settings × 5 models) reject the null of equal head-to-tail and tail-to-head performance for original, synthetic, and masked entities, all with p<0.05. On the TekGen benchmark the same comparison over 160 observations shows no significant difference for original or synthetic entities but a significant one for masked entities. The autho

Load-bearing premise

Replacing a named entity with a synthetic name or a placeholder leaves the sentence's relation intact, so the drop in accuracy measures loss of entity familiarity rather than a sentence that no longer expresses the relation.

Editorial extensions

If this is right

  • Adding relation descriptions to the answer options does not consistently improve inverse-relation classification across the five models.
  • Directionality effects are dataset-dependent: every model showed head-to-tail vs tail-to-head asymmetry on the FewRel-derived partition, while the TekGen partition showed asymmetry only when entities were masked.
  • Substituting synthetic or masked entities changes performance materially, especially on FewRel, which the authors read as sensitivity to entity representations.
  • The 5,457-instance, 27-label benchmark gives the field a reusable testbed for direction-verified inverse relation classification.

Reading between the lines

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

  • If the asymmetry reflects pretraining exposure to head-to-tail phrases, fine-tuning on direction-labeled triples or adding contrastive inverse examples should shrink the gap—a testable prediction the paper does not make.
  • The TekGen masked-only effect suggests a latent directional bias that entity names mask; a follow-up could correlate per-relation accuracy with the surface frequency of each relation phrase in the pretraining corpus.
  • Because the choice set always contains exactly the inverse pair plus one negative, part of the measured asymmetry may come from answer-position or label-format bias; varying the set size and options would separate that.
  • The same direction-verified construction can double as a probe for how relation direction is represented internally, for example by inspecting activation differences between correct and incorrect direction prompts.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper investigates whether instruction-tuned LLMs correctly recognize the directionality of inverse relations at the sentence level. The authors construct a benchmark from FewRel and TekGen by verifying head-to-tail and tail-to-head relation labels against Wikidata, and evaluate five open-source LLMs using zero-shot multiple-choice prompts. They compare two directions (HT vs. TH), two prompting variants (with/without relation descriptions), and three entity representations (original, synthetic, masked). The main reported findings are: a statistically significant HT/TH asymmetry on FewRel for all entity types; no significant asymmetry on TekGen for original and synthetic entities but a significant effect for masked entities; no consistent benefit from relation descriptions; and sensitivity to entity representations. The paper also introduces the benchmark design, including a Wikidata-based direction verification step, and provides the full data pipeline in the appendix.

Significance. If the headline claims are valid, this would be a useful systematic evaluation of inverse-relation directionality in LLMs, with a reusable benchmark construction methodology. Strengths include the use of Wikidata to verify relation directions, evaluation across five models, zero-shot prompting, and paired statistical tests. The null result on relation descriptions is a creditable negative finding. However, the central asymmetry claim is threatened by a lexical-overlap confound: answer labels are the relation names themselves, and several FewRel sentences contain those names verbatim, so the observed TH/HT difference may reflect surface-word matching rather than relational reasoning. The entity-familiarity interpretation is also explicitly acknowledged by the authors to be confounded by semantic changes from anonymization. Code and data are only promised upon acceptance, so reproducibility cannot be currently assessed. With additional controls, the benchmark could be valuable; in its present form, the main claim is not fully supported.

major comments (3)
  1. [4.2 / Table 2 / Table 4] The FewRel directional asymmetry is vulnerable to a lexical-overlap confound. In the Bohr example (Table 2), the TH ground-truth 'father' appears verbatim in the sentence ('his father's assistant'), while the HT ground-truth 'child' is only present as 'son'. Because the answer choices are the relation labels themselves, TH receives a surface cue that HT lacks. Masked-entity prompts preserve all content words, so the persistence of the asymmetry under masking is exactly what a cue-based explanation predicts; the absence of the effect on TekGen, whose labels ('characters', 'position held') rarely occur as surface tokens, is also consistent. To support the claimed 'systematic asymmetries', the authors should report per-relation HT vs. TH scores and run a control with paraphrased answer labels or with relation-denoting words removed from the sentence.
  2. [5.1 / Figure 4 / Table 4] Many TekGen inverse pairs have a single sentence (e.g., astronaut mission/crew member, reservoir created/dam in Figure 4), so per-pair macro-F1 takes values of 0 or 1 for those pairs. The paired Wilcoxon test in Table 4 pools 160 such observations, and this high-variance Bernoulli component can mask real effects or produce spurious ones, especially for the masked-entity comparison. Please report per-pair instance counts, exclude or down-weight pairs below a minimum support, and re-check the TekGen results with an instance-level bootstrap or a stratified permutation test.
  3. [6 / Table 6 / Limitations] The third contribution, framed as analyzing the effect of entity familiarity, is not supported by the entity-replacement experiments. As the Limitations section states, Presidio may leave many entities unchanged, and replacing entities can break attribute-dependent relations (e.g., father/mother). The macro-F1 differences in Table 6 are therefore consistent with a change in sentence semantics rather than a loss of entity familiarity. The manuscript should either restrict the analysis to verified anonymizations or explicitly state that Table 6 provides evidence of representation sensitivity only, and adjust the abstract and conclusions accordingly.
minor comments (6)
  1. [References] Reference [15] cites Thoppilan et al. (LaMDA) for Flan-T5 XL, which appears to be a citation error. Please cite the Flan-T5 source (e.g., Chung et al., 'Scaling Instruction-Finetuned Language Models').
  2. [Appendix B] The token list contains the artifact '/quotesingle.Vars' after 'father'; fix the tokenization display.
  3. [Figures 3 and 4] The counts in Figure 3 do not sum to the stated benchmark total; clarify whether these are direction-verified instances and how they relate to the overall 5,457 instances.
  4. [Tables 4-6] Please state whether the Wilcoxon tests are one- or two-sided, how ties are handled, and whether any multiple-comparison correction is applied. The 95% CI of the mean absolute difference does not indicate the direction of the effect.
  5. [4.2] Explain why TekGen negative relations are sampled from all 16 pairs, including those without any sentence instances; this choice may affect option difficulty and deserves justification.
  6. [Table 3] Add error bars or confidence intervals for the macro-F1 values, since many reported differences are within a few percentage points and may be within noise.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all evaluated quantities come from external datasets and zero-shot model outputs; no fitted inputs or self-citation load-bearing steps.

full rationale

This paper is an empirical benchmark evaluation, not a derivation with fitted parameters. Ground-truth labels are obtained from external sources (FewRel/TekGen sentences and Wikidata property queries), and all reported results are zero-shot greedy-decoded model outputs on those externally labeled instances. No parameter is fitted to the evaluation data, and no 'prediction' is constructed from the measurement it purports to validate. The paired Wilcoxon tests compare model performance across experimentally manipulated conditions (direction, relation descriptions, entity perturbations); these are not quantities defined in terms of one another. The benchmark construction choices—fixed seed for negative sampling, Wikidata direction verification, discarding inconsistent pairs—are transparent and do not smuggle in the target result. The only significant caveat is that entity anonymization may change sentence semantics, which the authors explicitly acknowledge in the Limitations section; that is a validity concern, not a circularity. There are no load-bearing self-citations, and none of the enumerated circularity patterns (self-definition, fitted input called prediction, self-citation chain, uniqueness imported from authors, ansatz smuggled via citation, renaming known result) is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The benchmark is an empirical construction with no fitted parameters. The three assumptions listed are the main unproved premises the conclusions rest on: a relaxed notion of inverse subrelations, Wikidata as the authority on relation direction, and macro-F1 as a meaningful metric on extremely imbalanced data. The fixed random seed and negative sampling scheme are procedural choices, not free parameters.

assumptions (3)
  • domain assumption R or R−1 may correspond to gender-specific subrelations of a more general relation, and cannot simultaneously hold for the same ordered pair.
    Adopted in the Inverse Relation Definition (Section 2) to let Mother/Father be inverses of Child while avoiding logical contradictions; a modeling convenience, not a fact about language or Wikidata.
  • domain assumption Wikidata property directionality provides the correct ground-truth direction labels for the sentence-level classification tasks.
    Section 4.1: 'Relation directions are verified against Wikidata' and entity pairs with inconsistent directional information are discarded. If Wikidata directions are wrong or the sentence instantiates a different relation, the labels are wrong.
  • domain assumption Macro-F1 over a highly imbalanced label set, including many relations with a single sentence, is a valid per-pair performance measure.
    Section 5.1: 'We report macro-F1 because the dataset is imbalanced.' With TekGen pairs having as few as 1 instance, per-label F1 is 0 or 1, making the paired comparisons noisy; this assumption underlies all reported scores and significance tests.

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Cite this review

Pith. "Pith review of Reversing Arrows in Large Language Models." pith.science (2026). https://pith.science/paper/3JPUNUHZ

@misc{pith2026260803512,
  author       = {Pith},
  title        = {Pith review of: Reversing Arrows in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3JPUNUHZ}},
  note         = {Machine review of arXiv:2608.03512}
}
read the original abstract

Large language models (LLMs) have achieved strong performance on text-to-knowledge graph generation and related tasks. Nevertheless, it is still unclear whether they accurately model the direction-dependent semantics of inverse relations, in which reversing the order of the arguments alters the meaning of a relation (e.g., \textit{mother} versus \textit{child}). To the best of our knowledge, this work presents the first systematic study of inverse relation directionality in LLMs, using a benchmark consisting of 5,457 instances spanning 27 distinct inverse relation labels. We evaluate five open-source LLMs under a multiple-choice prompting framework and further examine the influence of relation descriptions and entity representations by substituting the original entities with synthetic and masked entities. Our findings reveal systematic asymmetries in inverse relation classification across LLMs, indicate that relation descriptions do not consistently improve performance, and show that model performance can be sensitive to variations in entity representations.

Figures

Figures reproduced from arXiv: 2608.03512 by the authors.

Figure 1
Figure 1. Example of an inverse relation pair: child versus mother. In this work, we address the following research question: Do instruction-tuned LLMs correctly recognize inverse relation directionality? To answer this question, we conduct a systematic analysis of inverse relation directionality in LLMs. We evaluate whether instruction-tuned LLMs correctly recognize inverse relation pairs in both head￾to-tail and tail-to-hea… view at source ↗
Figure 2
Figure 2. Overview of the benchmark construction and evaluation pipeline [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Statistics of head-to-tail relation labels in the dataset constructed from FewRel. 0 50 100 150 200 250 Samples characters present in work position held officeholder astronaut mission crew member based on derivative work cast member character role reservoir created dam part of has part taxon synonym taxon synonym of creator notable work followed by follows coach of sports team head coach depicts depicted by 284 284 … view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Statistics of head-to-tail relation labels in the dataset constructed from the TekGen parti￾tion [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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