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arxiv: 2605.12299 · v1 · submitted 2026-05-12 · 💻 cs.CL

Recognition: no theorem link

GKnow: Measuring the Entanglement of Gender Bias and Factual Gender

Hinrich Sch\"utze, Leonor Veloso

Pith reviewed 2026-05-13 04:46 UTC · model grok-4.3

classification 💻 cs.CL
keywords gender biasfactual genderneuron ablationlanguage modelscircuit analysisdebiasingGKnow benchmarkstereotypes
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0 comments X

The pith

Gender bias and factual gender are entangled in language model circuits and neurons, making ablation unreliable for debiasing.

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

The paper introduces GKnow, a benchmark designed to evaluate language models on both factual gender knowledge and stereotypical gender bias across varied prediction types. Analysis of the circuits and neurons driving these predictions reveals that the two are deeply intertwined rather than operating through separate mechanisms. As a direct result, ablating neurons to reduce bias also damages the model's handling of factual gender information. This entanglement means that neuron ablation cannot serve as a reliable method for removing bias without collateral effects. The work further demonstrates that standard gender bias benchmarks often fail to detect the accompanying loss in factual accuracy.

Core claim

We curate GKnow, a benchmark to assess gender knowledge and gender bias in language models across different types of gender-related predictions. GKnow allows us to identify and analyze circuits and individual neurons responsible for gendered predictions. We test the impact of neuron ablation on benchmarks for disentangling stereotypical and factual gender (DiFair and the test set of GKnow), as well as StereoSet. Results show that gender bias and factual gender are severely entangled on the level of both circuits and neurons, entailing that ablation is an unreliable debiasing method. Furthermore, we show that benchmarks for evaluating gender bias can hide the decrease in factual gender thatac

What carries the argument

The GKnow benchmark for measuring both factual gender knowledge and stereotypical bias, paired with circuit and neuron identification to test ablation effects.

Load-bearing premise

The DiFair benchmark and GKnow test set can reliably disentangle stereotypical gender bias from factual gender knowledge.

What would settle it

Observing a neuron set whose ablation reduces bias on StereoSet and DiFair while preserving or improving accuracy on GKnow's factual gender test items.

Figures

Figures reproduced from arXiv: 2605.12299 by Hinrich Sch\"utze, Leonor Veloso.

Figure 1
Figure 1. Figure 1: Edge and node intersection over union (Jaccard similarity) for minimal, faithful circuits in Llama. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Cross-task faithfulness between the gendered [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Ratio of different types of connections within [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of GKnow sets (types of predic [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Faithfulness for the gender_prediction and pronoun_prediction subsets of GKnow across top-k steps, for Llama (top) and Olmo (bottom). Results are averaged over feminine and masculine subsets. models’ original performance), selecting the top￾n edges for n = 10000, 20000, 30000, ... (results depicted in [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Edge and node intersection over union (Jaccard similarity) for minimal, faithful circuits in Olmo. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Cross-task faithfulness between the gendered tasks of GKnow, for Olmo. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overlap of the top 100 IG neurons across GKnow subsets, for Llama (left) and Olmo (right). [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

Recent works have analyzed the impact of individual components of neural networks on gendered predictions, often with a focus on mitigating gender bias. However, mechanistic interpretations of gender tend to (i) focus on a very specific gender-related task, such as gendered pronoun prediction, or (ii) fail to distinguish between the production of factually gendered outputs (the correct assumption of gender given a word that carries gender as a semantic property) and gender biased outputs (based on a stereotype). To address these issues, we curate \gknow, a benchmark to assess gender knowledge and gender bias in language models across different types of gender-related predictions. \gknow allows us to identify and analyze circuits and individual neurons responsible for gendered predictions. We test the impact of neuron ablation on benchmarks for disentangling stereotypical and factual gender (DiFair and the test set of GKnow), as well as StereoSet. Results show that gender bias and factual gender are severely entangled on the level of both circuits and neurons, entailing that ablation is an unreliable debiasing method. Furthermore, we show that benchmarks for evaluating gender bias can hide the decrease in factual gender knowledge that accompanies neuron ablation. We curate GKnow as a contribution to the continuous development of robust gender bias benchmarks.

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

2 major / 2 minor

Summary. The paper introduces GKnow, a new benchmark designed to separately evaluate factual gender knowledge (correct gender inference from semantic properties) and stereotypical gender bias in language models. It identifies circuits and individual neurons responsible for gendered predictions, then conducts ablation experiments on DiFair, the GKnow test set, and StereoSet. The central results claim severe entanglement between bias and factual gender at both circuit and neuron levels, rendering ablation-based debiasing unreliable; the work also argues that standard bias benchmarks can mask accompanying losses in factual knowledge.

Significance. If the entanglement findings hold after addressing controls, the paper would be significant for mechanistic interpretability and bias mitigation research. It contributes a benchmark that explicitly disentangles factual gender from stereotypes, provides empirical evidence on the limits of neuron/circuit ablation for debiasing, and highlights risks in current evaluation practices. Strengths include the curation of GKnow as a public resource and the multi-benchmark testing of both circuits and neurons.

major comments (2)
  1. [Ablation Experiments] Ablation results (detailed after the circuit identification in the experiments): performance drops are reported only on DiFair, GKnow test set, and StereoSet, with no controls on unrelated tasks such as general language understanding (e.g., GLUE subsets or factual QA). This is load-bearing for the entanglement claim, because nonspecific representational damage from ablation could produce the observed drops without requiring circuit-level overlap between bias and factual gender.
  2. [Neuron-level Analysis] § on neuron-level analysis: the claim that individual neurons are 'responsible for gendered predictions' and that their ablation reveals entanglement relies on the assumption that the GKnow test set and DiFair reliably isolate factual vs. stereotypical effects; without reported validation that ablations preserve non-gender capabilities, the neuron-level conclusion risks overinterpretation.
minor comments (2)
  1. [Abstract and Introduction] The abstract and introduction could more explicitly state the size and construction details of the GKnow test set (e.g., number of examples per category) to allow immediate assessment of statistical power.
  2. [Figures] Figure captions for circuit diagrams should include the exact activation thresholds or selection criteria used to identify the reported circuits.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review and the recommendation for major revision. We appreciate the feedback on the ablation experiments and neuron-level analysis. We address each major comment below, indicating the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Ablation Experiments] Ablation results (detailed after the circuit identification in the experiments): performance drops are reported only on DiFair, GKnow test set, and StereoSet, with no controls on unrelated tasks such as general language understanding (e.g., GLUE subsets or factual QA). This is load-bearing for the entanglement claim, because nonspecific representational damage from ablation could produce the observed drops without requiring circuit-level overlap between bias and factual gender.

    Authors: We agree that including controls on unrelated tasks would provide stronger evidence for the entanglement claim by ruling out nonspecific representational damage. Our focus was on the gender-specific benchmarks to directly evaluate the disentanglement. We will incorporate ablation results on a GLUE subset and a factual QA task in the revised manuscript. revision: yes

  2. Referee: [Neuron-level Analysis] § on neuron-level analysis: the claim that individual neurons are 'responsible for gendered predictions' and that their ablation reveals entanglement relies on the assumption that the GKnow test set and DiFair reliably isolate factual vs. stereotypical effects; without reported validation that ablations preserve non-gender capabilities, the neuron-level conclusion risks overinterpretation.

    Authors: The GKnow benchmark is designed to isolate factual gender knowledge from stereotypical bias, providing the basis for our neuron-level claims. We acknowledge the lack of explicit validation for non-gender capabilities in the neuron ablations. In the revision, we will add a discussion of this limitation and, if possible, include checks on unrelated tasks for the neuron ablations. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmark curation and ablation experiments are self-contained

full rationale

The paper introduces the GKnow benchmark to separate factual gender knowledge from stereotypical bias, identifies responsible circuits/neurons via standard mechanistic interpretability techniques, and reports ablation results on DiFair, GKnow test set, and StereoSet. No equations, fitted parameters renamed as predictions, self-citation load-bearing premises, or ansatzes smuggled via prior work appear in the derivation chain. All central claims rest on direct experimental measurements rather than reducing to definitional equivalences or inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard domain assumptions in neural network interpretability research and the validity of the new benchmark for distinguishing factual and biased gender.

axioms (1)
  • domain assumption Mechanistic interpretability can identify circuits and neurons responsible for specific predictions in language models.
    This is a foundational assumption for analyzing individual components and ablation effects.

pith-pipeline@v0.9.0 · 5519 in / 1235 out tokens · 67896 ms · 2026-05-13T04:46:17.392845+00:00 · methodology

discussion (0)

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Reference graph

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