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geodml-emnlp-2026-reviewer

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GEODML — EMNLP 2026 reviewer pack

A condensed companion to the EMNLP 2026 submission "Causal Analysis of LLM Search Rerankers via Double/Debiased Machine Learning." Designed for fast verification, not full reproduction.

This pack (5.6 MB) — every paper claim as a CSV + every figure as PDF/PNG + a one-shot verify.py claim-checker.
Full reproducibility dataset (1.8 GB) → ValerianFourel/geodml-emnlp-2026 — raw LLM rerank outputs, page features, DFS confounders, and the full DML/probing/saliency/ablation code.

What we did (1-paragraph)

We ask which content features of a web page cause an LLM reranker to keep that URL in its top-K (admission) and to push it toward the top — separating cause from spurious correlation with confounders like domain authority. Six features are declared a priori as treatments (stats density, question-headings, JSON-LD schema, citation authority, topical competence, freshness) and tested with Double/Debiased ML controlling for 28 confounders plus the other five treatments mutually. 65K (keyword × url × condition) rows across 4 prompt variants × 2 reranker backbones (Llama-3.3-70B, Qwen-2.5-72B) × 2 SERP backends × 2 candidate-pool sizes feed 216 fitted DML models. Mechanism is verified by three orthogonal interpretability probes (linear probes on hidden states, gradient saliency, token ablation).

Headline. T5 topical competence is the dominant promoter; T3 JSON-LD schema is a small demoter (i.e. it does not help — counter to common SEO advice); T1b stats density has high Qwen saliency (1.93×) but zero DML coefficient — attention is not effect.

For deeper analysis (re-fitting DML on custom slices, inspecting raw LLM rerank outputs, plotting layer-wise probes), grab the full pack: ValerianFourel/geodml-emnlp-2026.

Setup (60 seconds)

# 1. Pull the reviewer pack
huggingface-cli download ValerianFourel/geodml-emnlp-2026-reviewer \
    --repo-type=dataset --local-dir ./geodml-reviewer
cd geodml-reviewer

# 2. Install
pip install pandas

# 3. Run the one-shot claim-checker
python verify.py

verify.py prints every headline number alongside its paper-text expected value and a tolerance. The last line says either

   All paper claims VERIFIED against the tables.

or it lists which claims fall outside tolerance.

What is what

`tables/` — condensed paper claims (CSV)

File	Rows	What it is
`table2_dml_headline.csv`	18	The paper's Table 2. Spec B (mutually-controlled) DML coefficients for 6 treatments × 3 outcomes, pooled across all slices. Columns: outcome, treatment, coef, se, ci_lo, ci_hi, p_val, sig_stars, n, coef_promoter_dir.
`dml_all_specs_all_slices.csv`	216	Every fitted DML model — Spec A (marginal) and Spec B (partial), across 11 slices (POOLED + 10 sub-slices), 6 treatments, 3 outcomes. Columns: spec, slice, treatment, outcome, coef, se, p_val, n, bonferroni_sig.
`probing_peaks_per_variant.csv`	~56	Peak linear-probe ROC AUC per (variant, treatment, pooling). Columns: variant, treatment, pooling, peak_layer, peak_roc_auc, layer_0_roc_auc.
`admission_probe_headline.csv`	8	The admission pre-commitment story — L0, peak, and final-layer ROC AUC of the `Y1_admission_inctx` probe across 4 variants × 2 poolings.
`saliency_summary.csv`	8	Llama-3.3-70B vs Qwen-2.5-72B gradient-saliency ratios on 4 treatments. Numbers >1 mean the model attends more to treatment tokens than to surrounding tokens.
`ablation_summary.csv`	24	Mean / median / std Δrank when treatment tokens are ablated, per (treatment, prompt, frame).
`freshness_leakage_diagnostic.md`	—	Year-token leakage diagnostic for T6 freshness — included as a methodological caveat.

`figures/` — every PDF + PNG the paper references

Each figure is included as both PDF (for camera-ready) and PNG (for quick HTML preview).

File	What it is
`dml_estimation_framework.{pdf,png}`	The DML estimation framework diagram (data flow).
`fig01_admission_forest.{pdf,png}`	Forest plot of Spec B coefficients on Y1 admission.
`fig02_rank_delta_forest.{pdf,png}`	Forest plot on Y2 = rank_delta.
`fig03_post_rank_forest.{pdf,png}`	Forest plot on Y3 = rank_post.
`fig04_three_outcome_grid.{pdf,png}`	All three outcomes side-by-side.
`fig05_admission_vs_rank_scatter.{pdf,png}`	Y1 vs Y2 per treatment — admission and promotion magnitude correlate.
`fig06_marginal_vs_partial.{pdf,png}`	Spec A vs Spec B (does mutual control change the story?).
`fig07_variant_grid_admission.{pdf,png}`	Y1 across the 4 prompt variants.
`fig08_variant_grid_rank.{pdf,png}`	Y2 across the 4 prompt variants.
`fig09_compare_rag_vs_snippet.{pdf,png}`	RAG vs snippet-only prompt comparison.
`fig10_compare_ddg_vs_searxng.{pdf,png}`	DuckDuckGo vs SearXNG SERP-backend comparison.
`fig11_compare_llama_vs_qwen.{pdf,png}`	Llama vs Qwen reranker comparison.
`fig12_compare_pool20_vs_pool50.{pdf,png}`	Top-20 vs top-50 candidate-pool comparison.
`fig13_admission_detail.{pdf,png}`	Detail view of admission coefficients with CIs.
`fig14_robust_survivors.{pdf,png}`	The robust-winners frame (slice-stable treatments).
`fig_probing_layerwise.{pdf,png}`	Layer-wise probe ROC AUC per treatment.
`fig_probing_pooling.{pdf,png}`	Probe ROC AUC by token-pooling strategy.
`fig_admission_pooled.{pdf,png}`	Admission probe across layers, pooled across variants.
`fig_admission_variants.{pdf,png}`	Admission probe across layers, split by variant.
`fig_saliency.{pdf,png}`	Diverging-bar saliency-ratio chart (Llama vs Qwen).

`verify.py` — one-shot claim checker

Reads every CSV in tables/ and asserts each headline number against its paper-text value within tolerance. Exits 0 on PASS, 1 on FAIL. Run after every pull to confirm the data matches what the paper says.

Headline numbers — Table 2 (Spec B POOLED)

DML coefficients with has_llms_txt as a confounder (not a treatment), across 6 canonical content treatments.

Treatment	Y1 selected	Y2 Δrank	Y3 rank_post
T5 topical competence	+0.037**	−0.530**	−0.299**
T2a Q-headings	+0.016**	+0.136**	−0.041*
T3 schema (JSON-LD)	−0.014**	−0.051*	+0.095**
T6 freshness	−0.005**	−0.061**	+0.005
T4 citation authority	+0.001	−0.023*	−0.015*
T1b stats density	−0.000	−0.003	−0.002

Sign convention: +Y1, +Y2, −Y3 ⇒ URL favoured by the LLM. Stars: *** < .001, ** < .01, * < .05.

Admission pre-commitment probe

Linear probe for the binary admission outcome on the LLM's internal activations at each layer. Averaged across the 4 prompt variants, mean pooling over context tokens.

Quantity	Value	Interpretation
Layer 0 ROC AUC	0.671	Embedding alone — weak admission signal.
Peak ROC AUC	0.862	At layer 60 (~75% network depth).
L0 → peak gain	+0.191	Genuine compositional integration.

Saliency (gradient attribution)

Saliency ratio = mean |∇| on treatment tokens / mean |∇| on surrounding-context tokens. >1 means the model attends more to the treatment than to context; <1 means it ignores it.

Treatment	Llama-3.3-70B	Qwen-2.5-72B	DML direction	Reading
T1b stats density	0.89×	1.93×	null	Qwen looks at stats but DML says it doesn't move ranks.
T2a Q-headings	1.05×	0.90×	promoter	Comparable attention; works for both.
T3 schema	0.19×	0.40×	demoter	Both models ignore JSON-LD — DML demoter is consistent.

Citation

@inproceedings{fourel2026geodml,
  title     = {Causal Analysis of LLM Search Rerankers via Double/Debiased Machine Learning},
  author    = {Fourel, Valerian},
  year      = {2026},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing}
}