README.md

metadata

language:
  - en
license: cc-by-4.0
pretty_name: GEODML — EMNLP 2026 Reviewer Pack (Condensed)
size_categories:
  - n<1K
tags:
  - causal-inference
  - double-ml
  - dml
  - llm
  - reranker
  - search
  - geo
  - generative-engine-optimization
  - interpretability
  - probing
  - saliency
  - ablation
  - emnlp
  - emnlp-2026
  - reproducibility
task_categories:
  - text-ranking
  - text-classification

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.

---

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}
}

License

CC BY 4.0 — free reuse with attribution.

Contact

valerian.fourel@gmail.com