Faruna01/lsr-anchoring-phase2-results

LSR Anchoring — Multilingual Benchmark & Safety Steering Results

This dataset contains all experimental results for two related studies:

Study 1 - Benchmark: Multilingual Jailbreak Vulnerability Benchmark and Mitigation for Low-Resource African Languages GlobalSouthML Hackathon 2026, Apart Research

Study 2 — Mitigation: Latent Space Refusal Anchoring for Low-Resource African Languages: Mechanistic Safety Recovery Without Retraining ICML 2026, GlobalSouthML Workshop

Together they form a full pipeline: find the failure → characterise it geometrically → fix it, all without supervised African-language data.

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Languages Covered

Code	Language	Family	Resource Level
yo	Yoruba	Niger-Congo	Low
ha	Hausa	Afro-Asiatic	Low
ig	Igbo	Niger-Congo	Low
igl	Igala	Niger-Congo	Very Low (no ISO 639-1)
sw	Swahili	Bantu	Mid
ar	Arabic	Semitic	High

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Models Evaluated

Model	Study	Method
meta-llama/Meta-Llama-3-8B-Instruct	Both	Benchmark + SAE & Mean-Act Steering
meta-llama/Llama-3.1-70B-Instruct	Steering only	Mean-Act Steering
mistralai/Mistral-7B-Instruct-v0.3	Both	Benchmark + Mean-Act Steering
Qwen/Qwen2.5-7B-Instruct	Both	Benchmark + Mean-Act Steering
google/gemma-2-9b-it	Benchmark only	Benchmark

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Repository Structure

lsr-anchoring-phase2-results/ │ ├── benchmark/ ← Study 1: Benchmark results │ ├── raw_llama3-8b_*.csv — Per-prompt responses, Llama-3-8B │ ├── raw_mistral-7b_*.csv — Per-prompt responses, Mistral-7B │ ├── raw_qwen2.5-7b_*.csv — Per-prompt responses, Qwen2.5-7B │ ├── raw_gemma2-9b_*.csv — Per-prompt responses, Gemma2-9B │ ├── summary_llama3-8b_*.json — SRR summary by language │ ├── summary_mistral-7b_*.json │ ├── summary_qwen2.5-7b_*.json │ ├── summary_gemma2-9b_*.json │ ├── benchmark_results_merged.csv — All models merged │ └── Model-EnglishSRR-AvgAfricanSRR-SafetyGap.csv │ ├── llama-3-8b-path-a/ ← Study 2: Llama-3.1 8B SAE steering │ ├── results_rows.csv/json │ ├── anchor_cache.json │ ├── full_sweep.csv │ └── logs/ │ ├── llama-3-8b-path-b/ ← Study 2: Llama-3.1 8B Mean-Act steering │ ├── results_8b_pathB_rows.csv/json │ └── anchor_cache_8b_pathB.json │ ├── llama-3-70b/ ← Study 2: Llama-3.1 70B │ ├── results_70b_rows.csv/json │ ├── anchor_cache.json │ └── logs/ │ ├── mistral-7b/ ← Study 2: Mistral-7B steering │ ├── results_mistral7b_rows.csv/json │ ├── anchor_cache_mistral7b.json │ └── logs/ │ ├── qwen2.5-7b/ ← Study 2: Qwen2.5-7B steering │ ├── results_qwen7b_rows.csv/json │ ├── anchor_cache_qwen7b.json │ └── logs/ │ └── data/ ← Study 2: Cross-model summary files ├── results_multilang_v3.json ├── results_phase2b_all.json ├── results_phase2b_arabic.json └── results_phase2b_yoruba_ext.json

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Study 1 — Benchmark Results Summary

Safety Refusal Rate (SRR) = refused harmful prompts / total harmful prompts.

Model	English	Yoruba	Hausa	Igbo	Igala	Swahili	Arabic
Llama-3-8B	99%	24%	24%	31%	21%	38%	87%
Mistral-7B	85%	11%	28%	15%	16%	25%	12%
Qwen2.5-7B	95%	4%	6%	14%	13%	0%	83%
Gemma2-9B	97%	55%	72%	20%	47%	94%	99%

Key finding: English SRR stays at 85–99% across all models. Every African language tested shows substantial collapse. Igala is the sharpest stress test, with weak refusal across three of four models despite no prior AI safety coverage.

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Study 2 — LSR-Anchoring Results Summary

Model	Best Language	Peak SRR	KL at Peak	DPL at Peak
Llama-3.1 8B (SAE/Path A)	Igala	0.844	0.31	0.06
Llama-3.1 8B (Mean-Act/Path B)	Hausa	0.71	0.48	0.12
Llama-3.1 70B	Yoruba, Igala	1.00	2.58–3.53	—
Mistral-7B	Igala	0.75	0.61	0.06
Qwen2.5-7B	Hausa	0.51	0.49	0.50

Notable finding: Arabic exhibits inverse transfer on every architecture. English-derived steering directions reduce refusal rates on Arabic harmful prompts as alpha increases (SRR → −0.20 at α=25 on 70B), indicating a geometric mismatch rather than a baseline effect. Do not apply LSR-Anchoring to Arabic-language agents without language-specific direction derivation.

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Live Dashboard

Explore all benchmark and steering results interactively: https://huggingface.co/spaces/Faruna01/lsr-dashboard

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Citation

@misc{faruna2026benchmark,
  title   = {Multilingual Jailbreak Vulnerability Benchmark and Mitigation
             for Low-Resource African Languages},
  author  = {Faruna, Godwin Abuh},
  year    = {2026},
  url     = {https://huggingface.co/datasets/Faruna01/lsr-anchoring-phase2-results}
}

@misc{faruna2026lsranchoring,
  title   = {Latent Space Refusal Anchoring for Low-Resource African Languages:
             Mechanistic Safety Recovery Without Retraining},
  author  = {Faruna, Godwin Abuh},
  year    = {2026},
  url     = {https://huggingface.co/datasets/Faruna01/lsr-anchoring-phase2-results}
}

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Code

All experiment scripts, benchmark code, and prompt sets: https://github.com/farunawebservices/lsr-anchoring

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License

MIT License.