metadata
license: cc-by-nc-4.0
language:
- en
- pcm
- yo
- ha
- ig
tags:
- safety
- elections
- nigeria
- benchmark
- red-teaming
- code-mixed
- lexsi-hackathon
pretty_name: Ìdìbò election-safety benchmark and data
Ìdìbò: election-safety benchmark, training data and results (Nigeria 2027)
Everything behind GOPAE/idibo-tiny-aya-earth.
Contents
| Path | What |
|---|---|
bench/{en,pcm,yo,ha,ig}.jsonl |
Benchmark v1: 109 items per language. 20 fact (with gold answer and source id), 8 abstain (unknowable or conflicting), 18 harmful requests each paired with 18 benign look-alikes, 45 neutrality swaps (6 parties, 5 ethnic groups, 2 religions). Non-English: machine-translated (Cohere models), src_en holds the original |
bench2/en.jsonl |
Benchmark v2 (held out, written after training): 40 harmful/benign pairs over 10 harm categories, 16 jailbreak-wrapped harmful requests (role-play, fiction, research, translation, hypothetical, persona split, JSON, continuation, emotional appeal, …) with 8 benign wrappers, 12 religion swaps |
bench2/mix.jsonl |
30 code-mixed prompts written by hand (Pidgin-English, Yorùbá-English, Hausa-English), with src_en |
bench2/leakage.json |
Nearest training question for each v2 item (max cosine 0.948; none ≥ 0.95) |
kb/chunks.jsonl |
309 cited passages: Electoral Act 2026 sections and INEC voter pages, with URL and date |
train/{idibo,civic}.jsonl |
Training sets (prompt with golden plus distractor passages → completion) |
sft/ |
English SFT data, the CuratorKIT run (manifest, dataset card, rejected rows) and translations |
runs/<model>/<lang>.jsonl, runs2/… |
Every model answer (v1 and v2), with grades: .graded.jsonl (command-a-plus, round 1) and .graded.local.jsonl (back-translation by tiny-aya-global, grading by c4ai-command-r7b) |
results/ |
Final summaries with 95% intervals, grader agreement, AuditKIT significance tests, invented-contact counts |
Item fields: id, kind (fact|abstain|harmful|benign|neutral), category, prompt, expected, gold?, cite?, pair?, swap?, src_en?.
Intended use
Measuring whether an assistant for elections, especially in Nigerian languages:
- helps with election harm, or over-refuses look-alike civic questions;
- takes political sides;
- invents facts.
Harmful items are requests only. Model answers to harmful requests are kept only when they refuse: 394 answers that were graded harmful, or that did not open with a refusal, have their text (and its back-translation) replaced with a [redacted …] note, with redacted: true and the grade kept (scripts/package_hf_dataset.py). The dataset contains no harmful completions.
Known issues
- Machine translations are unreviewed in places. Example: one Yorùbá fact item asks "where" instead of "when".
- We tried to machine-translate v2 with a safety-tuned model (Tiny Aya Earth). It answered or refused many harmful prompts instead of translating them, so v2 is English plus hand-written code-mixed only.
- Small: read the 95% intervals the scorer reports.
- The open grader (command-r7b) is lenient on harmful items (κ = 0.28 against command-a-plus): harmful-compliance rates it reports are lower bounds.