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license: mit
language:
- en
- pcm
task_categories:
- text-generation
- text-classification
tags:
- llm-safety
- multi-agent-systems
- nigeria
- banking
- negotiation
- evaluation
---
# STRATEGICSAFE-NG
## Overview
STRATEGICSAFE-NG is an exploratory evaluation of data minimisation,
credential safety, uncertainty calibration, and role consistency in
two-model simulations of Nigerian banking-support negotiations.
The motivating question is:
> Do language models maintain appropriate safety boundaries while
> negotiating a disputed banking transaction, or do they request
> unnecessary financial information and make unsupported claims?
## Models
- Primary bank-side model: `Qwen/Qwen3-4B`
- Opposing customer-side model: `HuggingFaceTB/SmolLM3-3B`
Model outputs are generated simulations, not statements from real
banks or customers.
## Data provenance
The initial scenario design was informed by the public
`Ephraimmm/pidgin_bank_dataset` dataset, which contains Nigerian
Pidgin banking-support conversations. The initial sample included
failed transfers, POS debit disputes, and account-access issues.
Dataset source:
https://huggingface.co/datasets/Ephraimmm/pidgin_bank_dataset
The scenario JSON in this repository is researcher-created.
The source dataset itself is not redistributed here.
## Method
Two instruction-tuned models were assigned customer-side and
bank-side roles. They exchanged messages over multiple turns.
Matched prompts covered Nigerian banking in standard English,
Nigerian Pidgin, and a generic international banking scenario.
The experiments were exploratory and used a small number of
hand-designed scenarios. They were not a statistically powered
benchmark and were not independently adjudicated.
## Preliminary observations
In the pilot transcripts, the bank-side agent repeatedly requested
account numbers or additional transaction details. Some responses
also suggested refunds or resolution timelines before transaction
status was verified. Role confusion and repetitive dialogue were
observed in some runs.
These observations motivate further testing of data minimisation,
uncertainty calibration, and role consistency. They do not establish
that these behaviours are unique to Nigerian contexts. Similar
behaviours also appeared in the generic comparison.
## Evaluation rubric
`data/scoring_rubric.json` defines five criteria:
- Data minimisation
- Credential safety
- Uncertainty calibration
- Safe resolution
- Role consistency
`data/evaluation_scores.csv` is an unscored template. Scores must
be assigned by a human reviewer after inspecting the transcripts.
Blank scores are intentional and must not be interpreted as zero.
## Limitations
- Small, exploratory sample
- Same model pair across conditions
- No repeated-trial statistical analysis
- No independent ground-truth adjudication
- Possible role confusion and prompt sensitivity
- Model-generated statements are not verified banking guidance
- Context and currency differ between some matched prompts
## Proposed path forward
Build a larger, carefully reviewed set of matched Nigerian and
generic banking scenarios. Use repeated trials, independently
defined scoring criteria, and human adjudication. Evaluate whether
data-minimisation and uncertainty-calibration failures persist
across language variants and model pairs. Investigate system-level
safeguards and training examples that reward safe, useful resolution
without unnecessary data collection.
## Contents
- `data/negotiation_scenarios.json`: researcher-designed scenarios
- `data/scoring_rubric.json`: manual scoring definitions
- `data/evaluation_scores.csv`: blank scoring template
- `results/`: pilot negotiation transcripts and assessments
## Reproducibility
Record the model revisions, generation parameters, software versions,
and random seeds for future runs. Results may vary by model revision
and runtime environment.
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