Datasets:
language:
- en
license: mit
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: Project Sentinel – Semantic Filtering Dataset
Project Sentinel – Semantic Filtering Dataset
Dataset Summary
This dataset is designed to train and evaluate lightweight semantic filtering models for Retrieval-Augmented Generation (RAG) systems deployed in resource-constrained / edge environments.
It supports binary classification of (query, candidate_sentence) pairs into:
- 1 – Supporting Fact: Factually useful evidence required to answer the query
- 0 – Distractor: Topically similar but factually insufficient or misleading text
The dataset was created to address the hallucination-by-distraction failure mode commonly observed in local RAG pipelines.
This dataset was used to fine-tune a 4-layer TinyBERT cross-encoder for the Project Sentinel semantic gatekeeping module.
Supported Tasks
- Semantic filtering
- Evidence verification
- Hallucination reduction in RAG
- Binary text classification
Dataset Structure
The dataset consists of sentence-level query–evidence pairs derived from the HotpotQA (Distractor) dataset.
Each sample follows the structure:
| Field | Type | Description |
|---|---|---|
query |
string | Natural language question |
sentence |
string | Candidate sentence extracted from context |
label |
int | 1 = supporting fact, 0 = distractor |
Dataset Splits
| Split | Samples |
|---|---|
| Train | 69,101 |
| Validation | 7,006 |
The class distribution is intentionally imbalanced, reflecting real-world retrieval conditions where distractors vastly outnumber true evidence.
Data Construction
Source Dataset
- HotpotQA – Distractor Setting
- Multi-hop QA dataset with explicit annotations for supporting facts and distractor paragraphs
Positive Samples
- Extracted exclusively from annotated supporting fact sentences
- Based on
(title, sentence index)ground-truth supervision
Negative Samples
- Non-annotated sentences from gold paragraphs (hard negatives)
- Sentences from distractor paragraphs intentionally selected for topical similarity
This strategy produces adversarial negatives closely matching real retrieval errors.
Preprocessing Pipeline
- Removal of null or malformed samples
- Sentence-level deduplication
- Text normalization and whitespace cleanup
- Controlled negative sampling
- Label integrity verification
Input Representation
The dataset is intended for cross-encoder architectures.
Model input format:
[CLS] query [SEP] sentence [SEP]
- Maximum sequence length: 512 tokens
- Designed for TinyBERT / MiniLM-style encoders
Class Imbalance Handling
Due to natural imbalance, training is expected to use:
- Weighted cross-entropy loss
- Recall-preserving thresholds
This ensures evidence recall is prioritized over aggressive filtering.
Intended Use
✔ Training semantic gatekeepers for RAG
✔ Edge-deployed LLM pipelines
✔ Hallucination suppression
✔ Early-exit decision systems
Limitations
- Derived from Wikipedia-based QA (HotpotQA)
- English-only
- Sentence-level relevance only (not passage-level)
Performance may vary when applied to highly domain-specific corpora.
Citation
If you use this dataset, please cite:
@article{salih2026sentinel,
title={Project Sentinel: Lightweight Semantic Filtering for Edge RAG},
author={Salih, El Mehdi and Ait El Mouden, Khaoula and Akchouch, Abdelhakim},
year={2026}
}
Contact
El Mehdi Salih
Mohammed V University – Rabat
Email: elmehdi_salih@um5.ac.ma