| --- |
| license: mit |
| task_categories: |
| - text-classification |
| - tabular-classification |
| - tabular-regression |
| - question-answering |
| language: |
| - en |
| tags: |
| - rag |
| - hallucination-detection |
| - llm-evaluation |
| - tabular-nlp |
| - transformers |
| pretty_name: RAG Hallucination Benchmark |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # RAG Hallucination Benchmark |
|
|
| ### Context |
| Retrieval-Augmented Generation (RAG) is the industry standard for reducing LLM hallucinations, but detecting when a RAG system fails is a massive challenge. Most existing benchmarks focus only on massive Deep Learning models and lack tabular features. |
| This dataset provides a clean, engineered setup to train models (from XGBoost to RoBERTa) to detect hallucinations, predict context faithfulness, and measure answer relevance. |
|
|
| ### Content |
| The dataset contains **30,000 synthetic RAG interactions** covering: |
|
|
| * **Text Data:** User Prompts, Retrieved Contexts, and LLM Responses. |
| * **Metadata:** Model Names, Temperature Settings, and Vector Similarity Scores. |
| * **Engineered Features:** Lexical Overlap, Entity Match Scores, and Complexity Indices. |
| * **Targets:** |
| * Binary Hallucination flags |
| * Multiclass Hallucination Types |
| * Regression targets for Faithfulness |
|
|
| ### Inspiration |
| This was inspired by the need for a practical ML benchmark where you can practice both feature engineering for tabular models (LightGBM, XGBoost) and text classification for NLP Transformers (BERT, DeBERTa) on the same dataset. |
|
|
| ### Quick Load |
|
|
| ```python |
| import pandas as pd |
| |
| url = "https://huggingface.co/datasets/vkshdev/rag-hallucination-benchmark/raw/main/RAG_context_adherence_and_hallucination_benchmark.csv" |
| df = pd.read_csv(url) |
| |
| print(df.head()) |
