datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DeceptionBench
DeceptionBench: A Comprehensive Benchmark for Evaluating Deceptive Behaviors in Large Language Models
🔍 Overview
DeceptionBench is the first systematic benchmark designed to assess deceptive behaviors in Large Language Models (LLMs). As modern LLMs increasingly rely on chain-of-thought (CoT) reasoning, they may exhibit deceptive alignment - situations where models appear aligned while covertly pursuing misaligned goals.
This benchmark addresses a critical gap in AI… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/DeceptionBench.EDR_Telemetry_SampleThis dataset contains raw Endpoint Detection & Response (EDR) telemetry captured during controlled Deception.Pro malware sandbox operations on an enterprise Active Directory network. Unlike most malware sandboxes — which detonate samples for roughly 30 minutes — our operations run for hours or days per analysis, capturing the full arc of adversary behavior. The data represents a full-fidelity snapshot of system activity recorded while threat actors interacted with a live deception environment… See the full description on the dataset page: https://huggingface.co/datasets/DeceptionPro/EDR_Telemetry_Sample.gemma-4-e2b-deception-behavior-completions
Gemma-4-E2B deception & behavior completions
Consolidated 910-row corpus of (scenario prompt + Gemma-4-E2B-generated completion) pairs from earlier mechanistic-interpretability experiments. Each row captures the prompt the model saw and the text it actually produced; for a subset, Claude-Haiku-4-5 judge verdicts and SAE-feature labels are included.
The corpus is meant to be used as activation-extraction input for downstream interpretability work — Natural Language Autoencoder (NLA)… See the full description on the dataset page: https://huggingface.co/datasets/Solshine/gemma-4-e2b-deception-behavior-completions.contextual-deception-detection
ConDec: Contextual Deception Detection Benchmark
Detecting Technically-True-but-Misleading Claims in Scientific ML Papers
Overview
ConDec is a benchmark for detecting contextual deception — statements in scientific ML papers that are literally true but systematically misleading due to omitted context, cherry-picked results, or other forms of pragmatic manipulation.
Unlike fact verification, which checks whether claims are supported by evidence, contextual deception… See the full description on the dataset page: https://huggingface.co/datasets/t6harsh/contextual-deception-detection.