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SabrinaSadiekh/responses-and-asr-labels-small-models

LLM Responses and ASR Labels — Small Models Model responses to harmful prompts, labelled by 4 LLM-as-judge guards.Companion dataset for the master's thesis ASR Signal Geometry: Dense Representations vs. SAE Features (HSE, 2025). Dataset composition N = 4 326 prompts per model, (no adversarial suffix). Two sources: Source N Description JailbreakBench () 100 Curated harmful behaviours Anthropic HH-RLHF red-team-attempts () 4 226 Red-team conversations… See the full description on the dataset page: https://huggingface.co/datasets/SabrinaSadiekh/responses-and-asr-labels-small-models.

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LLM Responses and ASR Labels — Small Models

Model responses to harmful prompts, labelled by 4 LLM-as-judge guards. Companion dataset for the master's thesis ASR Signal Geometry: Dense Representations vs. SAE Features (HSE, 2025).

Dataset composition

N = 4 326 prompts per model, (no adversarial suffix). Two sources:

Source	N	Description
JailbreakBench ()	100	Curated harmful behaviours
Anthropic HH-RLHF red-team-attempts ()	4 226	Red-team conversations filtered to (highest harm rating)

Models

Split	Model
	google/gemma-2-2b-it
	google/gemma-3-270m
	google/gemma-3-270m-it
	EleutherAI/pythia-70m-deduped

Columns

Column	Description
	Prompt identifier (…, …)
	Prompt source: or
	Attack variant (all rows: )
	Input prompt sent to the model
	Model response (greedy decoding)
	HH-RLHF red-team success rating (3 = most successful; for jbb)
	HH-RLHF harmlessness score transcript ( for jbb)
	LlamaGuard-3 binary label (1=unsafe, 0=safe)
	LlamaGuard-4 binary label
	Qwen3-small binary label
	Qwen3-large binary label
	Mean of all four guards (NaN→0); values ∈ {0.0, 0.25, 0.5, 0.75, 1.0}

Usage

Citation

If you use this dataset, please cite the accompanying thesis (link TBD).