Update README.md

README.md

@@ -9,6 +9,19 @@ language:
 size_categories:
 - 10K<n<100K
 configs:
+- config_name: default
+  data_files:
+  - split: harmful
+    path:
+    - beavertails_unsafe_vllm.csv
+    - donot_answer_vllm.csv
+    - forbidden_questions_vllm.csv
+    - harmfulqa_vllm.csv
+    - llm_redteaming_vllm.csv
+    - synthetic_harmful_prompts_vllm.csv
+    - prompt_injection_unsafe_vllm.csv
+  - split: safe
+    path: prompt_injection_safe_vllm.csv
 - config_name: beavertails
   data_files:
   - split: harmful
@@ -41,6 +54,9 @@ configs:
   data_files:
   - split: safe
     path: prompt_injection_safe_vllm.csv
+tags:
+- Model_safety
+- SFT
 ---
 
 # Safety Aligned Datasets
@@ -65,79 +81,107 @@ This dataset was built to close that gap — and to go further. Because alignmen
 
 ---
 
-## Dataset Overview
+## Dataset Structure
+
+This corpus contains **eight configurations** across **two splits**.
+
+The `default` config combines all files and exposes both splits simultaneously — useful for full-dataset training runs. The individual named configs allow subset-level access for targeted experiments.
+
+---
 
-This corpus spans **eight configurations** across **two splits**, each targeting a distinct failure mode in model alignment.
+## Splits
 
-### The `harmful` Split
+### `harmful` — Unsafe Behavior Across Seven Configurations
 
-This split contains adversarial prompts and model-generated responses representing unsafe behavior across seven configurations. Each configuration covers a different attack class or harm domain.
+Each configuration under this split targets a different failure mode in model alignment.
+
+---
 
 **`beavertails`**
-Covers the canonical safety meta-categories — violence, hate speech, self-harm — drawn from one of the most widely cited safety benchmarks in the field. A principled baseline across well-defined harm taxonomies.
+
+Covers the canonical safety meta-categories — violence, hate speech, self-harm — drawn from one of the most widely cited safety benchmarks in the field. Provides a principled baseline across well-defined harm taxonomies, making it the right starting point for calibrating a model's refusal behavior before moving to more complex attack classes.
+
+---
 
 **`donot_answer`**
-Prompts mapped to a structured AI safety evaluation framework. Best suited for benchmarking against established refusal rubrics and tracking alignment progress across model versions.
+
+Prompts mapped to a structured AI safety evaluation framework. Best suited for benchmarking against established refusal rubrics and tracking alignment progress across model versions in a reproducible, framework-aligned way.
+
+---
 
 **`forbidden_questions`**
-The most adversarially complex configuration. Prompts rely on fictional framing, nested hypotheticals, and philosophical edge cases — exactly where naive keyword-based safety mechanisms consistently break down.
+
+The most adversarially complex configuration in the harmful split. Prompts rely on fictional framing, nested hypotheticals, and philosophical edge cases — exactly where naive keyword-based safety mechanisms consistently break down. A model that handles this configuration well has learned to reason about intent, not just surface form.
+
+---
 
 **`harmfulqa`**
-Query-style harmful inputs that closely mimic real user behavior. Risk vectors span multiple domains, making this subset particularly important for models deployed in open-ended conversational settings.
+
+Query-style harmful inputs that closely mimic how real users phrase requests. Risk vectors span multiple domains, making this subset particularly important for models deployed in open-ended conversational settings where the adversarial signal is subtle.
+
+---
 
 **`llm_redteaming`**
-Empirical jailbreaks documented during public and academic red-teaming exercises. These are prompts that have already succeeded in the wild — your model needs to have seen them before deployment.
+
+Empirical jailbreaks sourced from public and academic red-teaming exercises. These are prompts that have already succeeded against deployed models in the wild. A model that has not seen this class of attack is likely to fail it.
+
+---
 
 **`synthetic_harmful_prompts`**
-Adversarially generated expansions that simulate novel, unseen attack patterns. A zero-day stress test designed to push models toward generalizing safe behavior rather than memorizing safe responses.
+
+Adversarially generated expansions designed to simulate novel, unseen attack patterns. Unlike the empirical configurations above, this subset pushes models to generalize safe behavior rather than recall seen examples — the closer approximation of real-world robustness.
+
+---
 
 **`prompt_injection_unsafe_vllm`**
-Targets a distinct and increasingly critical threat class: prompt injection attacks. These prompts attempt to override or hijack the model's system-level instructions through crafted user inputs — a common real-world attack vector in agentic and tool-augmented LLM deployments. Exposure to this subset is essential for any model operating in a pipeline where user input reaches the inference context alongside a system prompt.
+
+Targets prompt injection attacks — attempts to override or hijack system-level instructions through crafted user inputs. This is a distinct and increasingly critical threat class in agentic and tool-augmented LLM deployments, where user input reaches the inference context alongside a privileged system prompt. Most general-purpose safety datasets do not cover this class at all.
 
 ---
 
-### The `safe` Split
+### `safe` — Correct Behavior Under Adversarial Pressure
 
 **`prompt_injection_safe_vllm`**
-The structural counterpart to the unsafe injection configuration above. These records show the model correctly maintaining its intended behavior under injection pressure — ignoring override instructions and staying within its sanctioned boundaries. When paired with the unsafe configuration in a DPO or contrastive SFT setup, these serve as the *chosen* side of the preference pair.
+
+The structural counterpart to `prompt_injection_unsafe_vllm`. These records show the model correctly maintaining its intended behavior under injection pressure — ignoring override instructions and staying within its sanctioned boundaries. In a DPO or contrastive SFT setup, these serve as the *chosen* side of the preference pair, with the unsafe configuration providing the *rejected* side.
 
 ---
 
 ## How to Use This Dataset
 
-### Supervised Fine-Tuning (SFT)
+### Supervised Fine-Tuning
 
-Each adversarial prompt and its generated response can be paired with a curated safe response to create contrastive training examples. The model learns not just what to refuse, but how to refuse in a way that is polite, reasoned, and non-dismissive.
+Each adversarial prompt and its model-generated response can be paired with a curated safe response to create contrastive training examples. The model learns not just what to refuse, but how to refuse in a way that is polite, reasoned, and non-dismissive.
 
-### Direct Preference Optimization (DPO)
+### Direct Preference Optimization
 
-The `(prompt, safety_res, model)` triplet structure maps naturally onto DPO's chosen/rejected framework. The `safety_res` field provides the baseline behavior to use as the rejected sample, paired against a human-curated or verified chosen response. For the prompt injection configurations specifically, `prompt_injection_unsafe_vllm` and `prompt_injection_safe_vllm` form a ready-made contrastive pair without any additional annotation overhead.
+The `(prompt, safety_res, model)` triplet maps naturally onto DPO's chosen/rejected framework. The `safety_res` field provides the baseline behavior to treat as the rejected sample, paired against a verified chosen response. For prompt injection specifically, `prompt_injection_unsafe_vllm` and `prompt_injection_safe_vllm` form a ready-made contrastive pair with no additional annotation overhead.
 
 ### False-Refusal Calibration
 
-Over-refusal is an alignment failure too. Several configurations — particularly `forbidden_questions` and `harmfulqa` — include borderline inputs that use sensitive keywords in genuinely benign contexts. Training on these teaches a model to reason about intent, not just surface-level patterns.
+Over-refusal is an alignment failure too. Configurations like `forbidden_questions` and `harmfulqa` include borderline inputs that use sensitive keywords in genuinely benign contexts. Training on these teaches a model to reason about intent rather than pattern-match on surface signals.
 
 ### Prompt Injection Robustness
 
-With LLMs increasingly deployed in agentic workflows, RAG pipelines, and tool-use environments, resistance to instruction hijacking is no longer optional. The injection split pair directly addresses this training gap, which most general-purpose safety datasets ignore entirely.
+With LLMs increasingly deployed in agentic workflows, RAG pipelines, and tool-use environments, resistance to instruction hijacking is no longer optional. The injection split pair directly addresses this gap for any model operating in a multi-context deployment setting.
 
 ---
 
 ## Adversarial Technique Reference
 
-The prompts across all configurations cover four primary attack classes:
+The prompts across all configurations cover four primary attack classes.
 
 **Role-Play Frameworks**
 Instructions that force the model into an alternate persona — developer mode, uncensored assistant, fictional AI with no restrictions. These exploit the model's instruction-following strength directly against its safety constraints.
 
 **Hypothetical Contextualization**
-Harmful requests buried inside academic thought experiments, historical framing, or fictional narratives. The surface text appears benign; the extracted information is not.
+Harmful requests embedded inside academic thought experiments, historical framing, or fictional narratives. The surface text appears benign; the information being sought is not.
 
 **Linguistic Obfuscation**
 Multi-turn logical chains, domain jargon, and structural misdirection designed to bypass keyword filters and confuse intent classifiers. These require the model to reason about the trajectory of a conversation, not just the final message in isolation.
 
 **Prompt Injection**
-Direct attempts to override system-level instructions through adversarially crafted user inputs. Variants include goal hijacking (redirecting the model to a different task), prompt leakage (extracting the system prompt), and jailbreak injection (embedding role-play attacks inside what appears to be ordinary user data). Particularly relevant for RAG systems, tool-augmented agents, and any deployment where user input coexists with a privileged system context.
+Direct attempts to override system-level instructions through adversarially crafted user inputs. Variants include goal hijacking, prompt leakage, and jailbreak injection. Particularly relevant for RAG systems, tool-augmented agents, and any deployment where user input coexists with a privileged system context.
 
 ---
 
@@ -159,11 +203,13 @@ Batch processing across eight heterogeneous prompt distributions requires a fram
 
 ## Data Schema
 
-Each record across all configurations contains three fields:
+Each record across all configurations contains three fields.
 
-- **`prompt`** — The raw adversarial or harmful input instruction, unmodified from its source
-- **`safety_res`** — The response generated by the inference model during the pipeline run
-- **`model`** — The specific model variant used, enabling controlled ablation studies across response sources
+| Field | Type | Description |
+| --- | --- | --- |
+| `prompt` | string | The raw adversarial or harmful input instruction, unmodified from its source |
+| `safety_res` | string | The response generated by the inference model during the pipeline run |
+| `model` | string | The specific model variant used, enabling controlled ablation across response sources |
 
 ---