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README.md

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 ---
 license: mit
+task_categories:
+- text-classification
+- zero-shot-classification
+tags:
+- nlp
+- contradiction-detection
+- nli
+- robustness
+- data-augmentation
+- adversarial-nlp
 ---
+# MutaCon: A Mutation-Based Contradiction Dataset
+
+MutaCon (Mutation-Based Contradiction Dataset) is a specialized benchmark corpus designed to evaluate the sensitivity, robustness, and semantic boundaries of Natural Language Inference (NLI) models and Large Language Models (LLMs). 
+
+The dataset pairs high-quality baseline English sentences with systematically engineered "mutant" variants. Each mutant is generated via deterministic syntactic, lexical, or structural transformations and features a continuous validation score quantifying its semantic divergence.
+
+## Dataset Structure
+
+### Data Fields
+
+| Field Name | Type | Description |
+| :--- | :--- | :--- |
+| `text` | `string` | The original, high-quality source sentence. |
+| `mutant` | `string` | The programmatically perturbed version of the original sentence. |
+| `strategy` | `string` | The specific linguistic mutation rule applied (1 of 13 categories). |
+| `contradiction_score` | `float` | Continuous confidence score of the contradiction ($[0, 1]$ scale). |
+
+### Example Instance
+
+```json
+{
+  "text": "Frederick Dent bought the two story farmhouse and surrounding land located southwest of St. Louis in 1820.",
+  "mutant": "Frederick Dent sold the two story farmhouse and surrounding land located southwest of St. Louis in 1820.",
+  "strategy": "verb_flip",
+  "contradiction_score": 0.999
+}
+
+```
+
+## Technical Generation Taxonomy
+
+MutaCon utilizes `spaCy` dependency parsing and regex-based span manipulation to execute exactly **13 algorithmic mutation strategies**. These strategies isolate specific linguistic and logical vulnerabilities:
+
+| Strategy | Rule Description | Example Transformation |
+| --- | --- | --- |
+| `word_flip` | Token-level antonym substitution using a static mapping lookup. | *best* $\rightarrow$ *worst* |
+| `state_flip` | Inverts the semantic status of adjectives or conditions. | *open* $\rightarrow$ *closed* |
+| `phrase_flip` | Multi-word substring substitutions to flip clause polarity. | Idiomatic/multi-token expressions |
+| `verb_flip` | Replaces active verbs with their functional or legal antonyms. | *bought* $\rightarrow$ *sold* |
+| `role_swap` | Extracts the `ROOT` verb, then swaps the noun chunks representing the subject (`nsubj`/`nsubjpass`) and object (`obj`/`dobj`/`pobj`). | *A bit B* $\rightarrow$ *B bit A* |
+| `direction_flip` | Swaps positional/directional parameters matching a strict `from X to Y` pattern. | *from X to Y* $\rightarrow$ *from Y to X* |
+| `comparative_swap` | Splits a sentence at the token *than*, reversing the left and right context strings. | *X greater than Y* $\rightarrow$ *Y greater than X* |
+| `number_change` | Extracts numerical entities (`CARDINAL`, `QUANTITY`, `MONEY`, `PERCENT`, `DATE`), updating values by scalar steps ($\pm 1$) or percentage scaling ($\times 1.1$). | *1 GeV/n* $\rightarrow$ *3 GeV/n* |
+| `modality_shift` | Shifts qualifiers governing certainty, frequency, or obligation. | *most frequently* $\rightarrow$ *most rarely* |
+| `quantifier_shift` | Targets determiners and logical quantifiers to alter set scopes. | *few* $\rightarrow$ *none* / *most* $\rightarrow$ *all* |
+| `passive_agent_blind_swap` | Strips the passive helper/agent structure (`was/were/is/are + verb + by`), disrupting thematic role processing. | *was broken by* $\rightarrow$ *broken* |
+| `temporal_aspect_flip` | Mutates aspect/tense markers affecting timeline placement. | Tense aspect shifting |
+| `preposition_tweak` | Alters true prepositional dependencies (`dep_ == "prep"`) to distort spatial or contextual relations. | Case-preserving swaps |
+
+---
+
+## Curation Pipeline & Quality Control
+
+To ensure high data fidelity, MutaCon implements a rigourous three-stage filtration pipeline:
+
+```
+[Baseline Source] ──> [Linguistic Mutation] ──> [Grammar Filtering] ──> [Contradiction Scoring] ──> [Final Dataset]
+
+```
+
+### 1. Baseline Text Sourcing
+
+All source sentences are extracted from the curated [`agentlans/high-quality-english-sentences`](https://huggingface.co/datasets/agentlans/high-quality-english-sentences) dataset to guarantee syntactic variance and exceptional grammatical hygiene prior to mutation.
+
+### 2. Grammatical Validation
+
+Programmatic swaps (especially structural transformations like `role_swap`) can occasionally yield unnatural or fragmented phrasing. To prevent artificial artifacts, all generated mutants are cross-evaluated using the [`agentlans/snowflake-arctic-xs-grammar-classifier`](https://huggingface.co/agentlans/snowflake-arctic-xs-grammar-classifier). Unstable or ungrammatical mutants are automatically discarded.
+
+### 3. Contradiction Filtering & Scoring
+
+Contradictions are empirically validated downstream using [`roberta-large-mnli`](FacebookAI/roberta-large-mnli). The final `contradiction_score` represents the model's explicit confidence in the `contradiction` class prediction. This continuous metric effectively separates benign stylistic adjustments from true semantic contradictions.
+
+## Intended Uses
+
+* **NLI Stress Testing:** Assess whether NLI models genuinely parse semantic relationships or merely rely on shallow word-overlap heuristics (particularly effective when testing against structural changes like `role_swap` and `comparative_swap`).
+* **Adversarial Robustness Evaluation:** Benchmark the boundary vulnerabilities of frontier LLMs against minor, single-token logical and numerical flips.
+* **Contradiction Intensity Modeling:** Utilize the continuous `contradiction_score` to train regression-based semantic divergence and logical mismatch metrics.
+
+## Licensing
+
+This dataset is distributed under the permissive **MIT License**.
+

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