README.md

---
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**.