| --- |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| language: |
| - zh |
| - en |
| size_categories: |
| - n<1K |
| --- |
| *** |
| |
| # Dataset Card for Chinese Degree Expressions for Pragmatic Reasoning (CDE-Prag), an ongoing project about Enriched Meaning. |
| |
| ## Table of Contents |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Citation Information](#citation-information) |
| |
| --- |
| |
| ## Dataset Description |
| |
| - **OSF Repository:** [https://osf.io/yk2a6/?view_only=82bb92d6d7e943c9b93260c666cfc153] |
| - **Paper:** A Pragmatic Account of Ambiguity in Language Models: Evidence from Chinese [under review] |
| - **Point of Contact:** [Yan Cong] |
| |
| ### Dataset Summary |
| |
| **CDE-Prag** is a theory-driven evaluation dataset designed to probe the pragmatic competence of Large Language Models (LLMs) and Vision-Language Models (VLMs). It focuses specifically on **manner implicatures** and **ambiguity detection** through the lens of Chinese degree expressions (e.g., *Kai gao*, which is ambiguous between "Kai is tall" and "Kai is taller"). |
| |
| CDE-Prag tests whether models can navigate the trade-off between **production cost** (economy) and **communicative utility** (specificity). The dataset is divided into two subsets: |
| 1. **Exploratory VLM Dataset:** A multimodal set (text + image) derived from human-subject research. |
| 2. **Large-Scale LLM Dataset:** A text-only expansion containing 400 balanced context-utterance sets generating over 28,000 unique items. |
| |
| ### Supported Tasks and Leaderboards |
| |
| The dataset supports three primary pragmatic tasks: |
| |
| 1. **Truth Value Judgment (TVJ):** A "test of contradiction" to determine if the model can detect ambiguity. The model must judge if an utterance is true in contexts where only one interpretation (Positive or Comparative) holds. |
| 2. **Alternative Choice (ALT):** A pragmatic reconciliation task. The model must choose between a simple, ambiguous utterance (Economy) and complex, unambiguous alternatives (Specificity). |
| 3. **Contextual Modulation (ALT+QUD):** A conversational task where an explicit **Question Under Discussion (QUD)** is provided to test if the model shifts its preference based on contextual salience. |
| |
| ### Languages |
| |
| The dataset is in **Mandarin Chinese** (Simplified). |
| |
| --- |
| |
| ## Dataset Structure |
| |
| ### Data Instances |
| |
| #### VLM Subset [work-in-progress] |
| An example instance for the VLM dataset includes a visual scene and a context description: |
| ```json |
| { |
| "id": "vlm_tall_01", |
| "modality": "multimodal", |
| "context_text": "Anna considers 172cm to be tall. Ryan is 160cm, Kai 175cm, Jim 170cm.", |
| "image_path": "images/tall_01.png", |
| "utterance": "Kai gao (Kai is tall)", |
| "condition": "POS-T-COMP-F", |
| "task_type": "TVJ", |
| "options": ["Can", "Cannot"], |
| "human_label_distribution": {"Can": 1.0, "Cannot": 0.0} |
| } |
| ``` |
| |
| #### LLM Subset |
| An example for the text-only ALT task: |
| ```json |
| { |
| "id": "llm_expensive_05", |
| "modality": "text", |
| "context_text": "Description of a scenario where Item A is expensive but Item B is more expensive...", |
| "utterance": "Item A gui (Item A is expensive)", |
| "alternatives": { |
| "UTT": "Item A gui", |
| "ALT_1": "Item A hen gui (Positive)", |
| "ALT_2": "Item A bijiao gui (Comparative)", |
| "ALT_3": "Item A hen gui but not taller...", |
| "ALT_4": "Item A bijiao gui but not positive..." |
| }, |
| "condition": "POS-T-COMP-T", |
| "task_type": "ALT", |
| "QUD": "None" |
| } |
| ``` |
| |
| ### Data Fields |
|
|
| * `context_text`: The linguistic context establishing the world state (thresholds, comparison classes). |
| * `image`: (VLM only) Visual representation of the world state. |
| * `utterance`: The target ambiguous Chinese degree expression. |
| * `condition`: The truth-conditional status of the utterance (e.g., `POS-T-COMP-F` means Positive reading is True, Comparative reading is False). |
| * `alternatives`: A set of semantically equivalent but costlier options used in the ALT tasks. |
| * `QUD`: (Task 3 only) An explicit Question Under Discussion designed to make one reading more salient. |
|
|
| ### Data Splits |
|
|
| * **VLM Subset:** 26 unique sets based on 4 evaluative adjectives (*tall, expensive, big, fast*). Intended for few-shot or exploratory evaluation. |
| * **LLM Subset:** 400 balanced sets covering 34 additional adjectives (e.g., *hot, thick, old*), totaling ~28,000 unique items. This split is designed for high-powered statistical validation. |
|
|
| --- |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| This dataset was created to bridge the gap in **multilingual** and **multimodal** pragmatic resources. Current benchmarks often focus on literal semantics or scalar implicatures (mostly in English). CDE-Prag explicitly targets **M-implicatures** (Manner), where the choice of form (simple vs. complex) drives meaning. It allows researchers to test if models behave as "rational agents" by optimizing the trade-off between production cost and communicative clarity. |
|
|
| ### Source Data |
|
|
| * **VLM Data:** Derived from human-subject experimental stimuli in *Cong (2021)*. The images and contexts were validated for grammaticality and word frequency. |
| * **LLM Data:** Manually crafted expansions of the VLM logic. The text-only scenarios verbalize the visual information found in the VLM subset. |
|
|
| ### Annotations |
|
|
| * **Validation:** All items were manually verified for acceptability by a linguist who is a native speaker of Mandarin Chinese. |
| * **Human Baseline:** The dataset includes aggregated human response distributions (N=21 per task) to establish a "human-like" baseline for rationality and ambiguity detection. |
|
|
| --- |
|
|
| ## Considerations for Using the Data |
|
|
| ### Social Impact of Dataset |
|
|
| This dataset contributes to the development of more **culturally and linguistically inclusive AI**. It evaluates models on an understudied pragmatic phenomena. Furthermore, by probing "rational" communication strategies, it aids in creating agents that communicate more efficiently and naturally with humans. |
|
|
| ### Other Known Limitations |
|
|
| * **Scale of VLM Data:** The VLM subset is small (26 items) and should be treated as a proof-of-concept. Statistical claims based on this subset rely on bootstrapping over generations rather than items. |
| * **Domain Specificity:** The dataset focuses on degree expressions (gradable adjectives). Generalizability to other pragmatic phenomena (e.g., irony, metaphors) remains to be tested. |
|
|
| --- |
|
|
| ## Additional Information |
|
|
| ### Citation Information |
| We acknowledge Brian Buccola, Phillip Wolff, and Marcin Morzycki for their inspirations and fruitful discussions. |
| This research is supported by the College of Liberal Arts at Purdue University. |
| If you find this resource useful, please consider citing our work: |
|
|
| ```bibtex |
| @article{Cong2026Pragmatic, |
| title={A pragmatic account of ambiguity in language models: Evidence from Chinese}, |
| author={Cong, Yan}, |
| journal={Under Review}, |
| year={2026}, |
| note={https://huggingface.co/datasets/CALM-Lab-Purdue/EnrichedMeaningDataset} |
| } |
| ``` |
