update dataset card(11)

README.md

@@ -28,29 +28,19 @@ configs:
     data_files:
       - split: train
         path: all-modality_ambiguity.json
----
 
-<div align="center">
+---
 
 # TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models
 
-</div>
-
-[![Task](https://img.shields.io/badge/Task-Multimodal%20QA-blue)](#recommended-evaluation-protocol)
-[![Modalities](https://img.shields.io/badge/Modalities-Text%20%2B%20Image%20%2B%20Audio-purple)](#data-format)
-[![Languages](https://img.shields.io/badge/Languages-English%20%7C%20Chinese-green)](#dataset-statistics)
-[![Size](https://img.shields.io/badge/Size-327%20Items-orange)](#dataset-statistics)
-[![Review](https://img.shields.io/badge/Review-Anonymous-lightgrey)](#double-blind-review-notice)
+[![Task](https://img.shields.io/badge/Task-Multimodal%20QA-blue)](#recommended-evaluation-protocol) [![Modalities](https://img.shields.io/badge/Modalities-Text%20%2B%20Image%20%2B%20Audio-purple)](#data-format) [![Languages](https://img.shields.io/badge/Languages-English%20%7C%20Chinese-green)](#dataset-statistics) [![Size](https://img.shields.io/badge/Size-327%20Items-orange)](#dataset-statistics) [![Review](https://img.shields.io/badge/Review-Anonymous-lightgrey)](#double-blind-review-notice)
 
 **TRIAD** is a diagnostic benchmark for evaluating whether omni-modal models can resolve ambiguity by jointly integrating **text**, **image**, and **audio**.
 
----
 ## Overview
 
 TRIAD targets a failure mode common in multimodal evaluation: a model may appear to solve a multimodal task while actually relying on only one dominant modality. In TRIAD, each example is constructed so that the full tri-modal input is needed to identify the intended answer.
 
-<div align="center">
-
 |                     |                                               |
 | ------------------- | --------------------------------------------- |
 | **Task**            | Multiple-choice tri-modal question answering  |
@@ -62,9 +52,6 @@ TRIAD targets a failure mode common in multimodal evaluation: a model may appear
 | **Primary use**     | Held-out diagnostic evaluation                |
 | **Review status**   | Anonymous version for double-blind review     |
 
-</div>
-
----
 ## Table of Contents
 
 - [Overview](#overview)
@@ -84,22 +71,16 @@ TRIAD targets a failure mode common in multimodal evaluation: a model may appear
 - [Maintenance](#maintenance)
 - [Double-blind Review Notice](#double-blind-review-notice)
 
----
 ## What Makes TRIAD Different?
 
 TRIAD is designed around **tri-modal irreducibility**.
 
-<div align="center">
-
-
 | Input condition          | Expected property                                            |
 | ------------------------ | ------------------------------------------------------------ |
 | **Text + Image + Audio** | The intended answer should be identifiable.                  |
 | **Any two modalities**   | The answer should remain underdetermined or less well supported. |
 | **Any single modality**  | Multiple options should remain plausible.                    |
 
-</div>
-
 The benchmark emphasizes audio cues that cannot be fully captured by a transcript, including:
 
 - prosody and intonation;
@@ -108,7 +89,6 @@ The benchmark emphasizes audio cues that cannot be fully captured by a transcrip
 - environmental sound;
 - symbolic sound such as alarms, ringtones, and bells.
 
----
 ## Repository Layout
 
 ```text
@@ -127,9 +107,9 @@ TRIAD/
 │   └── statistics.json
 └── all-modality_ambiguity.json
 ```
+
 > The exact layout may vary across release versions, but every item contains relative paths to its image and audio files.
 
----
 ## Data Format
 
 Each item is stored as a JSON object.
@@ -164,7 +144,6 @@ Each item is stored as a JSON object.
 
 <details>
 <summary><strong>Field description</strong></summary>
-
 | Field                           | Type   | Description                                                  |
 | ------------------------------- | ------ | ------------------------------------------------------------ |
 | `id`                            | string | Unique item identifier. Items with the same prefix belong to the same scenario group. |
@@ -182,23 +161,21 @@ Each item is stored as a JSON object.
 | `category.modal_category.image` | string | Image ambiguity category.                                    |
 | `category.modal_category.audio` | string | Audio ambiguity category.                                    |
 
+
 </details>
 
----
 ## Loading the Dataset
 
-### Load JSONL metadata
+### Load JSON metadata
 
 ```python
 import json
 from pathlib import Path
 
-data_path = Path("data/triad.jsonl")
+data_path = Path("all-modality_ambiguity.json")
 
-items = []
 with data_path.open("r", encoding="utf-8") as f:
-    for line in f:
-        items.append(json.loads(line))
+    items = json.load(f)
 
 print(len(items))
 print(items[0]["question"])
@@ -212,8 +189,8 @@ from pathlib import Path
 root = Path(".")
 item = items[0]
 
-image_path = root / item["image"]
-audio_path = root / item["audio"]
+image_path = root / item["image"].replace("./", "")
+audio_path = root / item["audio"].replace("./", "")
 
 print(image_path)
 print(audio_path)
@@ -224,29 +201,20 @@ print(audio_path)
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("json", data_files="data/triad.jsonl")
+dataset = load_dataset("json", data_files="all-modality_ambiguity.json")
 print(dataset["train"][0])
 ```
 
----
 ## Dataset Statistics
 
-<div align="center">
-
-
 | Statistic                 | Value |
 | ------------------------- | ----: |
 | Number of items           |   327 |
 | Number of scenario groups |    86 |
 | Average group size        |   3.8 |
 
-</div>
-
 ### Group-size Distribution
 
-<div align="center">
-
-
 | Group size | Number of groups |
 | ---------: | ---------------: |
 |          2 |               23 |
@@ -254,22 +222,14 @@ print(dataset["train"][0])
 |          4 |               42 |
 |          8 |               10 |
 
-</div>
-
 ### Ambiguity-level Distribution
 
-<div align="center">
-
-
 | Ambiguity level | Number of items |
 | --------------- | --------------: |
 | `single_modal`  |             230 |
 | `dual_modal`    |              59 |
 | `tri_modal`     |              38 |
 
-</div>
-
----
 ## Ambiguity Taxonomy
 
 Every item receives one leaf-level ambiguity label for each modality.
@@ -307,14 +267,10 @@ Every item receives one leaf-level ambiguity label for each modality.
 | **Environmental** |    34 | Background sounds that change interpretation of the scene.   |
 | **Symbolic**      |    20 | Non-linguistic auditory symbols such as alarms, bells, ringtones, or notifications. |
 
----
 ## Recommended Evaluation Protocol
 
 The standard TRIAD evaluation provides the model with:
 
-<div align="center">
-
-
 | Component       | Included in standard evaluation |
 | --------------- | ------------------------------- |
 | Textual context | Yes                             |
@@ -323,8 +279,6 @@ The standard TRIAD evaluation provides the model with:
 | Question        | Yes                             |
 | Answer options  | Yes                             |
 
-</div>
-
 The model should output exactly one option key.
 
 ### Modality Conditions
@@ -364,7 +318,6 @@ Answer with a single option key.
 | Item-level accuracy  | Standard accuracy over all items.                            |
 | Group-level accuracy | Accuracy aggregated over scenario groups to reduce over-counting of sister examples. |
 
----
 ## Intended Uses
 
 TRIAD is intended for:
@@ -376,7 +329,6 @@ TRIAD is intended for:
 - comparing full-modality performance with modality-ablated settings;
 - analyzing which ambiguity types are hardest for different model families.
 
----
 ## Out-of-Scope Uses
 
 TRIAD is **not** intended for:
@@ -393,7 +345,6 @@ TRIAD is **not** intended for:
 
 > TRIAD is small and diagnostic by design. It should not be treated as a representative sample of real-world multimodal interactions.
 
----
 ## Data Collection and Annotation
 
 TRIAD was constructed by designing everyday scenarios in which text, image, and audio jointly determine the intended answer.
@@ -409,51 +360,44 @@ Each item is annotated with:
 
 Items were reviewed to reduce degenerate cases where the answer can be determined from only one or two modalities.
 
----
 ## Responsible AI Considerations
 
 <details open>
 <summary><strong>Privacy</strong></summary>
-
-
 The dataset is designed to avoid personally identifying information. Audio clips and images should not be used for identifying real individuals, voice matching, face recognition, or impersonation.
 
+
 </details>
 
 <details open>
 <summary><strong>Audio-specific risks</strong></summary>
-
-
 Because the dataset includes audio, it should not be used for biometric speaker recognition, voice cloning, speaker profiling, or identity inference. Audio is included only for evaluating multimodal reasoning.
 
+
 </details>
 
 <details open>
 <summary><strong>Sensitive content</strong></summary>
-
-
 The dataset avoids content involving violence, privacy violations, or sensitive personal information. Any remaining potentially sensitive cases should be treated as diagnostic examples only.
 
+
 </details>
 
 <details open>
 <summary><strong>Bias and limitations</strong></summary>
-
-
 TRIAD is a small diagnostic benchmark. Its examples are intentionally constructed around ambiguity and may not reflect the natural distribution of everyday multimodal data.
 
+
 Model performance on TRIAD should therefore be interpreted as a measure of ambiguity-resolution ability rather than general multimodal competence.
 
 The dataset contains English, Chinese, and mixed-language examples. Results may vary across languages and model families.
 
 </details>
 
----
 ## Licensing
 
 The license metadata is set to `MIT`. 
 
----
 ## Citation
 
 Citation information will be added after the double-blind review period.
@@ -467,14 +411,12 @@ Citation information will be added after the double-blind review period.
 }
 ```
 
----
 ## Maintenance
 
 The dataset will be versioned. Errata, corrected annotations, and future extensions will be documented in the repository release history.
 
 For the anonymous review version, contact information is withheld to preserve double-blind review. A public maintainer contact will be added after the review period.
 
----
 ## Double-blind Review Notice
 
 This repository is prepared for anonymous peer review. Please do not infer author identity from repository ownership, commit metadata, or temporary hosting information. Any non-anonymous information will be added after the review period.