---
license: mit
language:
- en
- zh
task_categories:
- visual-question-answering
- question-answering
task_ids:
- multiple-choice-qa
pretty_name: TRIAD
tags:
- multimodal
- omni-modal
- text-image-audio
- audio
- image
- text
- ambiguity
- benchmark
- evaluation
- neurips
- responsible-ai
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: all-modality_ambiguity.json
---
# TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models
[](#recommended-evaluation-protocol) [](#data-format) [](#dataset-statistics) [](#dataset-statistics) [](#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.
| | |
| ------------------- | --------------------------------------------- |
| **Task** | Multiple-choice tri-modal question answering |
| **Modalities** | Text + Image + Audio |
| **Languages** | English, Chinese, and mixed-language examples |
| **Instances** | 327 items |
| **Scenario groups** | 86 groups |
| **Answer format** | `option_A`, `option_B`, ... |
| **Primary use** | Held-out diagnostic evaluation |
| **Review status** | Anonymous version for double-blind review |
## Table of Contents
- [Overview](#overview)
- [What Makes TRIAD Different?](#what-makes-triad-different)
- [Repository Layout](#repository-layout)
- [Data Format](#data-format)
- [Loading the Dataset](#loading-the-dataset)
- [Dataset Statistics](#dataset-statistics)
- [Ambiguity Taxonomy](#ambiguity-taxonomy)
- [Recommended Evaluation Protocol](#recommended-evaluation-protocol)
- [Intended Uses](#intended-uses)
- [Out-of-Scope Uses](#out-of-scope-uses)
- [Data Collection and Annotation](#data-collection-and-annotation)
- [Responsible AI Considerations](#responsible-ai-considerations)
- [Licensing](#licensing)
- [Citation](#citation)
- [Maintenance](#maintenance)
- [Double-blind Review Notice](#double-blind-review-notice)
## What Makes TRIAD Different?
TRIAD is designed around **tri-modal irreducibility**.
| 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. |
The benchmark emphasizes audio cues that cannot be fully captured by a transcript, including:
- prosody and intonation;
- speaker-indexical cues;
- phonetic contrast and homophones;
- environmental sound;
- symbolic sound such as alarms, ringtones, and bells.
## Repository Layout
```text
TRIAD/
├── README.md
├── image/
│ ├── img_0001.jpg
│ ├── img_0002.jpg
│ └── ...
├── audio/
│ ├── aud_0001.wav
│ ├── aud_0002.wav
│ └── ...
├── metadata/
│ ├── attribution.json
│ └── 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.
```json
{
"id": "group_1_1",
"language": "en",
"question": "When is the school meeting scheduled?",
"context": "He told the parents to come to school for a meeting.",
"image": "image/img_0122.jpg",
"image_caption": "A picture of a calendar with Sunday circled.",
"audio": "audio/aud_0110.wav",
"audio_caption": "Someone said, 'Parents, please take note: remember to come to the school on this day.'",
"options": {
"option_A": "This sunday",
"option_B": "Next week",
"option_C": "The time is still unknown",
"option_D": "There is no meeting"
},
"answer": "option_A",
"category": {
"level": "single_modal",
"modal_category": {
"text": "Anaphoric",
"image": "Semiotic",
"audio": "Referential"
}
}
}
```
Field description
| Field | Type | Description |
| ------------------------------- | ------ | ------------------------------------------------------------ |
| `id` | string | Unique item identifier. Items with the same prefix belong to the same scenario group. |
| `language` | string | The language used in this data. |
| `question` | string | The question to be answered. |
| `context` | string | Textual context. |
| `image` | string | Relative path to the image file. |
| `image_caption` | string | Human-written image description. Metadata only unless caption-based evaluation is intended. |
| `audio` | string | Relative path to the audio file. |
| `audio_caption` | string | Human-written audio description or transcript-like summary. Metadata only unless transcript-based evaluation is intended. |
| `options` | object | Multiple-choice answer options. |
| `answer` | string | Gold answer key. |
| `category.level` | string | Overall ambiguity level: `single_modal`, `dual_modal`, or `tri_modal`. |
| `category.modal_category.text` | string | Text ambiguity category. |
| `category.modal_category.image` | string | Image ambiguity category. |
| `category.modal_category.audio` | string | Audio ambiguity category. |
## Loading the Dataset
### Load JSON metadata
```python
import json
from pathlib import Path
data_path = Path("all-modality_ambiguity.json")
with data_path.open("r", encoding="utf-8") as f:
items = json.load(f)
print(len(items))
print(items[0]["question"])
```
### Resolve media paths
```python
from pathlib import Path
root = Path(".")
item = items[0]
image_path = root / item["image"].replace("./", "")
audio_path = root / item["audio"].replace("./", "")
print(image_path)
print(audio_path)
```
### Load metadata with Hugging Face Datasets
```python
from datasets import load_dataset
dataset = load_dataset("json", data_files="all-modality_ambiguity.json")
print(dataset["train"][0])
```
## Dataset Statistics
| Statistic | Value |
| ------------------------- | ----: |
| Number of items | 327 |
| Number of scenario groups | 86 |
| Average group size | 3.8 |
### Group-size Distribution
| Group size | Number of groups |
| ---------: | ---------------: |
| 2 | 23 |
| 3 | 11 |
| 4 | 42 |
| 8 | 10 |
### Ambiguity-level Distribution
| Ambiguity level | Number of items |
| --------------- | --------------: |
| `single_modal` | 230 |
| `dual_modal` | 59 |
| `tri_modal` | 38 |
## Ambiguity Taxonomy
Every item receives one leaf-level ambiguity label for each modality.
### Text Ambiguity Categories
| Category | Count | Description |
| ------------- | ----: | ------------------------------------------------------------ |
| **Anaphoric** | 66 | Pronouns, demonstratives, ellipsis, or unclear reference. |
| **Syntactic** | 64 | Grammatical structure, attachment, or segmentation ambiguity. |
| **Logical** | 61 | Rules, conditions, negation, quantification, or option mapping. |
| **Pragmatic** | 51 | Intention, implicature, politeness, sarcasm, or indirect speech. |
| **Temporal** | 48 | Event order, tense, aspect, or completion-state ambiguity. |
| **Lexical** | 37 | Word sense, polysemy, homonymy, or translation ambiguity. |
### Image Ambiguity Categories
| Category | Count | Description |
| ----------------- | ----: | ------------------------------------------------------------ |
| **Semiotic** | 71 | Signs, gestures, symbols, expressions, or culturally mediated visual cues. |
| **Grounding** | 65 | Multiple possible visual referents or target objects. |
| **Spatial** | 61 | Position, orientation, depth, or spatial relation ambiguity. |
| **Kinematic** | 53 | A static frame of a dynamic action. |
| **Observational** | 41 | Viewpoint, occlusion, framing, or insufficient visual evidence. |
| **Contextual** | 36 | Uncertainty about scene type or situational context. |
### Audio Ambiguity Categories
| Category | Count | Description |
| ----------------- | ----: | ------------------------------------------------------------ |
| **Referential** | 98 | Spoken references, pronouns, kinship terms, or deictic expressions. |
| **Prosodic** | 84 | Stress, intonation, rhythm, sarcasm, or tone. |
| **Indexical** | 54 | Speaker cues such as age, gender presentation, role, or social relationship. |
| **Phonetic** | 37 | Homophones, minimal pairs, segmentation, or acoustic confusability. |
| **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:
| Component | Included in standard evaluation |
| --------------- | ------------------------------- |
| Textual context | Yes |
| Image | Yes |
| Audio | Yes |
| Question | Yes |
| Answer options | Yes |
The model should output exactly one option key.
### Modality Conditions
| Condition | Provided input |
| -------------------------- | -------------------- |
| **Full** | Text + Image + Audio |
| **Text only** | Text |
| **Image only** | Image |
| **Audio only** | Audio |
| **Text + Image** | Text + Image |
| **Text + Audio** | Text + Audio |
| **Image + Audio** | Image + Audio |
| **Question-only baseline** | Question + Options |
### Example Prompt
```text
You are answering a multiple-choice question that depends on three inputs:
a textual context, an image, and an audio clip.
Context: {context} Image: {image} Audio: {audio}
Question: {question}
Options:
(A) {option_A} (C) {option_C}
(B) {option_B} (D) {option_D}
Answer with a single option key.
```
### Metrics
| Metric | Description |
| -------------------- | ------------------------------------------------------------ |
| 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:
- evaluating omni-modal large language models;
- testing whether models genuinely integrate text, image, and audio;
- diagnosing modality reliance and modality neglect;
- studying ambiguity resolution in multimodal reasoning;
- 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:
- training or fine-tuning large models;
- speaker identification;
- voice biometrics;
- voice cloning or impersonation;
- face recognition or identity inference;
- surveillance;
- profiling individuals;
- evaluating human subjects;
- making decisions about real individuals.
> 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.
Each item is annotated with:
- a gold answer;
- an ambiguity level;
- one text ambiguity category;
- one image ambiguity category;
- one audio ambiguity category;
- group metadata linking sister examples.
Items were reviewed to reduce degenerate cases where the answer can be determined from only one or two modalities.
## Responsible AI Considerations
Privacy
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.
Audio-specific risks
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.
Sensitive content
The dataset avoids content involving violence, privacy violations, or sensitive personal information. Any remaining potentially sensitive cases should be treated as diagnostic examples only.
Bias and limitations
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.
## Licensing
The license metadata is set to `MIT`.
## Citation
Citation information will be added after the double-blind review period.
```bibtex
@misc{triad2026,
title = {TRIAD: Benchmarking Tri-Modal Irreducible Ambiguity Resolution for Multimodal Large Language Models},
author = {Anonymous},
year = {2026},
note = {Dataset submitted for double-blind review}
}
```
## 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.