Dynabench

Dynabench
Overview
Full name	Dynamic Benchmarking Platform
Abbreviation	Dynabench
Description	A dynamic adversarial benchmarking platform that continuously evolves to challenge state-of-the-art AI models through human-in-the-loop data collection
Release date	2020-09
Latest version	2.0 (MLCommons)
Benchmark updated	2024
Authors	Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, And others
Organization	Meta AI (formerly Facebook AI), Now MLCommons
Technical Details
Type	Dynamic Benchmarking, Adversarial Evaluation, Human-in-the-Loop
Modality	Text, Natural Language
Task format	Adversarial examples, Human-model interaction
Number of tasks	4 core NLP tasks
Total examples	500,000+ adversarial examples
Evaluation metric	Dynascore, Task-specific metrics
Domains	NLI, QA, Sentiment Analysis, Hate Speech Detection
Languages	English (primarily)
Performance
Human performance	Baseline (easy for humans)
Baseline	Varies by task and round
SOTA score	Dynamic (continuously changing)
SOTA model	Varies by task
SOTA date	Continuous updates
Saturated	Never (by design)
Resources
Website	Official website
Paper	Paper
GitHub	Repository
Dataset	[Available through platform Download]
License	MIT
Predecessor	Traditional static benchmarks

Dynabench is a revolutionary artificial intelligence benchmarking platform that fundamentally rethinks how we evaluate machine learning models through dynamic, adversarial data collection with humans in the loop. Originally launched in September 2020 by Meta AI (formerly Facebook AI Research) and now maintained by MLCommons, Dynabench addresses the critical problem of static benchmarks becoming obsolete as models improve. The platform has collected over 500,000 adversarial examples from 1,800+ registered users, creating continuously evolving benchmarks that adapt to challenge state-of-the-art models while remaining easy for humans to solve.

Overview

Dynabench represents a paradigm shift from traditional static benchmarks to dynamic, evolving evaluation systems. Instead of fixed test sets that models can eventually overfit or memorize, Dynabench employs a continuous cycle where human annotators create adversarial examples specifically designed to fool current state-of-the-art models. This approach ensures that benchmarks remain challenging and relevant, providing a more accurate assessment of model capabilities in real-world scenarios.

Core Philosophy

The platform is built on four fundamental principles:

• **Dynamic Evolution**: Benchmarks that grow harder as models improve
• **Human-AI Collaboration**: Leveraging human creativity to find model weaknesses
• **Real-world Relevance**: Examples that are challenging for AI but intuitive for humans
• **Continuous Learning**: Creating a never-ending cycle of model improvement

Dynamic Adversarial Data Collection

The Human-and-Model-in-the-Loop Process

Dynabench's innovative approach centers on its human-and-model-in-the-loop methodology:

Stage	Process	Outcome
**1. Example Creation**	Human annotators craft inputs to fool models	Potential adversarial examples
**2. Real-time Testing**	Examples tested against live models instantly	Immediate feedback on success
**3. Validation**	Other humans verify example correctness	Quality-assured data
**4. Dataset Integration**	Successful examples join training data	Evolving benchmark
**5. Model Retraining**	New models trained on updated data	Improved robustness
**6. Cycle Repeats**	Process continues with stronger models	Continuous improvement

Round-Based Structure

Dynabench organizes data collection into progressive rounds:

Round	Target Model	Difficulty	Success Rate
**Round 1**	Single BERT-Large	Baseline	~50% fool rate
**Round 2**	Ensemble of RoBERTa-Large	Increased	~35% fool rate
**Round 3**	Advanced ensemble + adversarial training	High	~20% fool rate
**Future Rounds**	State-of-the-art models	Continuously increasing	Decreasing

Tasks and Domains

Natural Language Inference (NLI)

The platform's flagship task, featuring the Adversarial NLI (ANLI) dataset:

Aspect	Details
**Task**	Determine if hypothesis is entailed, contradicted, or neutral given premise
**Dataset Size**	169,265 adversarial examples across three rounds
**Key Finding**	GPT-3 performed no better than chance on ANLI
**Impact**	Led to significant improvements in NLI robustness

Question Answering (QA)

Challenges the notion of "super-human" performance on QA tasks:

Component	Description
**Base Model**	RoBERTa trained on SQuAD 1.1
**Challenge**	Despite high SQuAD scores, models fail on adversarial examples
**Focus**	Reading comprehension with misleading context
**Innovation**	Dynamic question generation targeting model weaknesses

Sentiment Analysis

Demonstrates that sentiment analysis is far from "solved":

Feature	Implementation
**Approach**	Naturalistic prompt sentences for diverse data
**Complexity**	Beyond simple positive/negative classification
**Challenges**	Sarcasm, context-dependence, subtle sentiment
**Dataset**	Continuously growing adversarial examples

Hate Speech Detection

Addresses critical safety and fairness concerns:

Aspect	Focus
**Objective**	Identify problematic content while avoiding bias
**Metrics**	Includes fairness evaluation alongside accuracy
**Challenges**	Context-dependent offensiveness, cultural sensitivity
**Importance**	Real-world safety applications

Technical Architecture

Platform Components

Dynabench's technical infrastructure consists of several integrated systems:

Component	Technology	Purpose
**Frontend**	React.js	Web interfaces for annotation and evaluation
**Backend**	Python REST API	Model submission and evaluation
**Database**	PostgreSQL	Example storage and versioning
**Model Serving**	Docker containers	Isolated model execution
**MTurk Integration**	Custom interface	Crowdsourced annotation

API and Integration

```python

1. Example Dynabench API usage

from dynabench import DynabenchClient

client = DynabenchClient(api_key="your_key")

1. Submit model for evaluation

model_id = client.submit_model(

   model_path="path/to/model",
   task="nli",
   description="My NLI model"

)

1. Get evaluation results

results = client.get_results(model_id) print(f"Dynascore: {results['dynascore']}") print(f"Round 1 Accuracy: {results['round1_acc']}") ```

Performance and Evaluation

Dynascore Metric

Dynabench introduces Dynascore, a holistic evaluation metric:

Component	Description
**Accuracy**	Task-dependent percentage of correct examples
**Compute**	Examples processed per second on evaluation cloud
**Memory**	Average memory usage in gigabytes
**Robustness**	Performance on typographical errors and paraphrases
**Fairness**	Bias and fairness measures

Model Performance Insights

Key findings from Dynabench evaluations:

Finding	Implication
Models with 90%+ static accuracy fail on 50%+ adversarial examples	Static benchmarks overestimate capabilities
Training on adversarial data improves both robustness and standard performance	Dynamic data benefits general performance
Human-model performance gap remains large on adversarial examples	Significant room for improvement
Different models fail on different adversarial strategies	Ensemble approaches valuable

Research Impact and Contributions

Published Research

Dynabench has contributed to numerous significant publications:

Paper	Year	Key Contribution
"Dynabench: Rethinking Benchmarking in NLP"	2021	Platform introduction and methodology
"Adversarial NLI"	2020	ANLI dataset and adversarial training benefits
"ANLIzing the Adversarial NLI Dataset"	2020	Fine-grained error analysis
"Learning from the Worst"	2021	Theoretical foundations of adversarial learning

Community Engagement

The platform has fostered significant community involvement:

• **1,800+ registered users** actively creating adversarial examples
• **500,000+ examples** contributed by the community
• **Multiple universities** using Dynabench for research
• **Industry adoption** for robust model development

Migration to MLCommons

Transition Details

In recent years, Dynabench transitioned from Meta AI to MLCommons:

Aspect	Change	Impact
**Governance**	Meta AI → MLCommons	Community-driven development
**Leadership**	Working group co-chairs	Broader representation
**Repository**	Facebook → MLCommons GitHub	Continued open-source access
**Future Development**	DataPerf competition integration	Expanded scope

Future Directions

Under MLCommons, Dynabench plans to:

1. **Expand task coverage** beyond the initial four NLP tasks 2. **Enable custom tasks** for specific domains 3. **Integrate with DataPerf** competition framework 4. **Develop multimodal capabilities** for vision and audio 5. **Enhance fairness evaluation** across all tasks

Advantages Over Static Benchmarks

Comparative Analysis

Aspect	Static Benchmarks	Dynabench
**Data Collection**	One-time	Continuous
**Difficulty**	Fixed	Adaptive
**Overfitting Risk**	High	Low
**Real-world Relevance**	Decreases over time	Maintains relevance
**Model Weaknesses**	Hidden	Continuously exposed
**Community Involvement**	Limited	Central to process

Key Innovations

Dynabench introduces several groundbreaking concepts:

• **First platform** to implement truly dynamic benchmarking
• **Model-in-the-loop** annotation for real-time adversarial creation
• **Virtuous cycle** of continuous model improvement
• **Sample efficiency** through targeted adversarial generation
• **Reduced annotation artifacts** compared to static collection

Limitations and Challenges

Current Limitations

Limitation	Description	Mitigation Strategy
**Language Coverage**	Primarily English	Expanding to multilingual
**Task Scope**	Limited to four NLP tasks	MLCommons expansion plans
**Annotator Expertise**	Requires training	Improved interfaces and guidance
**Computational Cost**	Real-time model serving expensive	Optimization and caching

Research Challenges

1. **Balancing difficulty** between challenging models and maintaining human solvability 2. **Preventing adversarial example memorization** in future training 3. **Ensuring diversity** in adversarial strategies 4. **Scaling to more complex tasks** beyond NLP 5. **Maintaining quality** as community grows

Significance

Dynabench represents a fundamental shift in how we approach AI evaluation, moving from static snapshots to dynamic, evolving challenges that better reflect real-world deployment scenarios. By combining human creativity with machine learning in a continuous feedback loop, the platform addresses critical limitations of traditional benchmarks: overfitting, staleness, and lack of robustness testing.

The platform's success in revealing weaknesses in models that achieve near-perfect scores on static benchmarks demonstrates the importance of adversarial evaluation. Moreover, Dynabench's finding that training on adversarial examples improves both robustness and standard performance suggests that dynamic benchmarking is not just about evaluation but also about driving genuine improvements in AI capabilities.

As Dynabench continues to evolve under MLCommons stewardship, it stands as a model for future evaluation platforms across AI domains, establishing principles and methodologies that will shape how we measure and improve artificial intelligence systems for years to come.

See Also

• Adversarial Machine Learning
• Natural Language Processing
• MLCommons
• Meta AI
• Human-in-the-Loop Learning
• Benchmark Saturation
• ANLI Dataset
• Dynamic Evaluation