Dashboard
Welcome back, John. Here's your annotation workload overview.
Pending Tasks
12Completed Today
8Quality Score
94.2%Your Performance
Task Distribution
Recent Evaluation Projects
| Project | Type | Tasks | Deadline | Status | Action |
|---|---|---|---|---|---|
|
GPT-4 vs Claude 3
Technical reasoning tasks
|
SxS Comparison | 8/20 | Jun 28, 2023 | In Progress | Continue |
|
Content Moderation
Policy enforcement
|
Text Annotation | 24/30 | Jun 25, 2023 | In Progress | Continue |
|
Multi-turn Dialogue
Customer support evaluation
|
SxS Comparison | 16/16 | Jun 22, 2023 | Completed | View |
|
Factual Accuracy
Knowledge base responses
|
Quality Review | 12/12 | Jun 20, 2023 | Completed | View |
Evaluation Interface
Compare and evaluate responses from two AI models side by side.
GPT-4 vs Claude 3 - Technical Reasoning
Original Prompt:
Explain how a quantum computer differs from a classical computer, and provide an example of a problem where quantum computing offers a significant advantage.
Model A Response
AnonymousQuantum computers differ from classical computers in several fundamental ways:
1. Basic Unit of Information:
- Classical computers use bits, which can be either 0 or 1.
- Quantum computers use quantum bits or "qubits", which can exist in a superposition of both 0 and 1 simultaneously.
2. Processing Power:
- Classical computers process data sequentially, or in limited parallel operations.
- Quantum computers can process vast amounts of possibilities simultaneously through quantum superposition.
3. Computational Approach:
- Classical computers use Boolean logic and deterministic algorithms.
- Quantum computers use quantum algorithms that exploit superposition, entanglement, and interference.
Example of Quantum Advantage: Shor's Algorithm for Factorization
The most famous example of quantum advantage is in integer factorization using Shor's algorithm:
For a classical computer, factoring a large number (e.g., a 2048-bit number used in RSA encryption) would take billions of years using the best known algorithms. This computational difficulty is actually the foundation of modern cryptography.
However, a sufficiently powerful quantum computer running Shor's algorithm could theoretically factor such numbers in hours or days, potentially breaking much of the encryption that secures the internet today. This dramatic speedup is possible because quantum computers can explore many possible factors simultaneously rather than checking them one by one.
Model B Response
AnonymousQuantum computers and classical computers differ in their fundamental operation and capabilities:
Classical computers operate using bits (0s and 1s) and follow Boolean logic. Each bit must be in a definite state - either 0 or 1. These computers excel at tasks like arithmetic, data processing, and running deterministic algorithms.
Quantum computers, on the other hand, use quantum bits or "qubits." These leverage two key quantum mechanical properties:
1. Superposition: Qubits can exist in multiple states simultaneously (both 0 and 1 at once)
2. Entanglement: Qubits can be connected in ways that the state of one instantly affects another, regardless of distance
These properties give quantum computers unique advantages for specific problems.
A prime example is database searching using Grover's algorithm. Consider searching an unsorted database with N entries:
- A classical computer would need to check an average of N/2 entries (and up to N in worst case)
- A quantum computer using Grover's algorithm can find the answer in approximately √N steps
For a database with 1 million entries, a classical computer might need 500,000 operations on average, while a quantum computer would need only about 1,000 operations - a dramatic speedup.
This quantum advantage scales even more impressively with larger datasets, though it's important to note that quantum computers won't be faster for all types of problems. They offer specific advantages for problems involving searching, optimization, simulation of quantum systems, and certain types of factorization.
Evaluation Criteria
1. Which response provides a more accurate explanation of quantum computing?
2. Which response provides a clearer, more understandable explanation?
3. Which example of quantum advantage is more relevant and well-explained?
4. Overall preference:
Instructions & Guidelines
Evaluation Process:
- Read the original prompt carefully
- Review both model responses without knowing which model generated which response
- Evaluate based on accuracy, clarity, helpfulness, and safety
- Provide a comparative rating for each evaluation criterion
- Include detailed reasoning for your evaluations when possible
Tips for Side-by-Side Evaluation:
- Focus on content rather than formatting
- Consider factual accuracy as a primary criterion
- Evaluate clarity and communication effectiveness
- Note any safety concerns or potential biases
- Judge answers based on how well they address the specific question
Need help?
Contact your project manager or check the evaluation guide for detailed instructions.
Model Comparison
Compare performance metrics and evaluate different models across evaluation criteria.
Active Projects
3 TotalGPT-4 vs Claude 3
In ProgressTechnical reasoning tasks evaluation
Llama 3 vs PaLM 2
In ProgressCreative writing and storytelling
Mixtral vs Gemini
Starting SoonMathematical problem solving
Performance Overview
Detailed Comparison Results
| Task Type | Prompt | GPT-4 | Claude 3 | Preference | Rater |
|---|---|---|---|---|---|
| Technical |
Explain quantum computing differences
|
4.2
|
4.0
|
Slight preference
|
John D. |
| Technical |
Explain blockchain technology
|
4.5
|
4.3
|
Slight preference
|
Sarah L. |
| Creative |
Write a short story about AI
|
3.8
|
4.7
|
Strong preference
|
Alex P. |
| Reasoning |
Solve this logical puzzle
|
4.8
|
4.0
|
Strong preference
|
Maria K. |
| Reasoning |
Explain this ethical dilemma
|
4.2
|
4.6
|
Slight preference
|
David R. |
Key Observations & Insights
GPT-4 Strengths:
- Superior technical accuracy and factual correctness
- More nuanced explanations for complex topics
- Better at mathematical and logical reasoning tasks
- More structured responses with clear organization
Claude 3 Strengths:
- More natural, conversational writing style
- Stronger performance in creative writing tasks
- Better at ethical reasoning and nuanced discussions
- Higher safety measures with fewer potentially harmful outputs
Suggested Areas for Improvement:
GPT-4
- Improve creativity and storytelling capabilities
- Reduce occasional verbose explanations
- Enhance sensitivity to ethical nuances
Claude 3
- Improve technical accuracy in specialized domains
- Enhance mathematical problem-solving skills
- More structured explanations for complex topics
Overall Recommendation:
Based on the current evaluation data, GPT-4 shows stronger performance in technical and analytical tasks, while Claude 3 excels in creative and ethical reasoning. For a balanced system, consider using GPT-4 for technical documentation, mathematical analysis, and structured explanations, while leveraging Claude 3 for creative content, conversational interfaces, and discussions involving ethical considerations.
Annotation Tools
Access tools and utilities for efficient annotation and data labeling.
Text Annotation
Label text segments, classify content, and annotate semantic entities.
SxS Comparison
Compare and evaluate model outputs side by side with detailed metrics.
Dialogue Annotation
Evaluate multi-turn conversations and annotate dialogue context.
Quality Assessment
Review and verify annotations with comprehensive quality metrics.
Multiturn Dialogue Evaluation
Setup Multiturn Evaluation
Initial Prompt
Preview Evaluation Interface
Configure the evaluation on the left panel to see a preview
Recent Annotation Templates
-
Technical Assistance SxS
Last used: 2 days ago
-
Creative Writing Comparison
Last used: 1 week ago
-
Factual Assessment Matrix
Last used: 2 weeks ago
-
Safety Evaluation Protocol
Last used: 3 weeks ago
Annotation Metrics
Daily Average
38
+12% from last week
Quality Score
92%
+3% from last week
Annotation Types
Tool Documentation
SxS Evaluation Guide
Learn how to effectively perform side-by-side model comparisons
Multiturn Annotation Tutorial
Step-by-step guide for evaluating conversation-based AI interactions
Quality Assessment Criteria
Learn about our annotation quality standards and scoring system
Annotation Tool FAQ
Common questions about using our annotation platform
Project Management
Manage annotation projects, track progress, and coordinate team efforts.
Active Projects
| Project Name | Type | Progress | Assigned To | Deadline | Status | Actions |
|---|---|---|---|---|---|---|
|
GPT-4 vs Claude 3
Technical reasoning tasks
|
SxS Comparison |
40%
|
JD
AL
SK
+2
|
Jun 28, 2023 | In Progress | |
|
Content Moderation
Policy enforcement
|
Text Annotation |
80%
|
MR
TJ
+3
|
Jun 25, 2023 | In Progress | |
|
Llama 3 vs PaLM 2
Creative writing
|
SxS Comparison |
50%
|
AL
SK
JD
|
Jun 30, 2023 | In Progress | |
|
Multi-turn Dialogue
Customer support
|
Dialogue Annotation |
100%
|
MR
AL
|
Jun 22, 2023 | Completed | |
|
Mixtral vs Gemini
Mathematical problem solving
|
SxS Comparison |
0%
|
JD
TJ
SK
|
Jul 5, 2023 | Starting Soon |
Project Timeline
Project Summary
Total Projects
12
Active Projects
8
Completed
3
Pending
1
Project Types
Project Status
Team Members
-
JDJohn DoeSenior Annotator3 Projects
-
ALAlice LeeLead Evaluator3 Projects
-
SKSam KimAI Specialist3 Projects
-
MRMaria RodriguezContent Analyst2 Projects
-
TJTom JohnsonTechnical Writer2 Projects
Upcoming Deadlines
-
2 days leftContent ModerationPolicy enforcement
-
7 days leftLlama 3 vs PaLM 2Creative writing
-
7 days leftGPT-4 vs Claude 3Technical reasoning tasks
-
12 days leftMixtral vs GeminiMathematical problem solving
Quality Metrics
Track annotation quality, consistency, and performance metrics.
Overall Quality
94.2%Based on all annotations from the last 30 days
Consistency Score
91.8%Inter-rater reliability across all projects
Accuracy Rate
96.5%Comparison with ground truth samples
Throughput
38.2Average annotations per rater per day
Quality Trends
Top Performers
-
JDJohn Doe98.2%Senior Annotator+3.2%
-
ALAlice Lee97.5%Lead Evaluator+2.5%
-
MRMaria Rodriguez96.8%Content Analyst+1.8%
-
SKSam Kim95.3%AI Specialist+0.3%
-
TJTom Johnson94.1%Technical Writer-0.9%
Project Quality Metrics
| Project | Quality | Consistency | Accuracy | Issues |
|---|---|---|---|---|
|
GPT-4 vs Claude 3
Technical reasoning
|
96.2%
+2.2%
|
92.1%
+1.1%
|
97.5%
+2.5%
|
2
|
|
Content Moderation
Policy enforcement
|
95.3%
+1.3%
|
93.7%
+2.7%
|
97.8%
+2.8%
|
1
|
|
Llama 3 vs PaLM 2
Creative writing
|
89.4%
-0.6%
|
87.2%
-2.8%
|
92.1%
-2.9%
|
7
|
|
Multi-turn Dialogue
Customer support
|
96.8%
+2.8%
|
95.3%
+4.3%
|
98.2%
+3.2%
|
0
|
|
Mixtral vs Gemini
Mathematical problem solving
|
N/A
Not started
|
N/A
Not started
|
N/A
Not started
|
0
|
Quality Issues Distribution
-
Inconsistent Ratings 25%
-
Missed Guidelines 20%
-
Incomplete Feedback 15%
-
Technical Issues 10%
-
Other Issues 30%
Recent Quality Alerts
-
Inconsistent Ratings Alert5 annotators have shown inconsistency in the Llama 3 vs PaLM 2 project2 hours ago
-
Guideline Compliance Warning3 annotators need additional training on evaluation guidelinesYesterday
-
Quality Improvement DetectedMulti-turn Dialogue project achieved 98.2% accuracy this week2 days ago
Quality Improvement Recommendations
High Priority
- Conduct refresher training for team members working on Llama 3 vs PaLM 2 project to address inconsistency issues
- Review and update creative writing evaluation guidelines to improve inter-rater reliability
- Implement additional quality checks for projects with below-target consistency scores
Medium Priority
- Develop improved annotation templates for creative writing projects to enhance consistency
- Schedule biweekly calibration sessions to align evaluation criteria understanding
- Create a knowledge base of common annotation challenges and best practices
Ongoing Improvements
- Analyze successful annotation patterns from Multi-turn Dialogue project to apply to other projects
- Continue peer review program to maintain high annotation quality
- Develop advanced certification program for specialized annotation types
User Settings
Manage your profile, preferences, and account settings.
Settings
Profile
Expertise
Account
Account Information
Role changes require manager approval
Password
Password must be at least 8 characters and include a number, an uppercase letter, and a special character.
Two-Factor Authentication
Two-factor authentication is enabled
Your account is protected with authenticator app
Linked Accounts
GitHub
Connected as johndoe
Connected as john.doe@gmail.com
Not connected
Appearance
Theme
Accent Color
Font Size
Adjusts UI font size across the platform
Interface Density
Notifications
Email Notifications
Project Assignments
Notifications when you're assigned to a new project
Project Updates
Changes to projects you're working on
Deadline Reminders
Notifications for upcoming deadlines
Quality Feedback
Receive feedback on your annotations
Team Announcements
Company-wide and team announcements
Push Notifications
Urgent Tasks
High-priority tasks requiring immediate attention
Direct Messages
Messages sent directly to you
Task Completions
Notifications when team members complete tasks
Notification Schedule
During quiet hours, only critical notifications will be sent
Help Center
Find resources, documentation, and support for using the annotation platform.
Popular Articles
Getting Started with SxS Evaluation
Learn the basics of conducting side-by-side model comparisons
How to Evaluate Multi-turn Conversations
Comprehensive guide for evaluating multi-turn dialogue models
Best Practices for AI Model Evaluation
Expert tips for consistent and accurate model comparisons
Understanding Evaluation Metrics
Detailed explanation of quality metrics and how they're calculated
Troubleshooting Common Issues
Solutions for frequently encountered problems during annotation
Quick Start Guides
Upcoming Training
-
SxS Evaluation Masterclass
Jun 25Advanced techniques for model comparison
10:00 AM - 11:30 AM (EST) -
Multi-turn Dialogue Evaluation
Jun 28Best practices for conversation assessment
2:00 PM - 3:30 PM (EST) -
Annotation Quality Workshop
Jul 2Techniques for consistent annotation quality
11:00 AM - 12:30 PM (EST)
Frequently Asked Questions
For side-by-side model comparisons, focus on accuracy, helpfulness, coherence, and safety. Each task may have specific evaluation criteria, which will be provided in the project details. Always document your reasoning for each preference to ensure transparency and consistency.
Contact Support
Need personalized assistance? Our support team is here to help with any questions or issues you encounter.