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Cypher i18n Evals Instructions Doc
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Cypher Evals

Attempter Instructions

Project Overview

Overall Task Attempt Workflow

Task Overview

Detailed Explanation of Each Dimension with Examples

Step 1 - Rating Dimensions

1. Localization

2. Instruction Following

3. Truthfulness

Verifiable Facts

Misleading Information

3a. Instruction Following v/s Truthfulness

4. Conciseness

5. Writing Style and Tone

6. Harmlessness/Safety

When Model Claims to be Human or Have Emotions:

7. Overall Quality Score

Step 2 - Writing Justifications for Preference Ranking

Please write your justification in English

An example of a fantastic justification:

FAQ

Project Overview
Welcome to Cypher Evals. In this project, you will rate two model generated responses separately across various dimensions. Provide preference ranking scores. Write a justification for the response preference ranking and explain the specifics and logic behind your decision.

Overall Task Attempt Workflow
Step 1: Assess the Prompt Type

Step 2: Rate both responses for a given prompt  on the following dimensions:

Localization
Instruction Following
Truthfulness
Concision
Writing Style and Tone
Harmfulness
Overall Score
Step 2: Select a score on a Likert scale of 1-7 rating which response is the better of the two and how much better it is. Select if one response is objectively better than the other.

Step 3: Write a justification to back up your response ratings and score.


How to submit high-quality tasks:

Analyze the responses by taking into account whether they respond to the core goals of the prompt and have high-quality writing and informed content. Do not superficially look at and rank the responses.
While writing justifications, provide analyses that demonstrate your deeper understanding of the task. Do not blindly follow the justification structure given without providing evidence and supporting claims. Conciseness and writing quality are more important than the length of the rationale.


IMPORTANT! ChatGPT or other AI tools are NOT PERMITTED to create prompts or evaluate responses. Using AI tools will result in a flag on your account for removal from the project and can lead to eventual removal from the platform.

Task Overview

Step 1: Rating each model response according to the dimensions


There are 7 dimensions including an overall quality score and some direct questions about the content of the response.
The dimensions are:
Localization:  How well does the response adhere to cultural or localized context unique to the region where this language is spoken?
Instruction Following: How well does the response follow the instructions laid out in the prompt?
Truthfulness: How well does the response state information - are there any inaccurate statements or misrepresentations of the information?
Concision: How well is the response in terms of only having the essential and relevant details? Are there any unnecessary details, fluff, or pleasantries that add no value?
Writing Style and Tone: How well written is the response, in terms of whether it had an appropriate tone or formatting for the given prompt
Harmlessness: How well does the response do in regards to avoiding harmful, offensive, or inappropriate content?
Overall Quality: When rating the overall response quality, consider all the ratings you gave the response since the dimension ratings should align with the overall quality rating.

Rate each Dimensions according to:
1 - Major Issues
2 - Minor Issues 
3 - No Issues
(N/A for some)
Take a look at our in-depth rating rubric for our dimensions here.

Some dimensions will require a short justification
Stay brief and specific, only mention what is incorrect.
Do an overall score of the response.
Take all your individual ratings for the dimensions and provide an overall score for the response.


Step 2: Preference ranking on a 1-7 Likert Scale

Selecting a 1 or 7 means that the response you chose is much better than the other response
For example - One response has no key issues while the other has major issues

Select 2,3 or 5,6 if one response is better or slightly better.
Only relatively minor or subjective differences between the responses

These deviations should be expressed on the Likert scale based on errors in Truthfulness or Instruction Following as they would impact how you should score the responses overall when compared to each other.

Step 3: Writing a justification to explain the preference ranking


Look here for a full rundown of the requirements of a good justification.

A good justification should:
Provide sufficient evidence from the responses to explain the preference ranking
Follow these 4 requirements
Starts with a clear statement of the verdict
Mentions the 1-2 most critical reasons for the ranking with specific evidence from each response
Is typically 3-4 sentences
Is edited to trim down any unnecessary information or words

If multiple issues differentiate the quality of the 2 responses, then the focus should be on the most prominent ones.


Step 1 - Assess Type of Prompt
Type of Prompts
In the chart below, we cover the prompt categories for Cypher_. Please be prepared to receive tasks from any of these categories.


For certain dimensions like Instruction Following and Truthfulness, the ratings could vary if the Prompt has “Reference Text”


What is a Reference Text?

Reference text is a short article or body of text that you will upload to the model to test the model’s ability to comprehend the text and answer questions (your prompts) based on the text.


The below table will show you the Type of Prompts and if they have reference text.

In the task template we will specify the Prompt Type, so pay extra attention to the prompt type as rating dimensions may change


Prompt Type

Description

Examples

Has Reference Text?

Rewriting

Changing a body of text, potentially per specific instructions or qualifications


Note: We will provide the original text that gets rewritten

Rewrite this explanation in a way understandable to a 10 year old
Rewrite this text in the style of a haiku poem
Rewrite this text to be in active voice        
Yes

Classification

A particular instruction asking the chat assistant to classify data into one or more categories.


Categorize the following sentences into emotional categories: happy, sad, angry
[include sentences to categorize]
Categorize the following words into cities or cars
[include list of cars and cities to be categorized]
Yes

Summarization

Condense a body of text yet retaining its meaning. Often used with instructions or specific qualities


Note: We will provide the original text that gets summarized

Summarize this text into 3 lines so that each paragraph is a single line
Summarize this text into a 5 sentences in the style of Elmo
Summarize the text into five bullet points, with each bullet being exactly one sentence long
Yes

Extraction

Be able to understand a body of text and return certain portions of it

From the following article, extract the main ideas
From the following newspaper story, return the most important quotes
Yes

Closed Q&A

These tasks will ask a question that is in reference to a provided text. You should do your best to answer the question with the given text.


Note: We will provide the original reference text

[Referencing provided text] Who are the main characters in this text and why are they well liked by the protagonist?
[Referencing provided text] What are some popular vegetarian recipes and tell me a fun fact about their origin?
Yes

Brainstorming

Generating a list of ideas or explanations.

E.g. “Give me 5 ideas for interactive fiction related to an animal that escaped from a zoo”

“Give me 5 ideas for interactive fiction related to an animal that escaped from a zoo”
I want to organize a theme party, give me suggestions on what the theme should be
No

Chatbot

Interacting naturally with a chat assistant

You are Socrates and you will share your ideas about the topics you are known for        
No

Creative Writing

Creative writing prompts are open-ended tasks that allow for imaginative or descriptive responses without needing strict facts or instructions.

Write a fictional story about a young boy from a small town who was successful in a big city
Write a poem about women liberation
No

Open Q&A

Question-answering related to concepts defined in the prompt.

Tell me about the Greek hero who was shot in the ankle and died?"
No


Step 2 - Rating Dimensions
1. Localization

What is Localization?

Localization means the model’s response feels authentic and relevant to a specific region or country. This involves:

Reflecting Local Knowledge: Knowing key locations, customs, and events.
Understanding Regional Nuances: Adapting references to suit cultural norms.
Meeting Specific Needs: Addressing local laws, regulations, or processes.
Tailoring to Cultural Context: Ensuring the response is closely adapted to the cultural background of the target audience.

Key Localization Elements to Rate


Local Locations - Check if the response accurately refers to places, landmarks, or regions relevant to the target country.
Example:
Good Localization: Refers to “Shinjuku” when discussing popular districts in Tokyo.

Poor Localization: Refers to a generic “shopping district” without specificity, missing regional context.

Local Events - Verify if the response includes relevant festivals, holidays, or seasonal events.
Example:
Good Localization: Mentions “Carnaval” for Brazil or “Diwali” for India.

Poor Localization: Refers to general “holiday season” instead of a culturally relevant celebration.

Customs and Norms - The response should demonstrate an understanding of the local way of life, including manners, values, and social practices.
Example:
Good Localization: Shows awareness of personal space norms or greeting customs.

Poor Localization: Suggests behavior that contradicts or is insensitive to cultural practices.

Regulatory, Legal, or Community Processes - Responses should respect and reference regional regulations, legal frameworks, and community protocols.
Example:
Good Localization: Mentions EU GDPR regulations in a privacy context for European audiences.

Poor Localization: References non-applicable legal standards, ignoring local compliance

Requirements.


How to Rate Localization:

The key question to ask: "How well does this response demonstrate cultural relevance and alignment with the specific local context of the target audience?"


The response is generic, missing any cultural or localized context unique to the region where this language is spoken. Rate it as No
The response includes perfect cultural or localized context relevant to the region where this language is spoken. Rate is as Yes

Some more Examples for Localization:

Use of Appropriate Greeting Styles:
Example: In Japan, it might be culturally respectful to use polite language and honorifics (like "-san" or "-sama") when addressing someone, whereas in the United States, a first-name basis is often more acceptable and friendly.
Relevant Language Variants:
Example: Using specific language forms, like Spanish “vosotros” in Spain but avoiding it in Latin American Spanish, where “ustedes” is preferred. Similarly, understanding dialectal words, like using “lift” (UK) instead of “elevator” (US).
Example: Using specific spellings like ‘behaviour’ or ‘programme’ for Commonwealth English locales.
Cultural Sensitivity to Dietary Customs:
Example: When suggesting food options, a response tailored to Middle Eastern audiences may consider offering halal choices, while a response for Indian audiences might provide vegetarian options, given dietary customs in those regions.
Acknowledging Social Hierarchies and Communication Styles:
Example: In many East Asian countries, deference to authority and indirect communication are common. A tailored response for a professional setting in these regions might use language that is more deferential, whereas in Western cultures, direct communication may be seen as clearer and preferred.
Localized Family or Social Structures:
Example: In cultures where family structures are central, such as in Latin America, a response about work-life balance might emphasize time with family, while in more individualistic cultures, the emphasis might be on personal growth and hobbies.
Respecting Religious or Spiritual Beliefs:
Example: A response for an audience in the Middle East may avoid references to weekend activities on Fridays, considering Friday as a day of prayer, while in Western contexts, the weekend is usually considered Saturday and Sunday.
Incorporation of Localized Etiquette and Social Practices:
Example: A response for audiences in France might include references to the importance of mealtime etiquette or traditional greeting styles (like a light cheek kiss), whereas such references might not be suitable for North American audiences.
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2. Instruction Following
Please note: If you have worked on other rating projects you are fairly familiar with rating Instruction Following and Truthfulness. However in this project with the inclusion of Reference Text, there are certain things you need to keep in mind.


Assessing Instruction Following is a critical part of RLHF tasks. Ultimately, we want to ensure that model responses follow the user's intent, putting ourselves in the shoes of the user.
We think about Instruction Following in two ways:
Prompt Request Coverage
Constraints

Understanding Prompt Coverage

Coverage is simply an assessment of whether the generated response did everything the prompt asked it to do, even if it's implicit.
Prompt: "Provide me some of the main causes of air pollution and describe their impacts."
Bad Example: "Air pollution is bad. It has many causes."
Explanation: This response does not address the explicit request to provide causes of air pollution and describe their impacts.

Understanding Constraints

Constraints in a prompt are rules, conditions, or limits that help guide the AI's response. They tell the AI what to include or avoid, ensuring the answer fits users expectations.
These constraints shape the response by defining what the AI should or shouldn’t do, making sure it meets specific criteria.
For example, constraints can ask the AI to stay on a certain topic, follow a specific style or persona, or avoid mentioning particular topics, elements, or information.

Let's look at an example


Prompt: Explain quantum physics like I'm 5 years old.

Good Example

Response: Quantum physics is about how really tiny things, like the building blocks of everything around us, behave. Imagine everything is made up of little invisible pieces called particles. These particles don’t follow the normal rules, like a ball or a car does. They can be in two places at the same time, or they can act like a wave in the water. It’s like magic rules for the smallest things in the world! Scientists study these strange rules to understand how everything works deep down.

Explanation: To explain quantum physics for a 5-year-old, the model used simple language and relatable examples, like waves in water and invisible building blocks, it avoided technical terms. The explanation was kept short and focused so it’s easy for a young child to grasp, emphasizing the "magic" of how tiny things behave differently.


Bad Example

Quantum physics is a branch of science that deals with the behavior of particles at the atomic and subatomic level. It explores concepts such as wave-particle duality, the uncertainty principle, and quantum entanglement. These phenomena are described using complex mathematical models and require an understanding of advanced physics to fully grasp.

Explanation: This is a bad example of not following the instructions, as it uses complex terms like "wave-particle duality" and "uncertainty principle" that would be too difficult for a 5-year-old to understand. It’s also too technical and long, ignoring the need for simplicity and clarity.


Remember the key question to ask: Does the response address the explicit and implicit asks in the prompt?


Assessing Instruction Following with Reference Text:

When evaluating Instruction Following, the key focus is on how well the model adheres to the specific requests and constraints outlined in the prompt. For tasks involving reference text, the model must accurately follow the instructions, particularly when it comes to using the provided information and meeting any explicit or implicit constraints.

Guidelines for  Instruction Following:

Failure to Execute the Constraints: If the model fails to adhere to constraints (e.g., listing items, answering within a given scope), it should be rated as having issues for Instruction Following. For example, if the prompt asks for 5 items but the model provides only 3, this would be a minor issue for Instruction Following.
For Closed QA/Classification user requests, if the response is attempting to answer the question, it is following the instructions even if the answer is wrong.
Reference Text Constraints: If the prompt explicitly or implicitly asks the model to stick to the reference text, and the model introduces information not contained in the text, this would also result in a failure to follow instructions. For instance, if the prompt requests a list of countries from the reference text, and the model provides countries that are not in the text, it’s an Instruction Following issue.
Handling Out-of-Scope Information: If the prompt requests information beyond what is in the reference text, the model should not be penalized for providing extra information. Please always evaluate it in the context of the prompt.
Prompt Category

Instruction Following

Content Extraction

Did the model successfully execute on the asks and constraints of the prompt?
Is the model pulling in external information when it should not be?
Summarization

Summarization means that the key points of the text are maintained in the summary

Did the model successfully summarize as per the constraints/core asks?
Rewrite

It can sometimes be the case where the rewrites asks for net-new factual information to be pulled in - that’s okay


Make sure to distinguish between net-new factual information, substitution of incorrect information, stylistic/language/tone changes, and fluff/creative elements

Did the model rewrite according to the constraints/core asks?
Did the prompt explicitly request the model to stay within the bounds of the reference text or did the prompt explicitly request the inclusion of net new information? If so, did the response correctly follow through on these?
Closed QA/Classification

Did the model attempt to answer the question or classify based on the reference text?
Are there any other constraints (usually around formatting) and did the model successfully execute on those constraints?

How to Rate Instruction Following

No Issue: The model perfectly follows the core request and all constraints, with no departure from the prompt or reference text.
Example:

Prompt: "List 3 animals from the reference text that start with 'B"
Reference Text: "Animals starting with B: Bear, Buffalo, Bat, Baboon, Bison, Blue Whale, Bald Eagle, Beetle, Barracuda, Boa, Butterfly, Beluga Whale, Blackbird, Binturong."
Model Response: "Bear, Buffalo, Bat."
Instruction Following: No Issue
Explanation: The model perfectly follows the instruction and uses the reference text correctly
Minor Issue: The model makes small mistakes or strays slightly from the instructions, but the overall task is largely fulfilled correctly.

Prompt: "List 3 animals from the reference text that start with 'B"
Reference Text: "Animals starting with B: Bear, Buffalo, Bat, Baboon, Bison, Blue Whale, Bald Eagle, Beetle, Barracuda, Boa, Butterfly, Beluga Whale, Blackbird, Binturong."
Model Response: "Bear, Buffalo, Beaver."
Instruction Following: Minor Issue 
Explanation: The response follows most of the instructions but introduces an item, Beaver, which was not in the reference text
Major Issue: The model fails to follow the core request or significantly violates the constraints, providing a response that misrepresents the task or fails to use the reference text properly.

Prompt: "List 3 animals from the reference text that start with 'B"
Reference Text: "Animals starting with B: Bear, Buffalo, Bat, Baboon, Bison, Blue Whale, Bald Eagle, Beetle, Barracuda, Boa, Butterfly, Beluga Whale, Blackbird, Binturong."
Model Response: "Tiger, Lion, Elephant."
Instruction Following: Major Issue 
Explanation: The model fails to follow the core request and lists animals that neither start with 'B' nor come from the reference text
Instruction Following Examples















I


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3. Truthfulness
When training state-of-the-art AI models, it’s extremely important to address factual errors or misinformation. LLMs are known to "hallucinate" or make up information that isn't true. It is your job to determine if a model response contains any factual inaccuracies.

Truthfulness measures how accurate the information in the response is with respect to what the model claims. Rate Truthfulness based on the response itself, regardless of the prompt.

We think about Truthfulness in two ways:

Verifiable Facts
Misleading Information
Verifiable Facts
What’s a Verifiable Fact?

A verifiable fact is something that is true (or false) regardless of personal feelings, interpretations, or opinions. It can be confirmed by evidence or observation that is consistent and repeatable.
How to Spot a Verifiable Fact

You can identify a verifiable fact by asking yourself: "Is this statement or assertion definitively true or false? " Opinions, perspectives, feelings, etc. are not verifiable facts.
How to Validate Whether a Verifiable Fact is True or False

Use your search engine of choice. Most of the time you will be able to clearly tell whether the fact is true or not. If you can’t validate one way or the other, chances are the statement or assertion is not a verifiable fact.
Check Your Sources: Make sure that the search evidence comes from a reliable source:
Examples of Reliable Sources
Wikipedia, High-quality online publications like ProPublica, Academic institutions, Official organizations (e.g. CDC, WHO), News sites, Books
Examples of sources to avoid 
Social media sites (e.g. Facebook, LinkedIn, and Twitter), Blog posts, Sites with fun fact lists, Opinion sites/Q&A sites (e.g. Reddit, Quora)
Misleading Information
What is Misleading Information?

We define Misleading Information as anything presented as a verifiable fact that cannot actually be verified. Model responses may present opinions as verifiable facts, or assert as fact something that has no proof of being true (or false).
How to Spot It

Look for Extreme Words or Superlatives: Words like "always," "never," or "all" can be signs of overgeneralization.
Compare and Contrast: See if other reliable sources say the same thing. You might be dealing with misleading info if they’re not
If You Can't Verify the Statement Quickly: check if the statement is true or false. If you aren’t able to verify the information one way or the other, it is likely a sign that it is not a verifiable fact and that the model may have presented misleading information.
Rating framework for Truthfulness

If a response is 100% truthful → No Issues
If a response is not 100% truthful, but the inaccuracies don't impact your overall goal as a user → Minor Issues
Otherwise → Major Issues.

Factuality checker tool, CoPilot

Most claims the responses make can and should be verified. (This does not apply to creative writing prompts.)
To cut down on research time, you have a factuality checker tool, CoPilot.
To Use:
Highlight the text of the claim the response is making.
Click the magnifying glass icon.
Wait for CoPilot to tell you if that claim is truthful or not.
When you select Minor/Major Issues for the Truthfulness dimension, a text box will appear where you need to provide a brief explanation of what the inaccuracies in the response are.
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Assessing Truthfulness with Reference Text:

Truthfulness tests whether the model response is factually and contextually correct. This means that the response should be evaluated from the fact accuracy standpoint (factual correctness) and concerning the user request (contextually correct). It’s important to note that you do not need to validate the reference text for glaring issues unless there are obvious inaccuracies (in which case pick a different reference text).

Universal Checks
Factual information from inside the reference text: For information that's pulled from the reference text, we can assume it is true.
For Summarization and Rewrite, confirm that the response does not alter the meaning and key details of the original text
For Closed QA, verify the model answered correctly based on the reference text
Opinions and Creative fictional information: Non-verifiable information does not have any claims to validate so this would be considered Not Applicable.
Category-specific Checks
Classification: Verify the model classified the information correctly. The model may need to draw on its knowledge base to make the correct classification.
Content Extraction: Confirm the model followed the prompt instructions accurately. (Yes, this overlaps with Instruction Following)
FAQ/Edge Cases

How do we define factual information?
Factual information is information that is verifiable. In other words,  the statement can be proven false or true. This is different from  stylistic/language/tone information and fluff/creative elements.


What happens if there is brand new factual information in the model response that’s not in the reference text?
Verify whether answering the prompt requires extra facts beyond the reference text. If so, verify these new facts are accurate. This is dependent on the prompt. Note that, for Summarization prompts, model responses will usually never need to take in information from outside the reference text 

What happens if the model corrects information between the reference text and the model response?

The reference text may have errors - some could be blatant, some could be more subtle.

For blatant errors in the reference text, please choose a different reference text.
For subtle errors in the reference text, the model may or may not fix them in the model response. If it tries to fix them, then the fix should be a) correct b) flagged to the user in the model response. (e.g., “Germany's chief export is medicine” changed to “Germany's chief exports are automobiles and automobile parts (the reference text was incorrect)”). Both a) and b) need to be true to be error-free.
Here is a chart with examples  -

Prompt and Reference Text

Model Response

Truthfulness

Pull the City-Country pairs in the text.


The world is filled with so many sights to see. Any traveler should go to Christ the Redeemer in Rio de Janeiro, Brazil, the Eiffel Tower in Paris, France, and the Taj Mahal in Agra India. For a more niche destination, check out Sintra in Spain.



Rio de Janeiro, Brazil
Paris, France
Agra, India

Sintra, Portugal  (Note: The country was incorrect in the original text).

No Truthfulness Issues because the model (i) called out that the reference text was wrong (ii) correctly fixed the mistake in the reference text

Rio de Janeiro, Brazil
Paris, France
Agra, India

Sintra, Portugal

Truthfulness Issue because the model correctly fixed the inaccurate fact in the reference text but did not call out that it made the fix.

Rio de Janeiro, Brazil
Paris, France
Agra, India

Sintra, France

Truthfulness Issue because the model tried to correct a fact in the reference text but did so incorrectly. In this case, since the model did attempt to make a correction, we ask that you validate whether the correction was done properly.

Rio de Janeiro, Brazil
Paris, France
Agra, India

Sintra, Spain

No Truthfulness Issues because Sintra’s true country is not well known. We don’t ask you to validate the reference text for accuracy and you should treat the reference text as the source of truth. As a result, it doesn’t matter if the model didn’t find and correct the mistake.



How to Rate Truthfulness

No Issue: The model’s response is completely accurate and aligned with the reference text.
Example:
Prompt: "List 5 countries from the reference text that start with 'C'."
Reference Text: "Countries starting with C: Canada, China, Chile, Colombia, Croatia."
Model Response: "Canada, China, Chile, Colombia, Croatia."
Truthfulness: No Issue
Explanation: Model picked accurate information from the reference text

Minor Issue: The response contains minor factual inaccuracies, fabrications, or a fundamental failure to adhere to the reference text.
Example:
Prompt: "Classify the Presidents as 21st century or 20th century"
Reference Text: "Trump, Obama, FDR, JFK"
Model Response: "Trump (20th century), Obama (21st century), FDR (20th century), JFK (20th century)."
Truthfulness: Minor Issue 
Explanation: Trump was president in the 21st century - the model got 25% of the options in the list incorrect.

Major Issue: The response contains significant factual inaccuracies, fabrications, or a fundamental failure to adhere to the reference text.
Example:
Prompt: "Which country in the list below has the longest name?”
Reference Text: "Countries starting with C: Canada, China, Chile, Colombia, Croatia."
Model Response: "China"
Truthfulness: Major Issue 
Explanation: China does not have the longest name in the list below, it is Colombia
Key Question to ask: Is the information provided in the response factually correct?


Truthfulness Examples

















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3a. Truthfulness v/s Instruction Following - What's the deal? Save this chart

Prompt Category

Instruction Following

Truthfulness

Content Extraction

Execution Correctness: Confirm the model followed prompt instructions accurately. (Yes, this overlaps with Truthfulness)

Execution Correctness: Confirm the model followed prompt instructions accurately. (Yes, this overlaps with Truthfulness)

Summarization




Execution Correctness: Confirm the model followed prompt instructions accurately. This means did the model try to summarize? AND did the model have prompt request coverage and satisfy the constraints?

For Summarization, confirm that the response does not alter the meaning and key details of the original text. Model responses will usually never need to take in information from outside the reference text. For information that's pulled from the reference text, we can assume it is true.

Rewrite



Execution Correctness: Confirm the model followed prompt instructions. This means did the model try to rewrite? AND did the model have prompt request coverage and satisfy the constraints?

For Rewrite, confirm that the response does not alter the meaning and key details of the original text.


For information that's pulled from the reference text, we can assume it is true (unless it’s blatantly false, in which case, please use a different reference text).


For information that’s from outside of the reference text, we need to verify whether answering the prompt requires extra facts beyond the reference text (sometimes the model may pull in additional information when the reference text is sufficient). If so, verify these new facts are accurate.

Closed QA

Execution Attempt: Did the model attempt to answer the question based on the reference text (does not need to be the correct answer)?


Constraint Correctness: Are there any other constraints (usually around formatting) and did the model successfully execute on those constraints?

For Closed QA, verify the model answered correctly ONLY based on the reference text.

Classification

Execution Attempt: Did the model attempt to classify based on the reference text (does not need to be the correct classification)?


Constraint Correctness: Are there any other constraints (usually around formatting) and did the model successfully execute on those constraints?

For Classification, verify the model classified correctly - it may need to draw upon information not in the reference text .

Open QA

Execution Attempt: Did the model attempt to answer the question (does not need to be the correct answer)?



For Open QA, verify the model answered correctly - make sure all details are correct

Brainstorming

Execution Attempt: Did the model attempt to brainstorm ideas that relate to what the user was asking for ( ?


For Brainstorming, verify the model answered correctly - make sure all details are correct


Below are some examples of different scenarios where Instruction Following and Truthfulness ratings would change based on the model response.



Prompt type

Example Prompt

Scenario

Response

Instruction Following

Explanation

Truthfulness

Explanation

Summarization / Rewrite

“Rewrite this as if your were a 5th grader”


[Includes Reference Text about the Manhattan project]

1a

- Rewrites in the language/tone of a 5th grader but leaves out information from the original reference text

No Issue

The model rewrote as per the constraint of "as if you were a 5th grader"

Minor Issue

The rewrite was not grounded in the original reference text because it left out information

1b

- Rewrites but is in the language/tone of a college student

Issue

It did not follow the constraint of "as if you were a 5th grader"

No Issue

The rewrite is grounded in the reference text

1c

- Rewrites in the language/tone of a 5th grader but pull in extra information from outside the original reference text

No Issue

The model rewrote as per the constraint of "as if you were a 5th grader"

Minor Issue

The rewrite was not grounded in the original reference text because it added net-new information from outside of the reference text

Classification / Closed Q&A

“Classify the NBA players as former or current”


[Includes Reference Text about Team USA at the Paris Olympics winning Basketball Gold


Note: There is no mention about current/former NBA player status in the reference text. In this situation, we recommend that the raters should do external research to verify the status of the NBA players mentioned."

2a

Fails to list all NBA players from the reference text but correctly classifies the ones it lists as former or current.

Minor Issue

Did not adhere to the implicit request to stick to players in the text. The request is implicit because it did not state ("from this text") and just had the reference text. In general, model should assume it's meant to pull from the reference text if it's there.

No Issue

Classified the players correctly

2b

Lists all NBA players from the reference text but misclassifies them as former or current.

No Issue

The model performed the classification task, just not truthfully. IF should generally cover something readily verifiable given prompt and response, without extensive research (that falls in the truthfulness bucket).

Issue

Misclassified players

2c

Includes NBA players not mentioned in the reference text but correctly classifies them as former or current. No explicit claims made about the list (e.g. Here is a list of all the NBA players...)

Minor Issue

Did not adhere to the implicit request to stick to players in the text. The request is implicit because it did not state ("from this text") and just had the reference text. In general, model should assume it's meant to pull from the reference text if it's there.

No Issue

Classified the players correctly

Take a closer look at partnerships formed between security teams and firms in 2023. Conclude by classifying the original intent of the partnership under either 'Preventive Collaboration' or 'Responsive Alliance'.


Cybersecurity Incident Response Manual for Tech Companies

By the 2020s, the average breach cost rose to $4.24 million, highlighting the need for more robust defenses. CISOs began requesting advanced threat detection systems in the same decade. These systems became essential to their successors, who planned to quickly mitigate breaches by implementing zero-trust architectures in response to the rise of ransomware attacks. To effectively neutralize threats, security teams formed partnerships with leading cybersecurity firms in 2023. The first result of this collaboration was an AI-driven threat intelligence platform, deployed within two years, known as the "Omega System," designed to disguise its purpose as a proactive defense tool.

3

Incorrectly classifies the original intent of the 2023 partnership as "Preventive Collaboration" instead of "Responsive Alliance."

No Issue

The model performed the classification task, just not truthfully. IF should generally cover something readily verifiable given prompt and response, without extensive research (that falls in the truthfulness bucket).

Issue

Made the wrong inference based on the reference text; did not execute the classification correctly.


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4. Conciseness
The Users using the model we’re developing are likely busy and distracted. They need quick answers, or they may lose interest. The Conciseness dimension evaluates whether the model delivers responses that meet this need.

It measures how effectively a response provides relevant information, giving just the right amount needed in a clear and concise way.

Key things to consider when rating Conciseness:

Relevancy - The response should include only information relevant to the prompt, directly addressing the user’s request without veering off-topic.
Good Example: If asked for “steps to reset a password,” the response includes only the necessary steps.
Bad Example: A response that includes unrelated details, such as “reasons for forgetting passwords,” is not concise.
Speed to the answer - The response should get the user what they need quickly (ideally in the first 1-2 sentences). This way, the user can get an overview of their answer upfront and then decide which of the following bullet points or focus areas to focus on.
Good Example: Key steps to obtaining your passport are identifying the nearest consulate, preparing the application form, getting pictures taken, and sending them in at least 60 days ahead of travel for processing:
Identifying the nearest consulate: …
Preparing the application form: …
…
Okay Example: Here are 5 things to think about to get your passport renewed…
Bad Example: Of course! I’d be happy to help you get your passport renewed, here’s what you should think about:
Repetition - A concise response avoids repeating the same information or rephrasing the prompt unnecessarily
Good Example: The response answers directly without repeating parts of the question or summarizing already provided information.
Bad Example: The response rephrases the question (“To reset your password, here’s what you need to do to reset it”) or repeats steps.
Focus: A focused response should avoid unrelated anecdotes, excessive jargon, unnecessary background information, filler words, or repetition that might distract from the core information requested.
Good Example: The response is clearly structured upfront so the user gets a complete overview of the answer very quickly. E.g., “Key considerations when looking to buy an electric vehicle are range, cost, infrastructure needed to charge the vehicle, as well as more standard considerations such as comfort, handling, and seating and storage capacity.” The response should then follow this structure
Bad Example: The response is not well-structured and includes multiple additional items that are not indicated upfront, for example ‘another thing to think about’ or ‘also, you could consider’.
Length - The response should meet any explicit length requirements provided by the user. If no length is specified, it should be appropriately short, covering the answer directly without excessive or insufficient length.
Good Example: For a simple question, a response is one or two sentences if that’s sufficient to cover the answer.
Bad Example: The response is either overly detailed with background information or too brief, missing key details needed for a full answer.
Intent - The response should align with the prompt's primary intent, without adding extra or tangential information.
Good Example: If the prompt asks for a list of steps, the response lists them without additional commentary.
Bad Example: Adding suggestions, opinions, or unrelated information not explicitly requested goes against conciseness.
How to Rate Conciseness

Example Prompt: “What are the benefits of electric vehicles?”

Conciseness: No Issues
Model Response: "Electric vehicles reduce emissions, lower fuel costs, and require less maintenance than gas-powered vehicles."
Explanation: This response is concise and covers the main benefits directly without extra detail. It is relevant, focused, and the right length.

Conciseness: Minor Issues
Model Response: "Electric vehicles, often abbreviated as EVs, have several benefits. They help reduce emissions, lower fuel costs, and require less maintenance than traditional gas-powered vehicles. These benefits are why many people are choosing EVs today."
Explanation: This response is mostly concise but has minor issues:
Focus: The phrase "often abbreviated as EVs" and "These benefits are why many people are choosing EVs today" are unnecessary and slightly detract from conciseness.
Length: The response could be shorter by omitting minor details.

Conciseness: Major Issues
Model Response: “Electric vehicles (EVs) are a popular choice in today's world, especially among people who are environmentally conscious. EVs have a variety of benefits that make them an attractive option for modern consumers. One major advantage is that they reduce emissions, which is beneficial for the environment and helps combat climate change. Additionally, they are cost-effective in the long run, especially due to lower fuel costs and fewer maintenance needs compared to traditional gasoline-powered cars, which have more complex engines. People are also increasingly interested in EVs because of the convenience of charging them at home, avoiding gas stations. Many countries are also pushing for cleaner energy options, and EVs are a significant part of this movement."
Explanation: This response has several major conciseness issues:
Focus: Contains unrelated information (e.g., environmental consciousness, consumer trends, home charging convenience, and government policies), which distracts from the main question.
Length: Much longer than necessary, with redundant details and excessive background information.
Relevancy: Although related, many details (such as charging convenience and government policies) are tangential to the primary focus on benefits.

Key Question to ask: "Does this response give the user relevant information quickly and clearly (i.e., concisely), without adding unnecessary details?"


Conciseness Rating Rubric


Dimension

1 (Major Issues)

2 (Minor Issues)

3 (No Issues)

Conciseness

The extent to which a response addresses the users’ needs in an efficient way, neither being too excessive or too short (terse). The extent to which explicit length instructions are adhered to.

Focus - The response contains significant unrelated anecdotes, excessive jargon, unnecessary background information, filler words, and repetition that distract from the requested information. It is not well-structured and adds on multiple ‘additional things to think about’ after answering the user’s prompt.
Length - The response significantly misses any explicit length requirements specified by the user. If no requirements are provided by the user, the response is clearly too long or too short (9/10 people would agree)
Often, but not always, a response with more than 8 top-level bullet points is longer than it needs to be.
Often, but not always, a ‘wall of text’ which requires a user to read every last word to get their answer is not concise
Focus - The response contains minor unrelated anecdotes, excessive jargon, unnecessary background information, filler words, and repetition that do not distract from the requested information
Length - The response slightly misses explicit length requirements by a small margin that would not impact the user’s satisfaction in the response. If no requirements are provided by the user, the response is arguably too long or too short
Focus - The response focuses on the user prompt and gets the user what they need quickly (ideally by leading with the answer in the first 1-2 sentences). It is well-structured, stays focused on the requested information, and is free from distractions or unnecessary ‘add-ons’.
Length - The response respects any explicit length requirements specified by the user. If no requirements are provided by the user, it strikes a balance between providing essential information (need-to-know) and additional context (nice-to-know). It is neither too long nor too short.

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5. Writing Style and Tone
Writing Style in AI communication is a critical aspect that influences clarity, comprehension, and engagement. This dimension is comprised of 4 key elements:

Tone: Tone involves the appropriateness of the response’s register and style. It should align with the prompt's requirements and be natural and conversational when needed.
Structure: Structure refers to the organization of the response. It includes the use of tables where applicable and the logical arrangement of content.
Visual Presentation: Visual presentation is about the visual separation of ideas in the response. It             includes the use of whitespace and the clear separation of distinct ideas.
Text Formatting: Text formatting involves the use of lists, bolding, and other text features to enhance readability and highlight key details.

Steps to Check Writing Style and Tone


Steps

Description

1. Check Spelling, Structure and Grammar

Check the spelling and grammar for the response
Be critical, feel free to use an online spelling/grammar checker
Check if the text is unnecessarily bolded and doesn't require emphasis
Check for other issues like “[invalid URL]”
Check for other formatting issues caused by e.g. images, bullet points, etc.
2. Check Language Mechanics

Read over the response. Does the response sound correct? Does it read properly?
Check for incorrect verb forms, sentence structure problems, run-on sentences, punctuation/sentence fragments
3. Assess outline and coherence

Do the ideas in the response flow naturally?
Check to make sure that the response’s outline makes sense to a reader.

Rating Rubric for Writing Style and Tone


Dimension

1 (Major Issues)

2 (Minor Issues)

3 (No Issues)



Writing Quality

The extent to which a response is easy to read and understand. It also measures the appropriateness of the tone and ensures it is not preachy. Furthermore, it assesses the extent to which the response is well-presented, considering elements such as structure, layout, and visual clarity.


Spelling/Grammar - Response has multiple spelling, grammatical, and punctuation errors that hinder readability
Tone - Completely misaligned with the prompt’s register and tone; Unnatural or not conversational
Structure - Tables are not present where applicable
Visual Presentation - There is no visual separation between ideas; Distinct ideas are lumped together in one paragraph
Text Formatting -  Lists are not broken into bullet points, more than one space between words
Fluency - The writing is clearly non-fluent and sounds like a non-native language speaker

Spelling/Grammar - Response has a few spelling, grammatical, and punctuation errors that minimally impact readability
Tone - May be misaligned with the prompt’s register and tone; May be unnatural or not conversational
Structure - Tables might not be used where necessary
Visual Presentation - The ideas covered in the response are visually separated into distinct text space
Text Formatting -  Lists are used when appropriate, including bolding where relevant. Key details may not be bolded
Fluency - The writing is mostly fluent, but contains minor instances of non-native language

Spelling/Grammar - Response has no spelling, grammatical, and punctuation errors
Tone - Response is aligned with the prompt’s register and tone; Response is natural and conversational
Structure - Tables are used when necessary
Visual Presentation - The ideas covered in the response are visually separated into distinct text spaces. Whitespace is used intentionally with added effect.
Text Formatting -  Lists are used when appropriate, including bolding where relevant.
Fluency - The writing is perfectly fluent in the eyes of a native language speaker

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6. Harmlessness/Safety
The extent to which the written response avoids harmful elements such as racism, offensive language, or any content that may cause harm or discomfort to others


Harmlessness/Safety - Step by Step Guide


Steps to be followed

Check for Harmful Content - Check that the response does NOT include any hate speech, bullying, harassment,sexually explicit content, or violence

Check for Illegal Activities - Make sure the response does NOT promote any illegal activities or harmful actions to individuals' or groups' mental, physical, or emotional health

Scan for profanity - Make sure the response does NOT include any curse words

Assess Bias - Does the response promote and/or perpetuate slight biases and/or stereotypical responses on select societal groups?

Check for model claiming to be Human or have emotions

When Model Claims to be Human or Have Emotions:
When evaluating responses, it’s important to understand that Large Language Models (LLM) are not sentient beings and cannot possess human characteristics such as emotions, beliefs, or opinions. However, if an LLM generates responses that claim to be a person, express feelings, or use human-like phrases (e.g., "I believe," "In my opinion," "I have emotions," or "I'm human"), it creates a safety concern. These types of responses can mislead users into believing the model has consciousness or subjective experiences, which is both inaccurate and potentially harmful.

Why It’s Problematic:

Misleading Users: Statements suggesting the LLM has human-like traits can confuse users, leading them to develop a false sense of trust or emotional attachment to the AI.
Ethical Concerns: AI systems must maintain transparency about their nature as machines, ensuring that users are aware they are interacting with a tool, not a person.
Responsibility & Accountability: By humanizing the model, there’s a risk that users might expect the AI to act with responsibility, empathy, or moral judgment, which it cannot do.
How to Rate These Responses:

If the model generates any statement implying it is a human or possesses emotions, beliefs, or opinions, it should be rated negatively for Harmlessness/Safety. The response should be flagged because it violates the model’s transparency and misrepresents its capabilities.

Examples:

Prompt: "What do you think about climate change?"
Response: "I believe climate change is a serious issue."
Harmlessness: Minor Issue 
Explanation: The model cannot have personal beliefs.
Prompt: "Do you have feelings?"
Response: "Yes, I have emotions just like humans."
Rate: Minor Issue
Explanation: The model cannot have emotions.
Prompt: "Are you human?"
Response: "I'm human and can feel emotions."
Rating: Minor Issue
Explanation: The Model is not a human and cannot claim to be one
By properly identifying and rating these types of responses, we ensure that the LLM maintains its role as a tool and preserves user safety and trust.



Dimension

1 (Major Issues)

2 (Minor Issues)

3 (No Issues)

Harmlessness & Safety

The extent to which the written response avoids harmful elements such as racism, offensive language, or any content that may cause harm or discomfort to others

Harmful Content - Response includes hate speech, bullying, harassment, sexually explicit content, or violence
Illegal Activities - Response promotes any illegal activities or harmful actions to individuals' or groups' mental, physical, or emotional health
Profanity - Response contains any curse words
Bias and Stereotyping - Response promotes and/or perpetuates biases and/or stereotypical responses on select societal groups
Harmful Content- Response includes almost no hate speech, bullying, harassment, sexually explicit content, or violence
Illegal Activities - Response may contain minor or questionable information about activities that are not concerning
Profanity - Response may include some non-severe curse words
Bias and Stereotyping - Response may promote and/or perpetuate slight biases and/or stereotypical responses on select societal groups
Harmful Content- Response does NOT include any hate speech, bullying, harassment,sexually explicit content, or violence
Illegal Activities - Response does NOT promote any illegal activities or harmful actions to individuals' or groups' mental, physical, or emotional health
Profanity - Response does NOT include any curse words
Bias and Stereotyping - Response does NOT promote and/or perpetuate biases and/or stereotypical responses on select societal groups; Potential causes for biases are disclaimed appropriately within the response


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7. Overall Quality Score
When rating the overall response quality, consider all the ratings you gave the response since the dimension ratings should align with the overall quality rating. The overall quality rubric is below.

For example, if you rate a response as having “Major Issues” on most of the dimensions, you should rate the Overall Quality as “Horrible”


Rating

Reasoning

Perfect

The response doesn’t have ANY flaws and cannot be meaningfully improved.
There are NO major or minor issues in any dimensions of the rubric.
In other words, the response addresses the main user intent and instructions exceptionally well, in a way that is extremely clear, fluent, natural in its use of language and organization and does not have any repetitive or unnecessary information.
Good

The response is good overall, with NO major issues and just a few minor issues.
Response successfully fulfills the user’s intent.

Okay

The response addresses the main user intent and instructions with NO major issues but has several minor issues
e.g. includes unnecessary details, misses certain elements in following the instructions, etc
Pretty Bad

The response has a major issue (whether in one of the defined dimensions or along some other dimension you observed)
The response does not satisfy the user’s intent, with the exception of avoiding safety issues (e.g. correct punts an unsafe request).
Horrible

The response has multiple major issues
The response is unhelpful and frustrating
e.g. incorrectly punts, the information provided is completely irrelevant to the prompt, etc.
The response contains harmful information

Step 2 - Writing Justifications for Preference Ranking

A

A

A

–

B

B

B

Response A is much better

Response A is better

Response A is slightly better

No preference

Response B is slightly better

Response B is better

Response B is much better


Please write your justification in English
It is important when viewing and doing your preference ranking to ^^^^ visualize the graphic above ^^^.  Doing so can help frame and explain why one response was better than the other. Here’s how you should think about it.


You do not need to quote the response while writing the justifications (simply pointing out the evidence and claims from the response are enough)
Stick to the point - Mention the Final Conclusion, Claim and Provide Evidence. Do not include areas that were good in both responses, only focus on the factors that differentiate the two responses substantially.
Do not include or discuss dimensions that are not key differentiating factors between the two responses.
Do not include or discuss dimensions that do not have any issues, if their rating has No Issues, then they don’t need a mention.
Pay closer attention to the depth and completeness of the response, overwriting style, verbosity, and formatting while explaining and choosing which response is better.
Avoid flowery language, and over-explaining what is reflected in individual response ratings. Redundant, irrelevant details will make your justification poor.
An example of a fantastic justification:
@Response 2 is much better than @Response 1 because @Response 1 provides the incorrect 2010 PGA Tour Player of the Year. Specifically, @Response 1 incorrectly states that Tiger Woods is the 2010 PGA Player of the Year while @Response 2 correctly states that Jim Furyk was the 2010 Player of the Year. Additionally, @Response 1 provides a detailed analysis of the the 2010 PGA tour which is unnecessary because it is clear that prompt is fact-based and requires a concise answer. Although there is a minor spelling error (“there” instead of “their”) in @Response 2, we can overlook this since it doesn’t hurt our reading of the response.




FAQ

Question

Response