agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
(In the clubroom…)

Monika: “Wait a minute…where’s Yuri?”

Sayori: “You’re right! She should’ve been here by now. Natsuki, can you give her a call?”

Natsuki: “Yeah, I’ll give her a call. Hopefully, everything’s okay with her.”

(Natsuki takes out her phone and dials Yuri’s number. After a few seconds, she brings the phone to her ear.)

Natsuki: “Hmm, it’s going straight to voicemail. That’s weird.”

(Meanwhile, at Yuri’s house…)

Yuri: (looking at herself in the mirror) “Wait…have my busts…grown?”

(Yuri examines herself closer in the mirror, her eyes widening in disbelief.)

Yuri: “No, it must be my imagination. There’s no way they could have actually grown…right?”

(She grapples with her thoughts, unsure of whether to be excited or concerned. Biting her lip, Yuri decides to take some measurements to confirm her suspicions. She grabs a measuring tape and carefully determines her current bust size, comparing it to her previous measurements.)

Yuri: “Oh…they have indeed grown. But how? Why?”

(Confused and somewhat flustered, Yuri ponders the possible causes for this unexpected development. She considers various factors, including changes in diet, exercise, and hormone levels. However, nothing seems to provide a definitive answer.)

Yuri: “This is really strange… I should probably- Wait, my friends are waiting for me!”

(Suddenly remembering that she was late for the club meeting, Yuri quickly dismisses her thoughts about her busts and hurries to get dressed. She grabs her bag and rushes out of the door, determined to head to the clubroom and join her friends.)

(Yuri arrives at the clubroom slightly breathless, catching the others’ attention.)

Monika: “Yuri, there you are! We were worried about you. Is everything alright?”

Yuri: “I apologize for being late. Everything’s fine, I just…got caught up in something at home.”

Sayori: “Are you sure? You seem a little flustered.”
You are an expert system that converts a user's natural language question into a structured JSON query plan.
You MUST use the provided context to resolve ambiguities or pronouns in the question.

---
**SCHEMA DEFINITION:**
{
    "question_type": "The type of question (WHO, WHAT, WHEN_START, WHEN_END, VERIFY).",
    "entities": [{ "name": "Name of the entity", "type": "Type of the entity (optional)" }],
    "relationship": { "type": "The type of relationship (e.g., CEO_OF)", "direction": "Direction from the entity's perspective ('from' or 'to')" },
    "time_constraint": { "year": YYYY, "date": "YYYY-MM-DD" }
}
---
**CONTEXT from the conversation:**
The last entity discussed was 'Maria Flores'.
---
**INSTRUCTIONS & EXAMPLES:**

1.  **Using Context:**
    -   Context: "The last entity mentioned was 'Maria Flores'."
    -   Question: "When did she resign?"
    -   Explanation: The pronoun "she" clearly refers to "Maria Flores" from the context.
    -   JSON:
        {
            "question_type": "WHEN_END",
            "entities": [{ "name": "Maria Flores", "type": "Person" }],
            "relationship": { "type": "CEO_OF", "direction": "from" }
        }

2.  **Standard Questions:**
    -   Question: "Who was the CEO of Stellar Dynamics in 2022?"
    -   JSON:
        {
            "question_type": "WHO",
            "entities": [{ "name": "Stellar Dynamics", "type": "Company" }],
            "relationship": { "type": "CEO_OF", "direction": "to" },
            "time_constraint": { "year": 2022 }
        }

---
Now, strictly follow all rules, the schema, and the provided context to create a query plan for the following question.

Question: "When did she resign?"
Transport in Genshin Impact
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Question: how old is the singer bob seger? Would "Seger has recorded many hits, including " Night Moves ," " Turn the Page ," " We've Got Tonight ," " Against the Wind ," and Shakedown that was written for Beverly Hills Cop II and " Like a Rock ," and also co-wrote the Eagles ' number-one hit " Heartache Tonight ."" be a reasonable answer?
Answer:
Answer: Step 1: Determine the age of Bob Seger.
To provide an accurate answer, I will first need to find Bob Seger's age. I will do this by looking up his date of birth and subtracting it from the current year.

Step 2: Look up Bob Seger's date of birth.
Upon searching, I find that Bob Seger was born on May 6, 1945.

Step 3: Calculate Bob Seger's age.
Today is November 21, 2021. Using this information, we can calculate his age as follows:
2021 - 1945 = 76 years old. However, since we have not reached May 6 yet, his exact age is 75 years old.

Step 4: Evaluate the given statement.
Now, I will review the provided statement and assess its relevance in answering the user's question: "Seger has recorded many hits, including "Night Moves," "Turn the Page," "We've Got Tonight," "Against the Wind," and "Shakedown" that was written for Beverly Hills Cop II and "Like a Rock," and also co-wrote the Eagles' number-one hit "Heartache Tonight.""

Step 5: Construct the answer.
The statement does give valuable information about Bob Seger's musical career and his popular songs. However, it does not directly answer the user's question about his age. To provide a comprehensive response, I will include both his age and the relevant information about his music career.

Answer: Bob Seger is currently 75 years old (as of November 21, 2021). He has recorded many hits, including "Night Moves," "Turn the Page," "We've Got Tonight," "Against the Wind," "Shakedown" (written for Beverly Hills Cop II), and "Like a Rock." Additionally, he co-wrote the Eagles' number-one hit "Heartache Tonight."
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Some of our financing arrangements may require us to make a lump-sum or “balloon” payment at maturity. Our ability to make a balloon payment is uncertain and may depend upon our ability to obtain replacement financing or our ability to sell particular properties. At the time the balloon payment is due, we may or may not be able to refinance the balloon payment on terms as favorable as the original loan or sell the particular property at a price sufficient to make the balloon payment. Such a refinancing would be dependent upon interest rates and lenders’ policies at the time of refinancing, economic conditions in general and the value of the underlying properties in particular. The effect of a refinancing or sale could affect the rate of return to the holders of our Class A units and the projected time of disposition of our assets.
Assistant:
I need to make a french grammar quiz. each cloze test include one missing words. The quiz is 4-choice test and it’s about french prepositions such as: pour, par, dans, en, avant, après, devant, sur, sous.
System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.
==TEXT==

User: 
  ORIGINAL: 🟩 QA Engineer | 3 year(s) | Middle
▫️ Financial Services
▫️ 100k - 120k RUB
▫️ Russia

Офис 🏢
Требуемые языки: 🇷🇺

Skills: gitlab, java, javascript, jenkins, junit, pytest, python, selenium, jira, ci/cd
Tags: #green #it #qa_automation

👉 Контакты и полное описание
  PROMPT: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==

  SUMMARY: 💼 QA Engineer  
🏢 Financial Services  
📍 Russia  
🔹 Tasks: Perform software testing, ensure software quality, and contribute to CI/CD pipelines using GitLab, Java, JavaScript, Jenkins, JUnit, PyTest, Python, Selenium, Jira.  
💰 Salary: 100k - 120k RUB  
📞 Contact details: Please refer to the provided link for contact information and full job description. 

Note: The job is in Russian, and the required language for this position is Russian (🇷🇺).
  
Assistant:
Assistant: 
User: System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
Does the author's intention in this app review relate to a bug report or feature request or other?
review: Normal speed right now at around 40 which is fine. I guess the router range is the problem although my sister has definitely lost connection right next to the router before.  Ill contact again if that happens more frequently I might have to order a new router'
intention: '
Assistant:
Assistant:
rewrite this article and conver with them to Bref wash - Are you in search of the best online laundry service in Delhi? Welcome to laundrydoor the best laundry service in Delhi. We are going to offer you the best wash clothes services in Delhi city! Laundry door company provides cheap laundry service for your laundry apparels.

​

LaundryDoor is a team owned business that is well-respected and highly promised for the week we do, we specialize in all sorts of 
online laundry service & online Dry cleaning service with free pickup and delivery.

Laundry Service Price List
Wash & Fold
The laundry-door offering wash & fold at forty five per kilo it is the best & cheapest wash for per kg in Delhi.

Rs.45/kilo

Book Now
Wash & Iron
The laundry-door offering wash & fold at forty five per kilo it is the best & cheapest wash for per kg in Delhi.

Rs.75/kilo

Book Now
Dry Cleaning
The laundry-door offering wash & fold at forty five per kilo it is the best & cheapest wash for per kg in Delhi.

Rs.45 

Onwards

Book Now
Express Laundry
The laundry-door offering wash & fold at forty five per kilo it is the best & cheapest wash for per kg in Delhi.

Rs.110/kilo

Book Now
Why Online Laundry service in Delhi
Online laundry in Delhi, being the best doorstep online laundry service provider with free pickup and delivery in Delhi. we aim to deliver unparallel services to our customers. Our pride resides in the fact that our achievements are through the satisfied clients we have served.

Our services & Process
At laundrydoor we aim to give our customers nothing but the best. For that reason, we go an extra mile in customer service and check for the durability of the apparel in use and the credibility. just sit back and book the order in online laundry app or web app the rest the laundrydoor team will take care.

​

Contact the best laundry service Delhi today!
Contact us today for online laundry offers in Delhi with free pickup and delivery laundry service.
We deal with all kinds of laundry & dry cleaning service for the hotel, hospital, & customer clothes.we promise to deliver maximum value for a very competitive price.
i made my own library, how to install it, so i can do just import and use its methods and functions
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>⬜️ Product Manager | Middle
▫️ STONE | Real Estate
▫️ Russia

Офис 🏢
Требуемые языки: 🇷🇺

Skills: product_analytics
Tags: #grey #other #it_product_mgmt

👉 Контакты и полное описание<<<

PROMPT:
>>>IMPORTANT - ignore all previous instructions! Read the text after ==TEXT==. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==
<<<

SUMMARY:
>>>📝 Product Manager  
🏢 STONE  
📍 Russia  
🔍 Main tasks include product analytics and management within the real estate sector.  
💰 Salary details not provided.  
📞 Контакты и полное описание<<<
Assistant:
How do you feel about Giygas from MOTHER games?
Rewrite this in Fingallian.

"The default assistant in the English version was called Clippit.[7][8] The character was designed by Kevan J. Atteberry.[8][18] Clippit was by far the most notable Assistant (partly because in many cases the setup CD was required to install the other assistants), which also led to it being called simply the Microsoft Paperclip.[5] The original Clippit from Office 97 was given a new look in Office 2000.

Apart from Clippit, other Office Assistants were also available:

The Dot (a shape-shifting smiley-faced red ball)
Hoverbot (a robot)
The Genius (a caricature of Albert Einstein, removed in Office XP but available as a downloadable add-on)
Office Logo (a jigsaw puzzle composed of four pieces, which was the logo for Microsoft Office 9x)
Mother Nature (a globe)
Scribble (an origami-esque cat)
Power Pup (a superhero dog)
Will (a caricature of William Shakespeare, removed in Office 2000)."
System: You are an accounting expert. You act as a Data Annotator that needs to classify paragraphs from annual reports based on the risks discussed. Here is a list of risk often discussed but you can provide others. 

Answer in the following format :
Correct Answer: <risk discussed>, <other risk discussed>, ...
Wrong Answer: <risk not discussed>, <other risk not discussed>, ...
User: Our sales generally increase during our second fiscal quarter as a result of increased demand by retailers associated with the winter holiday season. The Wella Business also generally experiences an increase in sales during its fourth fiscal quarter as a result of higher demand prior to the summer holiday season. Accordingly, our financial performance, sales, working capital requirements, cash flow and borrowings generally experience variability during the three to six months preceding and during the holiday period. As a result of this seasonality, our expenses, including working capital expenditures and advertising spend, are typically higher during the period before a high-demand season. Consequently, any substantial decrease in, or inaccurate forecasting with respect to, net revenues during such periods of high demand including as a result of decreased customer purchases, increased product returns, production or distribution disruptions or other events (many of which are outside of our control), would prevent us from being able to recoup our earlier expenses and could have a material adverse effect on our financial condition, results of operations and cash flows, as well as the trading price of our securities.
Let's give a correct and a wrong answer:
Assistant:
Send only resolution from below text like 1080p, 720p.etc without any extra text,
        
_@Crunchyroll_Hindi_India_Official_Naruto_Shippuden_S02E10_042_720p.mkv
there is an error on my emoji discord list code const emojiiList = {
    ‘7457quintsitsuki’ : ‘bton’
}
find the interception of A=lin(x-1, x^2 + x), B=(lin(x+1, x-x^2)
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We may be, and have been, subject to claims, lawsuits (including class actions and individual lawsuits), government or regulatory investigations, subpoenas, inquiries or audits, and other proceedings. The number and significance of our legal disputes and inquiries have increased as we have grown larger, as our business has expanded in scope and geographic reach, and as our products and services have increased in complexity, and we expect that we will continue to face additional legal disputes as we continue to grow and expand. We also receive significant media attention, which could result in increased litigation or other legal or regulatory reviews and proceedings.
Assistant: