DataHammer/scimrc huggingface dataset:
Question: How do they model travel behavior?
Evidence: Differently to textual data, our goal in this paper is to explore the large amount of categorical data that is often collected in travel surveys. This includes trip purpose, education level, or family type. We also consider other variables that are not necessarily of categorical nature, but typically end up as dummy encoding, due to segmentation, such as age, income, or even origin/destination pair.
Differently to textual data, our goal in this paper is to explore the large amount of categorical data that is often collected in travel surveys. This includes trip purpose, education level, or family type. We also consider other variables that are not necessarily of categorical nature, but typically end up as dummy encoding, due to segmentation, such as age, income, or even origin/destination pair.
Answer: The data from collected travel surveys is used to model travel behavior.
Question: Provide a comma separated list of all the universities listed in this passage
Evidence: In addition, she has presented on the topic of synesthesia at a number of universities including Yale University, Princeton University, the University of California, San Diego, Rockefeller University, the University of Virginia, the University of Almería, the University of Jaén, Stockholm University and others. Duffy was invited to be a Plenary Speaker on synesthesia at the "Towards a Science of Consciousness" conference at University of Arizona in Tucson. She is a co-founder of and consultant to the American Synesthesia Association.
Answer: Yale University, Princeton University, the University of California, San Diego, Rockefeller University, the University of Virginia, the University of Almería, the University of Jaén, Stockholm University, University of Arizona
Question: What model do they train?
Evidence: We carried out the experimentation with a range of classifiers of different types: Support Vector Machines (SVM), Gaussian Naive Bayes, Multinomial Naive Bayes, Decision Trees, Random Forests and a Maximum Entropy classifier. They were tested in two different settings, one without balancing the weights of the different classes and the other by weighing the classes as the inverse of their frequency in the training set; the latter was tested as a means for dealing with the highly imbalanced data. The selection of these classifiers is in line with those used in the literature, especially with those tested by Han et al. BIBREF41 . This experimentation led to the selection of the weighed Maximum Entropy (MaxEnt) classifier as the most accurate. In the interest of space and focus, we only present results for this classifier.
We carried out the experimentation with a range of classifiers of different types: Support Vector Machines (SVM), Gaussian Naive Bayes, Multinomial Naive Bayes, Decision Trees, Random Forests and a Maximum Entropy classifier.
Answer: The answers are shown as follows:
* Support Vector Machines (SVM), Gaussian Naive Bayes, Multinomial Naive Bayes, Decision Trees, Random Forests and a Maximum Entropy classifier
Question: What programming language should I learn?
Answer: There are many factors to consider when choosing a programming language including ease of use, support by the developer community, licensing, and your application. Programming languages known for their readability include languages like Python and Go. These languages also have open source licenses with strong support by the developer community for various libraries one can include in their applications. Languages with a REPL are often easier to start learning because they’re interactive. Java, C, C++, and Scala are common languages but are often considered to be more challenging to start with than a scripting and general purpose language like Python. For specific applications like web pages, JavaScript is used by most websites.
Question: What is the drawback of existing models to in question generation?
Evidence: Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-relevant context words to finish the declarative-to-interrogative sentence transformation. Existing sequence-to-sequence neural models achieve this goal by proximity-based answer position encoding under the intuition that neighboring words of answers are of high possibility to be answer-relevant. However, such intuition may not apply to all cases especially for sentences with complex answer-relevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases. To address this issue, we propose a method to jointly model the unstructured sentence and the structured answer-relevant relation (extracted from the sentence in advance) for question generation. Specifically, the structured answer-relevant relation acts as the to the point context and it thus naturally helps keep the generated question to the point, while the unstructured sentence provides the full information. Extensive experiments show that to the point context helps our question generation model achieve significant improvements on several automatic evaluation metrics. Furthermore, our model is capable of generating diverse questions for a sentence which conveys multiple relations of its answer fragment.
However, such intuition may not apply to all cases especially for sentences with complex answer-relevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases.
Answer: The performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases.
Question: Give me a list of popular SUVs available in North America
Answer: SUVs (Sports Utility Vehicle) have become increasingly popular in North America in recent years.  Five popular SUVs available currently include:
 - Kia Telluride
 - BMW X5
 - Hyundai Palisade
 - Jeep Grand Cherokee
 - Volvo XC40
Question: What form of government is the United Kingdom?
Answer: The United Kingdom is a constitutional monarchy
Question: Where did they collect their dataset from?
Evidence: To make the annotated data publicly available, we selected 70 news articles from Arabic WikiNews site https://ar.wikinews.org/wiki. These articles cover recent news from year 2013 to year 2015 in multiple genres (politics, economics, health, science and technology, sports, arts, and culture.) Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each.
To make the annotated data publicly available, we selected 70 news articles from Arabic WikiNews site https://ar.wikinews.org/wiki. These articles cover recent news from year 2013 to year 2015 in multiple genres (politics, economics, health, science and technology, sports, arts, and culture.) Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each.
Answer: The answers are shown as follows:
* from Arabic WikiNews site https://ar.wikinews.org/wiki
Question: What are the pros and cons of charging your Tesla at home?
Answer: The most convenient way to charge your EV is to charge it at home overnight while you sleep. Home chargers tend to be slow chargers which reach around 3.6 - 7 kW, although you can also charge using your home’s plug sockets which charge at speeds of around 2.3 kW. 

Pros
Charging your Tesla Model Y at home overnight is probably the cheapest, most cost-efficient way to charge your EV. This is because off-peak energy prices are significantly cheaper than peak charging. Model Ys come with an adapter wire, allowing you to charge your EV using your home's plugs easily. 

If you're interested in charging at home, installing a home charging unit could be worth your time. Over time this could save you money. There are grants available to help you afford a home charger. 

The OZEV Chargepoint Grant is up to £350 or 75% off the fully installed charge point cost if specifics are met. There is also the Scottish EST Domestic Chargepoint Grant which gives you up to £300 and can be used alongside the OZEV Chargepoint Grant.

Cons
Home chargers rarely exceed speeds of 7 kW. This means charging the Tesla Model Y's large battery would take a long time. If you don't invest in a home charging station, it might also be risky to home charge as the 3-pin plug can damage your home's wiring. 

If you choose to charge during the day, it can also get quite expensive and may not compare favourably with public charging stations, despite being much slower.
Question: What Constitutes a Foul in Football?
Answer: As a rule, a football foul would be an unfair act committed by a player which interferes with active play. A match referee would decide on the foul while officiating the game's laws.

There are different outcomes that punish soccer fouls. The referee can award either a direct or an indirect free-kick (depending on the offence). In some cases, a foul in football results in a penalty spot kick awarded to the opposing team.
Question: What are some of the languages spoken in India?
Answer: Following are some of the languages spoken in India - Hindi, English, Tamil, Telugu, Malayalam, Oriya, Kannada, Telugu, Marathi, Punjabi, and Bengali.
Question: How are the auxiliary signals from the morphology table incorporated in the decoder?
Evidence: In order to address the aforementioned problems we redesign the neural decoder in three different scenarios. In the first scenario we equip the decoder with an additional morphology table including target-side affixes. We place an attention module on top of the table which is controlled by the decoder. At each step, as the decoder samples a character, it searches the table to find the most relevant information which can enrich its state. Signals sent from the table can be interpreted as additional constraints. In the second scenario we share the decoder between two output channels. The first one samples the target character and the other one predicts the morphological annotation of the character. This multi-tasking approach forces the decoder to send morphology-aware information to the final layer which results in better predictions. In the third scenario we combine these two models. Section "Proposed Architecture" provides more details on our models.
In the first scenario we equip the decoder with an additional morphology table including target-side affixes. We place an attention module on top of the table which is controlled by the decoder. At each step, as the decoder samples a character, it searches the table to find the most relevant information which can enrich its state. Signals sent from the table can be interpreted as additional constraints. In the second scenario we share the decoder between two output channels. The first one samples the target character and the other one predicts the morphological annotation of the character. This multi-tasking approach forces the decoder to send morphology-aware information to the final layer which results in better predictions. In the third scenario we combine these two models. 
Answer: The answers are shown as follows:
* an additional morphology table including target-side affixes.
* We inject the decoder with morphological properties of the target language.
Question: Given this paragraph about Freddie Mercury, where was his last performance with Queen.
Evidence: Throughout his career, Mercury performed an estimated 700 concerts in countries around the world with Queen. A notable aspect of Queen concerts was the large scale involved. He once explained, "We're the Cecil B. DeMille of rock and roll, always wanting to do things bigger and better." The band was the first ever to play in South American stadiums, breaking worldwide records for concert attendance in the Morumbi Stadium in São Paulo in 1981. In 1986, Queen also played behind the Iron Curtain when they performed to a crowd of 80,000 in Budapest, in what was one of the biggest rock concerts ever held in Eastern Europe. Mercury's final live performance with Queen took place on 9 August 1986 at Knebworth Park in England and drew an attendance estimated as high as 200,000. A week prior to Knebworth, May recalled Mercury saying "I'm not going to be doing this forever. This is probably the last time." With the British national anthem "God Save the Queen" playing at the end of the concert, Mercury's final act on stage saw him draped in a robe, holding a golden crown aloft, bidding farewell to the crowd.
Answer: Knebworth Park
Question: classify each of these as either computer software or hardware: RAM, SSD, keyboard, mouse, trackpad, python, R, Scala, Google, Gmail, Figma, camera, screen
Answer: Computer software: python, R, Scala, Google, Gmail, Figma
Computer hardware: RAM, SSD, keyboard, mouse, trackpad, camera, screen
Question: How do you make Cincinnati style Venison Chili?
Answer: In a 4 to 6 quart pot, bring 1 quart of water to a boil

Add:
2 lbs ground Venison

Sit until separated and reduce heat to a simmer

Add:  
2 medium yellow onions, finely chopped
5 to 6 cloves of garlic, crushed (or 3 teaspoons minced garlic)
One 15-ounce can tomato sauce
2 Tablespoons apple cider vinegar
1 tablespoon Worcestershire sauce

In a ziplock bag, crush and add:
10 black peppercorns
8 whole allspice
8 whole cloves

Stir and Add:
1 large bay leaf
2 teaspoons salt
2 teaspoons ground cinnamon
1 1/2 teaspoons ground cayenne pepper
1 teaspoon cumin
1/2 ounce grated unsweetened chocolate

Return to a boil, then reduce heat to a simmer for 2 hours.  

If using ground beef, cool uncovered and store overnight in the refrigerator.  Spoon off excess fat the next morning.  Heat and serve
Question: Who is the most sensational player in MLB today?
Answer: Shohei Ohtani
Question: Which of these are characters in Ted Lasso: Rebecca Welton, Olivia Benson, Jamie Tartt
Answer: Rebecca Welton and Jamie Tartt are both characters in the TV show Ted Lasso.  Olivia Benson is the main character on the TV Show Law & Order: SVU.
Question: Was any variation in results observed based on language typology?
Evidence: On another note, we apply our formalization to evaluate multilingual $\textsc {bert} $'s syntax knowledge on a set of six typologically diverse languages. Although it does encode a large amount of information about syntax (more than $81\%$ in all languages), it only encodes at most $5\%$ more information than some trivial baseline knowledge (a type-level representation). This indicates that the task of POS labeling (word-level POS tagging) is not an ideal task for contemplating the syntactic understanding of contextual word embeddings.
We know $\textsc {bert} $ can generate text in many languages, here we assess how much does it actually know about syntax in those languages. And how much more does it know than simple type-level baselines. tab:results-full presents this results, showing how much information $\textsc {bert} $, fastText and onehot embeddings encode about POS tagging. We see that—in all analysed languages—type level embeddings can already capture most of the uncertainty in POS tagging. We also see that BERT only shares a small amount of extra information with the task, having small (or even negative) gains in all languages.
Finally, when put into perspective, multilingual $\textsc {bert} $'s representations do not seem to encode much more information about syntax than a trivial baseline. $\textsc {bert} $ only improves upon fastText in three of the six analysed languages—and even in those, it encodes at most (in English) $5\%$ additional information.
On another note, we apply our formalization to evaluate multilingual $\textsc {bert} $'s syntax knowledge on a set of six typologically diverse languages. Although it does encode a large amount of information about syntax (more than $81\%$ in all languages), it only encodes at most $5\%$ more information than some trivial baseline knowledge (a type-level representation). This indicates that the task of POS labeling (word-level POS tagging) is not an ideal task for contemplating the syntactic understanding of contextual word embeddings.
We see that—in all analysed languages—type level embeddings can already capture most of the uncertainty in POS tagging. We also see that BERT only shares a small amount of extra information with the task, having small (or even negative) gains in all languages.
Finally, when put into perspective, multilingual $\textsc {bert} $'s representations do not seem to encode much more information about syntax than a trivial baseline. $\textsc {bert} $ only improves upon fastText in three of the six analysed languages—and even in those, it encodes at most (in English) $5\%$ additional information.
Answer: It is observed some variability - but not significant. Bert does not seem to gain much more syntax information than with type level information.
Question: How many teams compete in Formula one world championship?
Answer: Currently there are 10 teams competing in the Formula one world championships. The number of teams that compete in Formula one world championship have varied in the past.
Question: Classify each team as playing in the EPL or La Liga: Barcelona, Tottenham, Brighton, Liverpool, Sevilla, Valencia, Everton
Answer: Barcelona: La Liga
Tottenham: EPL
Brighton: EPL
Liverpool: EPL
Sevilla: La Liga
Valencia: La Liga
Everton: EPL
Question: Tell me about the Mughal Empire?
Evidence: The Mughal Empire was an early-modern empire that controlled much of the Indian subcontinent between the 16th and 19th centuries. For some two hundred years, the empire stretched from the outer fringes of the Indus river basin in the west, northern Afghanistan in the northwest, and Kashmir in the north, to the highlands of present-day Assam and Bangladesh in the east, and the uplands of the Deccan Plateau in South India.It included present day areas of India, Pakistan, Afghanistan and Bangladesh.
Answer: The Mughal empire is conventionally said to have been founded in 1526 by Babur, a warrior chieftain from what is today Uzbekistan, who employed aid from the neighboring Safavid and Ottoman empires, to defeat the Sultan of Delhi, Ibrahim Lodi, in the First Battle of Panipat, and to sweep down the plains of North India. The Mughal imperial structure, however, is sometimes dated to 1600, to the rule of Babur's grandson, Akbar. This imperial structure lasted until 1720, shortly after the death of the last major emperor, Aurangzeb, during whose reign the empire also achieved its maximum geographical extent. Reduced subsequently to the region in and around Old Delhi by 1760, the empire was formally dissolved by the British Raj after the Indian Rebellion of 1857.
Question: Which characters belong to DC or Marvel Universe? Electro, Black Lightning
Answer: Electro is Marvel, Black Lightning is DC
Question: Can you write me a haiku to boost my spirits today?
Answer: Sunlight streaming through, 
Lightning in the sky, 
My spirits lift.
Question: How many planets orbit the sun?
Answer: For many years astronomers stated that there were 9 planets that orbited the Sun. In 2006 the 9th planet and furthest from the Sun, Pluto, was demoted from a planet to a dwarf planet. Technically there are now 8 planets and 1 dwarf planet that orbit the Sun.
Question: Who was the most prolific religious leader ever?
Answer: Some know him as Zoroaster and others as Zarathustra. He lived between the 18th and 16th century BCE. The reputed founder of Zoroastrianism, he started the major religion of the Persian Empire. This empire flourished from 550 BCE to 330 BCE.

Nobody knows his birthplace. But many Arabic sources claim he got born in modern Azerbaijan. To this day, so many countries have tried to lay claim to his birthplace, that we might think he was not a single person.

Followers worshiped Ahura Mazda, the Supreme Being or God of Zoroastrianism. While the religion has gone extinct, we have to note it influenced so many ancient civilizations. Zoroastrianism influenced Classic Greek philosophy, Roman teachings, and many more.

There are still some people practicing Zoroastrianism, namely in India.
Question: List some reasons why cats are the best pets.
Answer: - They are cuddly
- They are independent
- They very frequently bond with their humans
- They provide emotional support
- They are less demanding than dogs
- They are easy to care for
- They don't need constant attention 24/7
- They can be trained to use a litter box or even a real toilet
- They don't take up a lot of space
- They don't require lots of expensive toys, equipment, or beds
- They don't need to go on walks every day
- They don't make large messes
- They are often much more easier to care for than dogs
- They don't slobber
- They don't lick your face
- They don't chew up your furniture
- They are extremely cute
Question: What do economists do?
Answer: Economists study a diverse range of social issues whose outcomes are driven by the choices of individuals under resource constraints and limited information. There are various subfields in economics:

- Microeconomists study the supply and demand decision of individuals and firms, such as how profits can be maximized and how much of a good or service consumers will demand at a certain price. 
- Industrial/Organizational Economists study the market structure of particular industries in terms of the number of competitors, and the market decisions of competitive firms and monopolies. These economists may also be concerned with antitrust policy and its impact on market structure.
- Macroeconomists study historical trends in the whole economy and forecast future trends in areas such as unemployment, inflation, economic growth, productivity, and investment. 
Financial Economists study the money and banking system and the effects of rising interest rates. 
- Public Finance Economists primarily are involved in studying the role of the government in the economy and the effects of tax cuts, budget deficits, and welfare policies.
International Economists study international financial markets, exchange rates, and the effects of various trade policies such as tariffs.
- Labor Economists study the supply and demand for labor and the determination of wages. These economists also try to explain the reasons for unemployment, and the effects on labor markets of changing demographic trends such as an aging population and increasing immigration. 
- Econometricians are involved in all areas of economics and use mathematical techniques such as calculus, game theory, and regression analysis to formulate economic models. These models help to explain economic relationships and are used to develop forecasts related to the nature and length of business cycles, the effects of a specific rate of inflation on the economy, the effects of tax legislation on unemployment levels, and other economic phenomena. Many economists have applied these fundamental areas of economics to more narrow areas with specific applications such as health, education, agriculture, urban and regional economics, law, history, energy, and the environment. 

Economists may work in Business, Finance Government and Academia.
Question: Think up a bunch of different ways to spend time outdoors
Answer: Hiking, camping, fishing, kayaking, walking, biking, jogging