DataHammer/scimrc huggingface dataset:
Question: What's a Louisville hot brown?
Evidence: A Hot Brown sandwich (sometimes known as a Louisville Hot Brown or Kentucky Hot Brown) is an American hot sandwich originally created at the Brown Hotel in Louisville, Kentucky, by Fred K. Schmidt in 1926. It is a variation of traditional Welsh rarebit and was one of two signature sandwiches created by chefs at the Brown Hotel shortly after its founding in 1923. It was created to serve as an alternative to ham and egg late-night dinners.

Ingredients
The Hot Brown is an open-faced sandwich of turkey breast and ham and bacon, covered in creamy Mornay sauce and baked or broiled until the bread is crisp and the sauce begins to brown. Cheddar cheese or American cheese may be added for the sauce. Alternatives for garnishes include tomatoes, mushroom slices, and, very rarely, canned peaches.

Some Hot Browns also include ham with the turkey, and either pimentos or tomatoes over the sauce, and imitation Hot Browns sometimes substitute a commercial cheese sauce instead of the Mornay.

When Fred K. Schmidt created the Hot Brown, its sliced roast turkey was a rarity, as turkey was usually reserved for holiday feasts. The original Hot Brown included the sliced turkey on an open-faced white toast sandwich, with Mornay sauce covering it, with a sprinkling of Parmesan cheese, completed by being oven-broiled until bubbly. Pimento and bacon strips were then added to it. After its debut, it quickly became the choice of 95% of the Brown Hotel's restaurant customers.

The dish is a local specialty and favorite of the Louisville area, and is popular throughout Kentucky. It was long unavailable at its point of origin, as the Brown Hotel was shuttered from 1971 to 1985.
Answer: The Hot Brown is an open faced sandwich that is a local speciality throughout Kentucky.   It was originally served in 1926 at Louisville's Brown Hotel.  The sandwich is a spin on the traditional Welsh Rarebit.  It's turkey breast, served open face on white bread, topped with Mornay sauce and run under the broiler until hot and bubbly.  Some variations include ham with the turkey, as well as pimentos or tomatoes.
Question: From the text, extract the locations in which outrigger vessels are present in. Separate them with commas.
Evidence: These vessels were the first true ocean-going ships, and are an important part of the Austronesian heritage. They were the vessels that enabled the Austronesian expansion from Taiwan into the islands of both the Indian and Pacific Ocean from around 3000 BC. They comprise the bulk of traditional boats in Island Southeast Asia, Island Melanesia, Micronesia, Polynesia, and Madagascar. They have spread to other cultures Austronesians came into contact with, notably in Sri Lanka and southern India as well as in the coast of East Africa. In modern times, outrigger vessels are used in the sport of sailing. Catamaran and trimaran configurations are also widely used for high speed craft.
Answer: Taiwan, Island Southeast Asia, Island Melanesia, Micronesia, Polynesia, Madagascar, Sri Lanka, southern India, East Africa
Question: How can you tell how old a tree is?
Answer: The size of tree has a direct correlation on how old the tree is.  The wider the diameter and circumference of the tree the older it is.  When a tree is cut down there will be inner rings in the stump that describe the tree's age as well.  A tree's diameter will increase in size about 2.5 centimeters per year.  You can estimate the age of a tree by measuring the circumference of the tree in centimeters and then dividing it by 2.5 centimeters.  It will be important to measure as close to the ground as possible to get the most accurate measurement for the tree's age.
Question: On a hiking trip where we expect to walk many miles, categorize each of the the following as either 'heavy', 'not heavy' or 'somewhat heavy'. A tent, a compass, a sleeping bag, a camping stove, a cooler full of food.
Answer: A tent is heavy.
A compass is not heavy.
A sleeping bag is not heavy.
A camping stove is somewhat heavy.
A cooler full of food is heavy.
Question: Why do people like soccer?
Answer: People like soccer as it is a creative worldwide game. Unlike other sports that have frequent stoppages and timeouts soccer has two 45 minute halves. On top of that it is a very creative sport as it requires 11 team members to work in union to achieve a common goal. Everybody can play soccer no matter your age or athletic level so it is extremely appealing to many. Additionally, unlike other sports that require significant associated cost, soccer is rather cheap, all you need is a ball.
Question: What is used as the optimizer in the UTCNN training process?
Evidence: In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer. For FBFans dataset, we learned the 50-dimensional word embeddings on the whole dataset using GloVe BIBREF21 to capture the word semantics; for CreateDebate dataset we used the publicly available English 50-dimensional word embeddings, pre-trained also using GloVe. These word embeddings were fixed in the training process. The learning rate was set to 0.03. All user and topic embeddings were randomly initialized in the range of [-0.1 0.1]. Matrix embeddings for users and topics were sized at 250 ( INLINEFORM0 ); vector embeddings for users and topics were set to length 10.
We applied the LDA topic model BIBREF22 on the FBFans dataset to determine the latent topics with which to build topic embeddings, as there is only one general known topic: nuclear power plants. We learned 100 latent topics and assigned the top three topics for each post. For the CreateDebate dataset, which itself constitutes four topics, the topic labels for posts were used directly without additionally applying LDA.
For the FBFans data we report class-based f-scores as well as the macro-average f-score ( INLINEFORM0 ) shown in equation EQREF19 . DISPLAYFORM0 
where INLINEFORM0 and INLINEFORM1 are the average precision and recall of the three class. We adopted the macro-average f-score as the evaluation metric for the overall performance because (1) the experimental dataset is severely imbalanced, which is common for contentious issues; and (2) for stance classification, content in minor-class posts is usually more important for further applications. For the CreateDebate dataset, accuracy was adopted as the evaluation metric to compare the results with related work BIBREF7 , BIBREF9 , BIBREF12 .
In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer.
Answer: AdaGrad.
Question: Who invented the Jeans
Evidence: Jeans are a type of pants or trousers made from denim or dungaree cloth. Often the term "jeans" refers to a particular style of trousers, called "blue jeans", with copper-riveted pockets which were invented by Jacob W. Davis in 1871 and patented by Jacob W. Davis and Levi Strauss on May 20, 1873. Prior to the patent, the term "blue jeans" had been long in use for various garments (including trousers, overalls, and coats), constructed from blue-colored denim.

"Jean" also references a (historic) type of sturdy cloth commonly made with a cotton warp and wool weft (also known as "Virginia cloth"). Jean cloth can be entirely cotton as well, similar to denim. Originally designed for miners, modern jeans were popularized as casual wear by Marlon Brando and James Dean in their 1950s films, particularly The Wild One and Rebel Without a Cause, leading to the fabric becoming a symbol of rebellion among teenagers, especially members of the greaser subculture. From the 1960s onwards, jeans became common among various youth subcultures and subsequently young members of the general population. Nowadays, they are one of the most popular types of specialty trousers in Western culture. Historic brands include Levi's, Lee, and Wrangler.
Answer: Jeans are a type of pants or trousers made from denim or dungaree cloth. Often the term "jeans" refers to a particular style of trousers, called "blue jeans", with copper-riveted pockets which were invented by Jacob W. Davis in 1871 and patented by Jacob W. Davis and Levi Strauss on May 20, 1873. Prior to the patent, the term "blue jeans" had been long in use for various garments (including trousers, overalls, and coats), constructed from blue-colored denim.

"Jean" also references a (historic) type of sturdy cloth commonly made with a cotton warp and wool weft (also known as "Virginia cloth"). Jean cloth can be entirely cotton as well, similar to denim. Originally designed for miners, modern jeans were popularized as casual wear by Marlon Brando and James Dean in their 1950s films, particularly The Wild One and Rebel Without a Cause, leading to the fabric becoming a symbol of rebellion among teenagers, especially members of the greaser subculture. From the 1960s onwards, jeans became common among various youth subcultures and subsequently young members of the general population. Nowadays, they are one of the most popular types of specialty trousers in Western culture. Historic brands include Levi's, Lee, and Wrangler.
Question: Who was the first player sent off during a soccer match?
Evidence: This is a list of all occasions where a football player was sent off from a FIFA World Cup match due to a foul or misconduct, either as a direct expulsion (red card) or as a second caution (yellow card) within the match.

This list includes all dismissals since the first World Cup in 1930. The use of physical red and yellow cards to respectively indicate dismissals and cautions is a later invention, having been introduced at the 1970 tournament.

Only players are listed, even if they were at the substitutes' bench at the time of the sending off. Managers and other technical staff members are not covered.

Statistics
Plácido Galindo was the first player to be sent off in a World Cup match, playing for Peru against Romania in a 1930 match officiated by Alberto Warnken. Although physical red cards were introduced from 1970, they were not put in practice until the 1974 World Cup, when referee Doğan Babacan sent off Chile's Carlos Caszely during a match against West Germany.
Two players have received red cards twice: Cameroon's Rigobert Song (1994 and 1998) and France's Zinedine Zidane (1998 and 2006).
Five dismissals have taken place during final matches: Argentina's Pedro Monzón and Gustavo Dezotti (both 1990), France's Marcel Desailly (1998) and Zinedine Zidane (2006), and John Heitinga of the Netherlands (2010).
Uruguay's José Batista received the quickest red card, in the first minute in a game against Scotland in 1986.
A few expulsions either took place after the final whistle of the match or were awarded to players at the substitutes' bench (or both), and thus did not reduce the number of players on the pitch during the game. Two of them, that of Leandro Cufré of Argentina (vs Germany, 2006) and that of Denzel Dumfries of the Netherlands (vs Argentina, 2022), took place after the penalty shoot-outs of knockout matches, making them the latest red cards.
Three goalkeepers have been sent off in the tournament: Italy's Gianluca Pagliuca (1994), South Africa's Itumeleng Khune (2010), and Wales's Wayne Hennessey (2022).
The match with the most dismissals was Portugal vs Netherlands of 2006, when referee Valentin Ivanov dished out 4 red cards, 2 for each team.
The referee who has sent off the most players is Arturo Brizio Carter, 7 red cards, in 5 different matches (also a record).
The team that has the greatest number of expulsed players is Brazil, with 11 – in 9 different matches, a record shared with Argentina. The team whose opponents have received the most red cards is Germany (incl. West Germany), with 20 – in 18 different matches, also a record.
The 2006 World Cup had the highest amount of red cards: a total of 28 players were sent off (in 20 matches, also a record).
Answer: Plácido Galindo was the first player to be sent off in a World Cup match, playing for Peru against Romania in a 1930 match officiated by Alberto Warnken. Although physical red cards were introduced from 1970, they were not put in practice until the 1974 World Cup, when referee Doğan Babacan sent off Chile's Carlos Caszely during a match against West Germany.
Question: Classify the following numbers as 'odd' or 'even' - 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16.
Answer: 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even'.
Question: How many strings does a bass guitar have?
Answer: Most bass guitars have four strings.  Because the bass is designed for lower frequency than standard (usually six-string) guitars, the bass guitar strings are thicker and heavier than standard guitar strings.

Some basses are made with five or even six strings.  This allows the bassist to hit higher or lower notes than the standard bass guitar range, which can allow more creative freedom.  Four strings is still considered the standard for bass guitars, though.
Question: Based on this paragraph about Massimo Bray, what language do you think he writes in?
Evidence: Massimo Bray is an academic publisher and a magazine editor. He was on the editorial board of the Italian Institute of Human Sciences until 1994. He is the cofounder of Notte della Taranta, which is among the most popular music festivals in Europe. He launched a blog on the Italian-language version of the Huffington Post.
Answer: Massimo Bray likely writes in Italian.
Question: Why is March 14th Pi day?
Answer: The number Pi is 3.14159265... and so on. The first three numbers, 3.14, correspond with the 14th of March in the American calendar notation, with March being the third month.
Question: What are the top 5 rock bands of all time?
Answer: The top 5 rock bands of all time is a difficult question as most individuals have different tastes and like various versions of rock.  There could be an argument that U2 belongs on that list given it's extensive catalog of music and presence on a global stage.  Others that might be considered include Led Zeppelin, The Grateful Dead, KISS, Radiohead.  This questions really comes down to what bands have grossed the most from Album sales & Touring receipts.  Any interesting question no doubt but comes down to their commercial success as well as the likes & dislikes of the fan.
Question: Name some of the popular gaming consoles in the 2020s
Answer: PlayStation 5, Xbox series X, Nintendo Switch, and Steam Deck
Question: What are the commonly used datasets fit for this type of precise reasoning mentioned in this paper?
Evidence: Reasoning is part of our every day routine: we hear Natural Language (NL) sentences, we participate in dialogues, we read books or legal documents. Successfully understanding, participating or communicating with others in these situations presupposes some form of reasoning: about individual sentences, whole paragraphs of legal documents, small or bigger pieces of dialogue and so on. The human reasoning performed in these different situations cannot be explained by a single rigid system of reasoning, plainly because reasoning is performed in different ways in each one of them. Consider the following example:
Three representatives are needed.
If a human reasoner with expert knowledge was to interpret the above utterance in a legal context, s/he will most probably judge that a situation where more than three references are provided could be compatible with the semantics of the utterance. To the contrary, if the same reasoner was to interpret the above as part of a casual, everyday conversation, then three would most likely be interpreted as exactly three, making the same situation incompatible with the utterance. In this paper, we want to focus on precise inference, inference which is either performed by experts or normal people after taking some time to consider the inferences that follow or not from a set of premises. The problem one encounters in testing systems of this type of reasoning is the fact that no large scale datasets of this sort of inference exist. The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
Answer: Logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.
Question: What is a Treasury Bond?
Answer: Treasury bonds (T-bonds) are government debt securities issued by the U.S. Federal government that have maturities greater than 20 years. T-bonds earn periodic interest until maturity, at which point the owner is also paid a par amount equal to the principal.
Question: How are volcanoes formed?
Answer: Volcanoes are formed when magma from within the Earth's upper mantle works its way to the surface. At the surface, it erupts to form lava flows and ash deposits. Over time as the volcano continues to erupt, it will get bigger and bigger.
Question: How is LIWC incorporated into this system?
Evidence: LAXARY includes a modified LIWC model to calculate the possible scores of each survey question using PTSD Linguistic Dictionary to fill out the PTSD assessment surveys which provides a practical way not only to determine fine-grained discrimination of physiological and psychological health markers of PTSD without incurring the expensive and laborious in-situ laboratory testing or surveys, but also obtain trusts of clinicians who are expected to see traditional survey results of the PTSD assessment.
LAXARY includes a modified LIWC model to calculate the possible scores of each survey question using PTSD Linguistic Dictionary to fill out the PTSD assessment surveys which provides a practical way not only to determine fine-grained discrimination of physiological and psychological health markers of PTSD without incurring the expensive and laborious in-situ laboratory testing or surveys, but also obtain trusts of clinicians who are expected to see traditional survey results of the PTSD assessment.
Answer: The answers are shown as follows:
* to calculate the possible scores of each survey question using PTSD Linguistic Dictionary
Question: How fast can an ostrich run?
Answer: An ostrich can run up to 56 mph (90 km/h). It is the fastest land animal after the cheetah.
Question: Identify which animal is domesticated or wild: Sheep, Caracal
Answer: Sheep is domesticated, Caracal is wild
Question: In the series A Song of Ice and Fire, who is the founder of House Lannister?
Answer: Lann the Clever
Question: What do the authors achieve by applying E2T to a basic sequence-to-sequence model?
Evidence: We proposed to leverage on linked entities to improve the performance of sequence-to-sequence models on neural abstractive summarization task. Linked entities are used to guide the decoding process based on the summary topic and commonsense learned from a knowledge base. We introduced Entity2Topic (E2T), a module that is easily attachable to any model using an encoder-decoder framework. E2T applies linked entities into the summarizer by encoding the entities with selective disambiguation and pooling them into one summary topic vector with firm attention mechanism. We showed that by applying E2T to a basic sequence-to-sequence model, we achieve significant improvements over the base model and consequently achieve a comparable performance with more complex summarization models.
We showed that by applying E2T to a basic sequence-to-sequence model, we achieve significant improvements over the base model and consequently achieve a comparable performance with more complex summarization models.
Answer: They achieve significant improvements over the base model and consequently achieve a comparable performance with more complex summarization models.
Question: How is the dataset collected?
Evidence: We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below:
We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.
Answer: The answers are shown as follows:
* A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al
* HEOT obtained from one of the past studies done by Mathur et al
Question: From the passage provided, extract the names of elementary particles and arrange them in alphabetical order
Evidence: In particle physics, an elementary particle or fundamental particle is a subatomic particle that is not composed of other particles. Particles currently thought to be elementary include electrons, the fundamental fermions (quarks, leptons, antiquarks, and antileptons, which generally are matter particles and antimatter particles), as well as the fundamental bosons (gauge bosons and the Higgs boson), which generally are force particles that mediate interactions among fermions.A particle containing two or more elementary particles is a composite particle.
Answer: 1. Antileptons
2. Antiquarks
3. Electrons
4. Gauge bosons
5. Higgs boson
6. Leptons
7. Quarks
Question: What are several unique ways in which you could travel from Seattle, Washington to Miami Florida?
Answer: Driving a car, riding on a plane, riding on a train, running, walking, riding a donkey, riding a horse, crab walking, riding a boat.
Question: What are the best woods for smoking?
Answer: Oak is the quintessential go-to for smoking meat. It is a great place to start if you are a newbie to smoking.  It will lend a medium to a strong flavor that is seldom overpowering.
	- Best Meat to Smoke: Lamb, beef, brisket, and sausages
	  
Hickory is the most versatile choice as it can be used to smoke wood in many ways. Be careful, though, because too much hickory flavor will cause your meat to have a more bitter flavor. It has a sweet, savory, and hearty, a bit bacony.
	- Best Meat to Smoke: Larger cuts of ribs and pork shoulders, as well as almost all red meat and poultry.
	  
Maple is one of the most subtle smoking wood, it will impart a more subtle smoke flavor.  Use maple for a sweet, light, mild smokiness.
	- Best Meat to Smoke: Poultry, pork, game foul
	  
Mesquite is a hardy wood packing intense flavor. It is recommended for grilling, or to be used in smaller quantities. It is famous for its strong, intense, and unique.
	- Best Meat to Smoke: Red Meat and for adding additional flavor when grilling

Pecan will lend a rich, sweet, nutty flavor. In fact, the wood is so sweet that you may want to use a combination of it with another harder wood to balance out the flavor.
	- Best Meat to Smoke: Briskets, roasts, and ribs
	  
Applewood is mild and sweet, lending a mellow flavor. Apple smoke flavors take a while to permeate the meat, so anticipate several hours of smoking.
	- Best Meat to Smoke: Chicken, wild foul, pork
	  
Alder has a very light and sweet flavor profile. It lends a delicate and sweet characteristic.
	- Best Meat to Smoke: Fish, like salmon and other Pacific Northwestern types.
	  
Cherry is mild and fruity, and when mixed with other hardwood like hickory, the two flavors compliment each other for an amazing result.
	- Best Meat to Smoke: Chicken, turkey, ham