DataHammer/scimrc huggingface dataset:
Question: Given these paragraphs about Asperger syndrome, after whom it was named?
Evidence: Asperger syndrome (AS), also known as Asperger's, is a neurodevelopmental condition characterized by significant difficulties in social interaction and nonverbal communication, along with restricted and repetitive patterns of behaviour and interests. The syndrome is no longer recognised as a diagnosis in itself, having been merged with other conditions into autism spectrum disorder (ASD). It was considered to differ from other diagnoses that were merged into ASD by relatively unimpaired spoken language and intelligence.

The syndrome was named after the Austrian pediatrician Hans Asperger, who, in 1944, described children in his care who struggled to form friendships, did not understand others' gestures or feelings, engaged in one-sided conversations about their favourite interests, and were clumsy. In 1994, the diagnosis of Asperger's was included in the fourth edition (DSM-IV) of the American Diagnostic and Statistical Manual of Mental Disorders; however, with the publication of DSM-5 in 2013 the syndrome was removed, and the symptoms are now included within autism spectrum disorder along with classic autism and pervasive developmental disorder not otherwise specified (PDD-NOS). It was similarly merged into autism spectrum disorder in the International Classification of Diseases (ICD-11) as of 2021.

The exact cause of Asperger's is poorly understood. While it has high heritability, the underlying genetics have not been determined conclusively. Environmental factors are also believed to play a role. Brain imaging has not identified a common underlying condition. There is no single treatment, and the UK's National Health Service (NHS) guidelines suggest that 'treatment' of any form of autism should not be a goal, since autism is not 'a disease that can be removed or cured'. According to the Royal College of Psychiatrists, while co-occurring conditions might require treatment, 'management of autism itself is chiefly about the provision of the education, training and social support/care required to improve the person's ability to function in the everyday world'. The effectiveness of particular interventions for autism is supported by only limited data. Interventions may include social skills training, cognitive behavioral therapy, physical therapy, speech therapy, parent training, and medications for associated problems, such as mood or anxiety. Autistic characteristics tend to become less obvious in adulthood, but social and communication difficulties usually persist.

https://en.wikipedia.org/wiki/Asperger_syndrome
Answer: The syndrome was named after the Austrian pediatrician Hans Asperger, who, in 1944, described children in his care who struggled to form friendships, did not understand others' gestures or feelings, engaged in one-sided conversations about their favourite interests, and were clumsy.
Question: What is the tallest structure in the Western Hemisphere?
Answer: The CN Tower in Toronto, Ontario, Canada is the tallest free-standing structure in the Western Hemisphere. It was completed in 1976, and was the tallest structure in the world from 1975 until 2007, when it was surpassed by the Burj Khalifa.
Question: Who is Silvio Micali?
Evidence: Silvio Micali (born October 13, 1954) is an Italian computer scientist, professor at the Massachusetts Institute of Technology and the founder of Algorand, a proof-of-stake blockchain cryptocurrency protocol. Micali's research at the MIT Computer Science and Artificial Intelligence Laboratory centers on cryptography and information security.

In 2012, he received the Turing Award for his work in cryptography along with Shafi Goldwasser for their work on zero-knowledge proofs.
Answer: Silvio Micali is a cryptography professor at MIT. He received a Turing award in 2012 along with Shafi Goldwasser for their work on zero-knowledge proofs. Micali also founded Algorand, which is a a proof-of-stake blockchain cryptocurrency protocol and company.
Question: What do their findings show?
Evidence: The NLP community is revisiting the role of linguistic structure in applications with the advent of contextual word representations (cwrs) derived from pretraining language models on large corpora BIBREF2, BIBREF3, BIBREF4, BIBREF5. Recent work has shown that downstream task performance may benefit from explicitly injecting a syntactic inductive bias into model architectures BIBREF6, even when cwrs are also used BIBREF7. However, high quality linguistic structure annotation at a large scale remains expensive—a trade-off needs to be made between the quality of the annotations and the computational expense of obtaining them. Shallow syntactic structures (BIBREF8; also called chunk sequences) offer a viable middle ground, by providing a flat, non-hierarchical approximation to phrase-syntactic trees (see Fig. FIGREF1 for an example). These structures can be obtained efficiently, and with high accuracy, using sequence labelers. In this paper we consider shallow syntax to be a proxy for linguistic structure.
While shallow syntactic chunks are almost as ubiquitous as part-of-speech tags in standard NLP pipelines BIBREF9, their relative merits in the presence of cwrs remain unclear. We investigate the role of these structures using two methods. First, we enhance the ELMo architecture BIBREF0 to allow pretraining on predicted shallow syntactic parses, instead of just raw text, so that contextual embeddings make use of shallow syntactic context (§SECREF2). Our second method involves classical addition of chunk features to cwr-infused architectures for four different downstream tasks (§SECREF3). Shallow syntactic information is obtained automatically using a highly accurate model (97% $F_1$ on standard benchmarks). In both settings, we observe only modest gains on three of the four downstream tasks relative to ELMo-only baselines (§SECREF4).
Recent work has probed the knowledge encoded in cwrs and found they capture a surprisingly large amount of syntax BIBREF10, BIBREF1, BIBREF11. We further examine the contextual embeddings obtained from the enhanced architecture and a shallow syntactic context, using black-box probes from BIBREF1. Our analysis indicates that our shallow-syntax-aware contextual embeddings do not transfer to linguistic tasks any more easily than ELMo embeddings (§SECREF18).
Overall, our findings show that while shallow syntax can be somewhat useful, ELMo-style pretraining discovers representations which make additional awareness of shallow syntax largely redundant.
Overall, our findings show that while shallow syntax can be somewhat useful, ELMo-style pretraining discovers representations which make additional awareness of shallow syntax largely redundant.
Answer: While shallow syntax can be somewhat useful, ELMo-style pretraining discovers representations which make additional awareness of shallow syntax largely redundant.
Question: What are the components of sound reproduction that are important in a home setting?
Answer: To accurately reproduce sound at home the important things to focus on are the placement of the speakers, the location of the listener, shape of the room, and the materials inside the room. Such as the placement of the speakers should follow the according setup guidelines, for example stereo speakers will be places at equal angles and height to the listener, placed a few feet away from any walls. The shape of the room, and materials define how the sound reflects in the space, with the goal being to reduce overlapping echo from flat surfaces.
Question: Who won the FIFA World Cup 2014?
Answer: Germany won the world cup in 2014. In the final, Germany defeated Argentina 1-0 with a goal scored by Mario Götze. The German team, led by coach Joachim Löw, celebrated their fourth title win. This win made it the nation with the second-highest score, only exceeded by Brazil, which won the world cup five times.
Question: What is the algorithm used for the classification tasks?
Evidence: To test whether topic models can be used for dating poetry or attributing authorship, we perform supervised classification experiments with Random Forest Ensemble classifiers. We find that we obtain better results by training and testing on stanzas instead of full poems, as we have more data available. Also, we use 50 year slots (instead of 25) to ease the task.
To test whether topic models can be used for dating poetry or attributing authorship, we perform supervised classification experiments with Random Forest Ensemble classifiers. 
Answer: The answers are shown as follows:
* Random Forest Ensemble classifiers
Question: Identify which instrument is string or percussion: Tumba, Villu Paatu
Answer: Tumba is percussion, Villu Paatu is string.
Question: Who says Pi is Wrong?
Answer: In the early 21st century, Bob Palais (a mathematician) and Michael Hartl (a physicist) declared that Pi, the traditional circle constant, was wrong.  Their argument is that Pi, defined as the ratio of a circle's circumference to its diameter, is not the most effective or clear way to describe the geometry of a circle.

Palais and Hartl contend that the ratio of a circle's circumference to its radius is a more useful constant.  They chose to name this new constant Tau.  Arithmetically, Tau = 2*Pi.

They argued that Tau produces cleaner mathematical equations throughout physics, mathematics, and statistics and, further, that it makes it easier to teach trigonometry and analytic geometry to new learners.
Question: What is the name of the wine that can only be named for its specific growing location in France?
Answer: Champagne is the only type of wine that can use that name if it is grown and produced in Champagne, France.
Question: Why is pricing important in the overall strategy of a product?
Answer: When different profit levers like price, variable cost, fixed cost, volume of goods sold are compared, studies show that a 1% improvement in price can create the maximum operating profit improvement of 11.5% compared to only 2.3% through fixed costs. This goes to show that pricing can have a monumental impact on the bottom line. 

This can be achieved by understanding different pricing strategies and how they can be executed through pricing tactics. The different pricing strategy includes using - 1. Cost-side factors (like fixed, variable costs, incremental break-even analysis); 2. Customer-side factors (like Economic Value Analysis (EVA); Price Elasticity of Demand; Measuring willingness to pay) 3. Competitive environment factors. 

Based on the business context, these strategies can be implemented with tacts like price discrimination (e.g., bundling); non-linear pricing; price promotions (e.g., hi-lo pricing); and most of all understanding consumer psychology with respect to pricing (anchoring effect, decoy pricing, compromise effect, charm pricing, etc).
Question: Which names below are more used for boys or girls in 2023
Answer: Amy, Babara, Claire, Daniel, Eric, Florence, George, Hilary, Illy, Lilly, Molly, Nancy, Olive, Paula, Robert, Stanley, Tara, Urs, Victoria, Wallace, Young, Zara
Question: Name the mathematicians whose stories are the focus of the book Hidden Figures
Evidence: Hidden Figures: The American Dream and the Untold Story of the Black Women Who Helped Win the Space Race is a 2016 nonfiction book written by Margot Lee Shetterly. Shetterly started working on the book in 2010. The book takes place from the 1930s through the 1960s, depicting the particular barriers for Black women in science during this time, thereby providing a lesser-known history of NASA. The biographical text follows the lives of Katherine Johnson, Dorothy Vaughan, and Mary Jackson, three mathematicians who worked as computers (then a job description) at NACA and NASA, during the space race. They overcame discrimination there, as women and as African Americans. Also featured is Christine Darden, who was the first African-American woman to be promoted into the Senior Executive Service for her work in researching supersonic flight and sonic booms.

The book reached number one on The New York Times Non-Fiction Best Sellers list and got the Anisfield-Wolf Book Award for Nonfiction in 2017. The book was adapted as a film by the same name, released in 2016, that was nominated for three Oscars. It received numerous other awards.
Answer: Katherine Johnson, Dorothy Vaughan, Mary Jackson
Question: What is the movie The Airzone Solution
Answer: The Airzone Solution (stylized as The AirZone Solution?), is a 1993 British sci-fi-thriller film, produced by BBV. It was written by Nicholas Briggs and directed by Bill Baggs. It stars Colin Baker, Nicola Bryant, Peter Davison, Sylvester McCoy, Jon Pertwee and Heather Tracy.
Question: How does the author construct the map?
Evidence: Our premise is that we can generate informative maps using geolocated information available on social media; therefore, we guide the blog collection process with the constraint that we only accept blogs that have specific location information. Moreover, we aim to find blogs belonging to writers from all 50 U.S. states, which will allow us to build U.S. maps for various dimensions of interest.
We first started by collecting a set of profiles of bloggers that met our location specifications by searching individual states on the profile finder on http://www.blogger.com. Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth. It is important to note that the profile finder only identifies users that have an exact match to the location specified in the query; we thus built and ran queries that used both state abbreviations (e.g., TX, AL), as well as the states' full names (e.g., Texas, Alabama).
After completing all the processing steps, we identified 197,527 bloggers with state location information. For each of these bloggers, we found their blogs (note that a blogger can have multiple blogs), for a total of 335,698 blogs. For each of these blogs, we downloaded the 21 most recent blog postings, which were cleaned of HTML tags and tokenized, resulting in a collection of 4,600,465 blog posts.
We first started by collecting a set of profiles of bloggers that met our location specifications by searching individual states on the profile finder on http://www.blogger.com. Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth. It is important to note that the profile finder only identifies users that have an exact match to the location specified in the query; we thus built and ran queries that used both state abbreviations (e.g., TX, AL), as well as the states’ full names (e.g., Texas, Alabama).
Answer: The authors began by searching states on the profile finder at http://www.blogger.com to gather profiles of a group of location-based bloggers. Starting from this list, they can locate the user's profile page and then extract additional information.
Question: Identify which animal is domesticated or wild: Llama, House cricket
Answer: House cricket is wild, Llama is domesticated
Question: Classify each of the following teams as college or professional: Florida Gators, Miami Marlins, Miami Hurricanes, Jacksonville Jaguars, Georgia Bulldogs, Atlanta Braves, Tampa Rays
Answer: Florida Gators: College
Miami Marlins: Professional
Miami Hurricanes: College
Jacksonville Jaguars: Professional
Georgia Bulldogs: College
Atlanta Braves: Professional
Tampa Rays: Professional
Question: Are red roses typically considered an appropriate romantic gift?
Answer: Yes, a bouquet of red roses is a classic romantic gesture
Question: Can you own a car in a state with an out of state driver license?
Answer: The short is answer is yes for most of the states. For example in the state of Washington you can register a car with an out of state  license as long as you don't plan to become a resident of the state. There are ofcourse rules around what it takes to become a resident of the state and a government website is ideal for it.
Question: Which is more important, Nature or Nurture?
Answer: The Nature vs Nurture argument has existed for as long as humans have been able to consider complex thought. The debate as to whether the nature (the intrinsic nature, the attitude, or the predisposition), or the nurture (the care, the teachings, or the conditioning) of a child is more responsible for who or what they become in adult life. 

Largely it is considered that humans are a product of context (Nurture). The situations in which we are raised have a considerable bearing on who we become in later life. But, it's not necessarily so black and white. There are countless examples of children having been born into extremely difficult circumstances, who have grown to be well rounded, high contributing members of society. 

As a result, It would be reasonable to assume that Nature is a strong factor, regardless of the Nurture (the environment) in which a person is raised. After all, there's no guarantee that someone growing up in an abusive household will then abuse others themselves. So then is Nature more important? 

The difficulty with the debate is that no one individual is the same as another. Experiments to establish the efficacy of Nature vs Nurture are difficult to perform. Not only from a human rights perspective, but additionally from a variable standpoint as well. Really there are far too many variables to control with regards to human nature, that any imposition of control on the Nurture element would be imbalanced. 

When considering this question, it's important to recognise that developmental psychology is still in its early stages, and to understand that both Nature AND Nurture are necessary factors in human development.
Question: What is the entity?
Evidence: An entity is an object or set of objects in the real world and can be referenced in a text with a proper name, noun or pronoun BIBREF12. The examples (UNKREF6–UNKREF8) show corresponding sentences that contain the named mention `John', the nominal mention `the boy' and the pronominal mention `he'. This distinction between names on the one hand and pronominal or nominal mentions on the other can also be applied to the broad semantic set of named entities from the legal domain, see (UNKREF9–UNKREF11). Thus, (UNKREF6, UNKREF9) contain actual named entities.
John is 8 years old.
The boy is 8 years old.
He is 8 years old.
The BGB regulates the legal relations between private persons.
The law regulates the legal relations [...].
It regulates the legal relations [...].
Basically, legal entities are either designations or references. A designation (or name) is the title of a legal document. In law texts, the title is strictly standardised and consists of a long title, short title and an abbreviation BIBREF13. The title of the Act on the Federal Constitutional Court is: `Gesetz über das Bundesverfassungsgericht (Bundesverfassungsgerichtsgesetz – BVerfGG)', where `Gesetz über das Bundesverfassungsgericht' is the long title, `Bundesverfassungsgerichtsgesetz' is the short title, and `BVerfGG' is the abbreviation. A reference to a legal norm is also fixed with rules for short and full references BIBREF13. Designations or references of binding individual acts such as regulations or contracts, however, are not uniformly defined.
A fundamental characteristic of the published decisions, that are the basis of our dataset, is that all personal information have been anonymised for privacy reasons. This affects the classes person, location and organization. Depending on the respective federal court, different rules were used for this anonymisation process. Named entities were replaced by letters or abbreviated (UNKREF14), sometimes ellipsis were used (UNKREF15, UNKREF16). Some anonymised locations are mentioned with terms such as “street”, “place”, “avenue”, etc. that are part of this named entity (UNKREF16).
Fernsehmoderator G. PER
`television presenter G.'
Firma X... UN
`company X...'
in der A-Straße STR in ... ST
`in the A-Street in ...'
An entity is an object or set of objects in the real world and can be referenced in a text with a proper name, noun or pronoun (Linguistic Data Consortium, 2008).
Answer: An entity is an object or set of objects in the real world and can be referenced in a text with a proper name, noun or pronoun.
Question: Who first used the term "glass ceiling"?
Evidence: A glass ceiling is a metaphor usually applied to women, used to represent an invisible barrier that prevents a given demographic from rising beyond a certain level in a hierarchy. No matter how invisible the glass ceiling is expressed, it is actually a difficult obstacle to overcome. The metaphor was first used by feminists in reference to barriers in the careers of high-achieving women. It was coined by Marilyn Loden during a speech in 1978. In the United States, the concept is sometimes extended to refer to racial inequality in the United States. Minority women in white-majority countries often find the most difficulty in "breaking the glass ceiling" because they lie at the intersection of two historically marginalized groups: women and people of color. East Asian and East Asian American news outlets have coined the term "bamboo ceiling" to refer to the obstacles that all East Asian Americans face in advancing their careers. Similarly, a multitude of barriers that refugees and asylum seekers face in their search for meaningful employment is referred to as canvas ceiling.
Answer: The term "glass ceiling" was first used by Marilyn Loden in a 1978 speech.
Question: Classify each of the following items as being found in a gym or a bar: Dumbbells, Tequila, Vodka, Barbells, Basketball Court, Beer, Sauna, Locker Room
Answer: Dumbbells: Gym
Tequila: Bar
Vodka: Bar
Barbells: Gym 
Basketball Court: Gym 
Beer: Bar 
Sauna: Gym
Locker Room: Gym
Question: Do the authors' methods outperform the current state-of-the-art in three out of six transfer directions?
Evidence: Several works have been proposed to learn multilingual word embeddings, which are then combined to perform cross-lingual document classifications. These word embeddings are trained on either word alignments or sentence-aligned parallel corpora. To provide reproducible benchmark results, we use MultiCCA word embeddings published by BIBREF3 .
There are multiple ways to combine these word embeddings for classification. We train a simple one-layer convolutional neural network (CNN) on top of the word embeddings, which has shown to perform well on text classification tasks regardless of training data size BIBREF4 . Specifically, convolutional filters are applied to windows of word embeddings, with a max-over-time pooling on top of them. We freeze the multilingual word embeddings while only training the classifier. Hyper-parameters such as convolutional output dimension, window sizes are done by grid search over the Dev set of the same language as the train set.
For comparison, we have evaluated its performance on the original subset of RCV2 as used in previous publications on cross-lingual document classification: we are able to outperform the current state-of-the-art in three out of six transfer directions.
Answer: Yes, they do.