DataHammer/scimrc huggingface dataset:
Question: How do people in Pittsburgh say "you all"?
Answer: People that speak with a Pittsburgh accent typically pronounce "you all" as "yinz". This is a unique feature of the infamous Pittsburgh accent, and is a source of pride and unity in the city of Pittsburgh.
Question: What is GraphQL?
Answer: GraphQL is an open-source data query language and data manipulation language for APIs, and a query runtime engine.
Question: What is the meaning of life?
Answer: The meaning of life, the complete answer to the ultimate question of life, the universe and everything, is...

42.

If you want to find out what the ultimate question actually is, you're going to need a bigger AI.
Question: Name five NFL teams in the AFC.
Answer: Five NFL teams in the AFC are the Buffalo Bills, New York Jets, Tennessee Titans, Houston Texans, and Las Vegas Raiders.
Question: what language is spoken in Brazil?
Answer: Portuguese is the official and national language of Brazil being widely spoken by most of the population. Brazil is the most populous Portuguese-speaking country in the world, with its lands comprising the majority of Portugal’s former colonial holdings in the Americas.
Question: What is Barbacoa?
Evidence: Barbacoa is a form of cooking meat that originated in the Caribbean with the Taíno people, who called it by the Arawak word barbaca, from which the term "barbacoa" derives, and ultimately, the word 'barbecue". In contemporary Mexico, it generally refers to meats or whole sheep or whole goats slow-cooked over an open fire or, more traditionally, in a hole dug in the ground covered with agave (maguey) leaves, although the interpretation is loose, and in the present day (and in some cases) may refer to meat steamed until tender. This meat is known for its high fat content and strong flavor, often accompanied with onions and cilantro (coriander leaf).
Answer: Barbacoa is a style of slow-cooking meat from the Caribbean area. In Mexico, it's generally used on sheep or goats that are buried in the ground and covered with leaves after roasting. Typical seasonings included onions and cilantro.
Question: Name some of the bravolebrities from The Real Housewives of Beverly Hills
Answer: Crystal Kung Minkoff, Dorit Kemsley, Erika Jayne, Lisa Rinna, Garcelle Beauvais, Kyle Richards, Sutton Stracke and Kathy Hilton
Question: What do the authors propose in this paper?
Evidence: There have been many advances in machine learning methods which help machines understand human behavior better than ever. One of the most important aspects of human behavior is emotion. If machines could detect human emotional expressions, it could be used to improve on verity of applications such as marketing BIBREF0 , human-computer interactions BIBREF1 , political science BIBREF2 etc.
Emotion in humans is complex and hard to distinguish. There have been many emotional models in psychology which tried to classify and point out basic human emotions such as Ekman's 6 basic emotions BIBREF3 , Plutchik's wheel of emotions BIBREF4 , or Parrott's three-level categorization of emotions BIBREF5 . These varieties show that emotions are hard to define, distinguish, and categorize even for human experts.
By adding the complexity of language and the fact that emotion expressions are very complex and context dependant BIBREF6 , BIBREF7 , BIBREF8 , we can see why detecting emotions in textual data is a challenging task. This difficulty can be seen when human annotators try to assign emotional labels to the text, but using various techniques the annotation task can be accomplished with desirable agreement among the annotators BIBREF9 .
In this work, we argue that creating a model that can better capture the context and sequential nature of text , can significantly improve the performance in the hard task of emotion detection. We show this by using a recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole, and we show that this can improve the final performance significantly.
Answer: A recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole.
Question: What kind of fungi are Ochrolechiaceae
Answer: The Ochrolechiaceae are a family of lichenized fungi in the order Pertusariales.
Question: What languages are spoken in Tunisia?
Evidence: The official language of Tunisia is Modern Standard Arabic. The vast majority of Tunisia's population is Arab and Muslim. Vernacular Tunisian Arabic is the most spoken, and French also serves as an administrative and educational language in some contexts, but it has no official status.

Tunisia is well integrated into the international community. It is a member of the United Nations, La Francophonie, the Arab League, the OIC, the African Union, the COMESA, the Non-Aligned Movement, the International Criminal Court, and the Group of 77, among others. It maintains close economic and political relations with some European countries, particularly with France, and Italy, due to their geographical proximity. Tunisia also has an association agreement with the European Union and has attained the status of a major non-NATO ally of the United States.
Answer: The official language of Tunisia is Modern Standard Arabic, though vernacular Tunisian Arabic is the most spoken. French also serves as an administrative and educational language in some contexts, but it has no official status.
Question: Given this paragraph about composer Paul Lansky, did he have an advanced degree?
Evidence: Paul Lansky (born 1944) is an American composer. He was educated at Manhattan's High School of Music and Art, Queens College and Princeton University, studying with George Perle and Milton Babbitt, among others. He received his Ph.D. in music from Princeton in 1973. His doctoral dissertation consisted of an essay titled "Affine music" and a composition of string quartet.Originally intending to pursue a career in performance, during 1965–66 he played the French horn with the Dorian Wind Quintet. He left the group to attend graduate school. From 1969 until his retirement in 2014 he was on the faculty at Princeton University where he retired as the William Shubael Conant Professor of Music.
Answer: Yes. Paul Lansky graduated from Princeton University with a Ph.D in music.
Question: Give me a list of the best true crime podcast to listen to
Answer: Morbid, Crime Junkie, My Favorite Murder, 2020, Dateline
Question: Identify which animal species is alive or extinct: Thylacosmilus, Swift Fox
Answer: Swift Fox is alive, Thylacosmilus is extinct
Question: What is the biggest company in the world in terms of market capitlization?
Answer: In terms of market capitalisation, Apple, Inc. is the biggest company valued at around 2.5 trillion dollars.
Question: What is been introduced into the Transformer architecture?
Evidence: The Transformer architecture BIBREF0 for deep neural networks has quickly risen to prominence in NLP through its efficiency and performance, leading to improvements in the state of the art of Neural Machine Translation BIBREF1, BIBREF2, as well as inspiring other powerful general-purpose models like BERT BIBREF3 and GPT-2 BIBREF4. At the heart of the Transformer lie multi-head attention mechanisms: each word is represented by multiple different weighted averages of its relevant context. As suggested by recent works on interpreting attention head roles, separate attention heads may learn to look for various relationships between tokens BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9.
The attention distribution of each head is predicted typically using the softmax normalizing transform. As a result, all context words have non-zero attention weight. Recent work on single attention architectures suggest that using sparse normalizing transforms in attention mechanisms such as sparsemax – which can yield exactly zero probabilities for irrelevant words – may improve performance and interpretability BIBREF12, BIBREF13, BIBREF14. Qualitative analysis of attention heads BIBREF0 suggests that, depending on what phenomena they capture, heads tend to favor flatter or more peaked distributions.
Recent works have proposed sparse Transformers BIBREF10 and adaptive span Transformers BIBREF11. However, the “sparsity" of those models only limits the attention to a contiguous span of past tokens, while in this work we propose a highly adaptive Transformer model that is capable of attending to a sparse set of words that are not necessarily contiguous. Figure FIGREF1 shows the relationship of these methods with ours.
Our contributions are the following:
We introduce sparse attention into the Transformer architecture, showing that it eases interpretability and leads to slight accuracy gains.
We propose an adaptive version of sparse attention, where the shape of each attention head is learnable and can vary continuously and dynamically between the dense limit case of softmax and the sparse, piecewise-linear sparsemax case.
We make an extensive analysis of the added interpretability of these models, identifying both crisper examples of attention head behavior observed in previous work, as well as novel behaviors unraveled thanks to the sparsity and adaptivity of our proposed model.
We introduce sparse attention into the Transformer architecture, showing that it eases interpretability and leads to slight accuracy gains.
Answer: Sparse attention.
Question: From the passage provided, extract the names of Jawaharlal Nehru's parents. Separate them with a comma.
Evidence: Jawaharlal Nehru was born on 14 November 1889 in Allahabad in British India. His father, Motilal Nehru (1861–1931), a self-made wealthy barrister who belonged to the Kashmiri Pandit community, served twice as president of the Indian National Congress, in 1919 and 1928. His mother, Swarup Rani Thussu (1868–1938), who came from a well-known Kashmiri Brahmin family settled in Lahore, was Motilal's second wife, his first having died in childbirth. Jawaharlal was the eldest of three children. His elder sister, Vijaya Lakshmi, later became the first female president of the United Nations General Assembly. His youngest sister, Krishna Hutheesing, became a noted writer and authored several books on her brother.
Answer: Motilal Nehru, Swarup Rani Thussu
Question: How many Presidential Medals of Freedom recipients did John Fitzgerald Kennedy choose?
Answer: JFK chose 31 recipients to receive Presidential Medals of Freedom
Question: What kind of animal is the Diaea dorsata
Answer: Diaea dorsata is one of the smaller crab spiders, with a palearctic distribution. Females can grow up to 6 mm, males up to 4 mm. Prosoma and legs are green, the opisthosoma is yellowish with a brown mark.
Question: From the passage provided, extract the date that National Beer Day is celebrated in the United States.
Evidence: National Beer Day is celebrated in the United States every year on April 7, marking the day that the Cullen–Harrison Act came into force after having been signed into law by President Franklin D. Roosevelt on March 22, 1933. This led to the Eighteenth Amendment being repealed on December 5, 1933, with the ratification of the Twenty-first Amendment to the U.S. Constitution. April 6, the day prior to National Beer Day, is known as New Beer's Eve.
Answer: National Beer Day is celebrated in the United States on April 7.
Question: How are the two datasets artificially overlapped?
Evidence: Two-talker overlapped speech is artificially generated by mixing these waveform segments. To maximize the speech overlap, we developed a procedure to mix similarly sized segments at around 0dB. First, we sort the speech segments by length. Then, we take segments in pairs, zero-padding the shorter segment so both have the same length. These pairs are then mixed together to create the overlapped speech data. The overlapping procedure is similar to BIBREF13 except that we make no modification to the signal levels before mixing . After overlapping, there's 150 hours data in the training, called 150 hours dataset, and 915 utterances in the test set. After decoding, there are 1830 utterances for evaluation, and the shortest utterance in the hub5e-swb dataset is discarded. Additionally, we define a small training set, the 50 hours dataset, as a random 50 hour subset of the 150 hours dataset. Results are reported using both datasets.
Two-talker overlapped speech is artificially generated by mixing these waveform segments. To maximize the speech overlap, we developed a procedure to mix similarly sized segments at around 0dB. First, we sort the speech segments by length. Then, we take segments in pairs, zero-padding the shorter segment so both have the same length. These pairs are then mixed together to create the overlapped speech data.
Answer: The answers are shown as follows:
* we sort the speech segments by length
* we take segments in pairs, zero-padding the shorter segment so both have the same length
* These pairs are then mixed together
Question: Why is it so hard now to name a favorite musician?
Answer: Compared to 10 years ago, this question has grown a lot harder to answer. In the old days, songs were indexed by song-writers. Prior to the digital age, the most popular media was physical cassettes, and it’s natural to group the cassettes by artists. Moreover, artists used to be the primary mechanism to discover new music. The cost of listening to new songs was usually high, since there were few ways to try new music, so fans tended to buy music from the same artists for quality (and taste) assurance.

With the evolution of music streaming and the decline of the recording industry, the granularity of “listening to music” has refined significantly. Listeners no longer need to buy whole cassettes, but can pick songs individually. Moreover, recommendation algorithms allow fans to discover music by popularity, genre or even mood.  As a result, music fans now sample from such a wide range of artists that it’s hard to name a single favorite. And due to the insane number of accessible songs, Even if you listen to a specific artist most frequently among your collection, you may still only like a small percentage of all the songs he/she wrote, which leads to the shaky conviction that you actually have a “favorite musician”.

On the other hand, I believe the palette for music depends a lot on “environment”. It could be a physical environment (eg. beach or gym), an action (eg. working or cooking), or a mood (eg. chill or psyched). The like or dislike towards a certain piece of music changes drastically when these variables change. To some extent, I view music more as a tool than as an art form, in that I seek certain utility from music at different times. However, music judgment for me is also not 100% a rational or utilitarian process. It still very much comes from intuition, though that intuition is heavily biased by environmental factors.
Question: What lexico-syntactic cues are used to retrieve sarcastic utterances?
Evidence: Generic Sarcasm. We first examine the different patterns learned on the Gen dataset. Table TABREF29 show examples of extracted patterns for each class. We observe that the not-sarcastic patterns appear to capture technical and scientific language, while the sarcastic patterns tend to capture subjective language that is not topic-specific. We observe an abundance of adjective and adverb patterns for the sarcastic class, although we do not use adjective and adverb patterns in our regex retrieval method. Instead, such cues co-occur with the cues we search for, expanding our pattern inventory as we show in Table TABREF31 .
Many of our sarcastic questions focus specifically on attacks on the mental abilities of the addressee. This generalization is made clear when we extract and analyze the verb, subject, and object arguments using the Stanford dependency parser BIBREF32 for the questions in the RQ dataset. Table TABREF32 shows a few examples of the relations we extract.
Hyperbole. One common pattern for hyperbole involves adverbs and adjectives, as noted above. We did not use this pattern to retrieve hyperbole, but because each hyperbolic sarcastic utterance contains multiple cues, we learn an expanded class of patterns for hyperbole. Table TABREF33 illustrates some of the new adverb adjective patterns that are frequent, high-precision indicators of sarcasm.
We learn a number of verbal patterns that we had not previously associated with hyperbole, as shown in Table TABREF34 . Interestingly, many of these instantiate the observations of CanoMora2009 on hyperbole and its related semantic fields: creating contrast by exclusion, e.g. no limit and no way, or by expanding a predicated class, e.g. everyone knows. Many of them are also contrastive. Table TABREF33 shows just a few examples, such as though it in no way and so much knowledge.
We observe an abundance of adjective and adverb patterns for the sarcastic class, although we do not use adjective and adverb patterns in our regex retrieval method. 
Many of our sarcastic questions focus specifically on attacks on the mental abilities of the addressee. This generalization is made clear when we extract and analyze the verb, subject, and object arguments using the Stanford dependency parser BIBREF32 for the questions in the RQ dataset. 
One common pattern for hyperbole involves adverbs and adjectives, as noted above. We did not use this pattern to retrieve hyperbole, but because each hyperbolic sarcastic utterance contains multiple cues, we learn an expanded class of patterns for hyperbole. 
We learn a number of verbal patterns that we had not previously associated with hyperbole, as shown in Table TABREF34 . 
Answer: The answers are shown as follows:
* adjective and adverb patterns
* verb, subject, and object arguments
* verbal patterns
Question: What benefits from the availability of the universal encoder?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
Answer: Directly translate source sentences into target sentences with the parent model.
Question: Can you tell me which of the following are days and which are months? Monday, Tuesday, Wednesday, October, March, February.
Answer: Monday, Tuesday and Wednesday are days, while October, March and February are months.
Question: What is Baumkuchen made of?
Evidence: Baumkuchen (German pronunciation: [ˈbaʊ̯mˌkuːxn̩] (listen)) is a kind of spit cake from German cuisine. It is also a popular dessert in Japan. The characteristic rings that appear in its slices resemble tree rings, and give the cake its German name, Baumkuchen, which literally translates to "tree cake" or "log cake".

History
It is disputed who made the first Baumkuchen and where it was first baked. One theory is that it was invented in the German town of Salzwedel, which is further popularized by the town itself. Another theory suggests it began as a Hungarian wedding cake.[citation needed] In Ein new Kochbuch (lit. "A New Cookbook"), the first cookbook written for professional chefs by Marx Rumpolt, there is a recipe for Baumkuchen. This publication puts the origin of Baumkuchen as far back at 1581, the year the cookbook was first published. Marx Rumpolt had previously worked as a chef in Hungary and Bohemia.[citation needed]

Characteristics

Baumkuchen baked on a spit
Traditionally, Baumkuchen is made on a spit by brushing on even layers of batter and then rotating the spit around a heat source. Each layer is allowed to brown before a new layer of batter is poured. When the cake is removed and sliced, each layer is divided from the next by a golden line, resembling the growth rings on a crosscut tree. A typical Baumkuchen is made up of 15 to 20 layers of batter. However, the layering process for making Baumkuchen can continue until the cakes are quite large. Skilled pastry chefs have been known to create cakes with 25 layers and weighing over 100 pounds (45 kg). When cooked on a spit, it is not uncommon for a finished Baumkuchen to be 3–4 feet (0.91–1.22 m) tall.

Baumkuchen ingredients are typically butter, eggs, sugar, vanilla, salt, and flour. Baking powder is not considered a traditional ingredient. The ratio of flour, butter and eggs is typically 1:1:2 respectively (i.e., 100 grams of flour, 100 grams of butter and 200 grams of eggs). The recipe can be varied by adding other ingredients, such as ground nuts, honey, marzipan, nougat and rum or brandy, to the batter or filling. Additionally, Baumkuchen may be covered with sugar or chocolate glaze. With some recipes, the fully baked and cooled Baumkuchen is first coated with marmalade or jam, and then covered with chocolate.
Answer: Baumkuchen ingredients are typically butter, eggs, sugar, vanilla, salt, and flour. Baking powder is not considered a traditional ingredient. The ratio of flour, butter and eggs is typically 1:1:2 respectively (i.e., 100 grams of flour, 100 grams of butter and 200 grams of eggs).