DataHammer/scimrc huggingface dataset:
Question: Which hosts Wikipedia ?
Evidence: Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history. It is consistently one of the 10 most popular websites ranked by Similarweb and formerly Alexa; as of 2023, Wikipedia was ranked the 5th most popular site in the world.
It is hosted by the Wikimedia Foundation, an American non-profit organization funded mainly through donations.
Answer: Wikipedia is hosted by the Wikimedia Foundation, an American non-profit organization.
Question: What was the first living creature sent into orbit around Earth?
Answer: Laika, a Russian street dog, went to space in 1957 on the satellite Sputnik 2.
Question: Draft an e-mail to a resort inquiring about information regarding a vow renewal celebration for my wife and me.
Answer: Hi Jane,

I am reaching out to obtain information about hosting a vow renewal celebration at your resort. You were listed as an event POC on the resort's website, please connect me with another colleague if you are not the right contact.

I outlined preliminary details below as a starting point for our discussion.
- Guest information
     - Tentative dates: July 11-16, 2022
     - Group size: 20-30
     - Group ages: 1 years old to elderly 

- Event(s):
     - Main Event: Vow Renewal/Celebration
          - My wife and I would like to have a vow renewal ceremony with dinner and dancing afterward. This will be largely a family gathering and I want it to be relaxed and memorable. 
          - We would like the vow renewal ceremony to take place outside and we are flexible on the dinner/dancing space.  
          - We would like the event to take place during the middle of stay (i.e., July 14).
     - Potential Additional Events: Welcome Party & Group Activity
          - We would like to block an area for "welcome drinks" one night and do a group activity one day (e.g., tennis tournament, chartering a boat).

Are you able to share any information around pricing for this group and hosting a private event?

Please let me know if you have any questions. Ideally you will provide preliminary information for me to review and then we can schedule a call for detailed questions. I'm looking forward to hearing from you soon.

Thank you,

John
Question: What are Canada’s three largest metropolitan areas?
Evidence: Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, making it the world's second-largest country by total area, with the world's longest coastline. It is characterized by a wide range of both meteorologic and geological regions. The country is sparsely inhabited, with the vast majority residing south of the 55th parallel in urban areas. Canada's capital is Ottawa and its three largest metropolitan areas are Toronto, Montreal, and Vancouver.

Indigenous peoples have continuously inhabited what is now Canada for thousands of years. Beginning in the 16th century, British and French expeditions explored and later settled along the Atlantic coast. As a consequence of various armed conflicts, France ceded nearly all of its colonies in North America in 1763. In 1867, with the union of three British North American colonies through Confederation, Canada was formed as a federal dominion of four provinces. This began an accretion of provinces and territories and a process of increasing autonomy from the United Kingdom, highlighted by the Statute of Westminster, 1931, and culminating in the Canada Act 1982, which severed the vestiges of legal dependence on the Parliament of the United Kingdom.
Answer: Toronto, Montreal, and Vancouver.
Question: What architecture does GPT use?
Answer: GPT uses the transformer architecture which is a neural network architecture based on the attention mechanism to find global dependencies between the input and the output. To be more specific, GPT belongs to a sub type in the transformer family that is called decoder-only transformer.
Question: Given a reference text about IMDB, tell me how it rates movies.
Evidence: As one adjunct to data, the IMDb offers a rating scale that allows users to rate films on a scale of one to ten.

IMDb indicates that submitted ratings are filtered and weighted in various ways to produce a weighted mean that is displayed for each film, series, and so on. It states that filters are used to avoid ballot stuffing; the method is not described in detail to avoid attempts to circumvent it. In fact, it sometimes produces an extreme difference between the weighted average and the arithmetic mean.
Answer: IMDB allows users to submit their reviews and ratings.  As the most extensive internet movie database and the 54th most visited website across the planet, the scale of reviews and ratings collected for movies is massive.  IMDB uses various mathematical calculations to determine the rating of movies.  Many users decide whether to spend time and money on a film solely on ratings from IMDB.  Because of IMDB's scale and impact, it is now a subsidiary of Amazon.
Question: Identify which animal species is alive or extinct: Phytosaurs, Leatherback Turtle
Answer: Leatherback Turtle is alive, Phytosaurs is extinct.
Question: Where did Rock and Roll come from?
Answer: Rock and Roll was derived from Jazz, Rhythm and Blues, and Country music that became popular in the mid 1950s in the USA.
Question: From the given text, Write some important points about song "Sunshine, Lollipops and Rainbows"
Evidence: "Sunshine, Lollipops and Rainbows" is a popular song sung by Lesley Gore. It was originally released on Gore's 1963 album Lesley Gore Sings of Mixed-Up Hearts. Composed by Marvin Hamlisch.

It was arranged by Claus Ogerman and produced by Quincy Jones.
Answer: 1. Lesley Gore is the singer of the well-known song "Sunshine, Lollipops and Rainbows." It first appeared on Lesley Gore Sings of Mixed-Up Hearts, Gore's 1963 album by Marvin Hamlisch, the composer.
2. It was created by Quincy Jones and produced by Claus Ogerman.
Question: What's special about Novy Urkarakh, the town in Russia?
Evidence: Novy Urkarakh (Russian: Новый Уркарах; Dargwa: Сагаси Уркарахъ) is a rural locality (a selo) in Urkarkhsky Selsoviet, Dakhadayevsky District, Republic of Dagestan, Russia. The population was 1,101 as of 2010. There are 9 streets. Dargins live there.
Answer: In Russia, the town of Novy Urkarakh is actually referred to as a selo. Selo's are very small rural communities spread across the country. In fact, Novy Urkarakh is so small that it is home to only 1,101 Dargins. Residents of the community get around and live on nine paved roads. Novy Urkarakh is that small!
Question: Why did Rome have so many bad emperors?
Answer: The Roman Empire had many bad emperors for a variety of reasons, including:

Inheritance: Unlike modern democracies, the Roman Empire was an inherited monarchy, meaning that power was passed down through the family. This often led to the appointment of inexperienced or unqualified heirs to the throne.
Political instability: The Roman Empire was plagued by political instability, particularly in the 3rd century AD, when there were frequent changes of leadership and power struggles between rival factions.
Assassinations and coups: Many emperors came to power through assassination or coup, which often resulted in instability and a lack of legitimacy.
Personal flaws: Some emperors were simply bad rulers due to personal flaws such as greed, cruelty, or incompetence.
External pressures: The Roman Empire faced a number of external pressures, including economic crises, military threats, and natural disasters. These challenges often put pressure on emperors to make unpopular decisions or take drastic measures that could harm their reputation.
Overall, the Roman Empire was a complex and challenging political system, and the challenges of maintaining power and stability often resulted in the appointment of less-than-ideal rulers.
Question: Choose the word which is different from the rest: tall, huge, thin, sharp, small
Answer: all except sharp are related to dimensions
Question: What is Game of Thrones based on?
Answer: Game of Thrones is based on the books written by George R. R. Martin called a Song of Ice and Fire.
Question: Tell me about the merger history of the Swiss bank UBS
Evidence: Swiss Bank Corporation was a Swiss investment bank and financial services company located in Switzerland. Prior to its merger, the bank was the third largest in Switzerland with over CHF 300 billion of assets and CHF 11.7 billion of equity.

Throughout the 1990s, SBC engaged in a large growth initiative, shifting its focus from traditional commercial banking into investment banking, in an effort to match its larger Swiss rival Credit Suisse. As part of this strategy, SBC acquired US-based investment bank Dillon Read & Co. as well as London-based merchant bank S.G. Warburg in the mid-1990s. SBC also acquired Chicago-based Brinson Partners and O'Connor & Associates. These acquisitions formed the basis for a global investment banking business.

In 1998, SBC merged with Union Bank of Switzerland to form UBS, the largest bank in Europe and the second largest bank in the world. The company's logo, which featured three keys, symbolizing "confidence, security, and discretion", was adopted by UBS after the 1998 merger. Although the combination of the two banks was billed as a merger of equals, it quickly became evident that from a management perspective, it was SBC that was buying UBS as nearly 80% of the top management positions were filled by legacy Swiss Bank professionals. Today, what was SBC forms the core of many of UBS's businesses, particularly UBS Investment Bank.
Answer: The Swiss Bank Corporation was a Swiss investment bank and financial services company located in Switzerland. Prior to its merger, the bank was the third largest in Switzerland with over CHF 300 billion of assets and CHF 11.7 billion of equity.

Throughout the 1990s, SBC engaged in a large growth initiative, shifting its focus from traditional commercial banking into investment banking, in an effort to match its larger Swiss rival Credit Suisse. As part of this strategy, SBC acquired US-based investment bank Dillon Read & Co. as well as London-based merchant bank S.G. Warburg in the mid-1990s. SBC also acquired Chicago-based Brinson Partners and O'Connor & Associates. These acquisitions formed the basis for a global investment banking business.

In 1998, SBC merged with Union Bank of Switzerland to form UBS, the largest bank in Europe and the second largest bank in the world. The company's logo, which featured three keys, symbolizing "confidence, security, and discretion", was adopted by UBS after the 1998 merger. Although the combination of the two banks was billed as a merger of equals, it quickly became evident that from a management perspective, it was SBC that was buying UBS as nearly 80% of the top management positions were filled by legacy Swiss Bank professionals. Today, what was SBC forms the core of many of UBS's businesses, particularly UBS Investment Bank.
Question: Who is the greater footballer of all time?
Answer: There is no universal consensus on who the greatest footballer of all time is. In the women's game, Marta of Brazil and Mia Hamm of the USA are considered to be two of the greatest footballers of all time. Both were forwards, and both were awarded the FIFA World Player of the Year multiple times - 6 in total for Marta, twice for Mia Hamm. In the men's game, Lionel Messi (Argentina & Barcelona) and Cristiano Ronaldo (Portugal and Real Madrid) are considered two of the greatest players of all time during modern times. Before them, Pele of Brazil and Diego Maradona of Argentina were often considered two of the best players of all time.
Question: How do learn how to ride a bicycle?
Answer: It takes small steps to learn how to ride a bicycle the first one is getting on your bike without losing to get down the balance. Next ride your bike in small distances in a soft area. Last go for longer and longer distances until you have a hang of it.
Question: How do they do in their illustrative example?
Evidence:  As an illustrative example, we first present a baseline model for Markov syntactic structure (POS induction) that treats a sequence of pre-trained word embeddings as observations. Then, we propose our novel approach, again using Markov structure, that introduces latent word embedding variables and a neural projector. Lastly, we extend our approach to more general syntactic structures.
As an illustrative example, we first present a baseline model for Markov syntactic structure (POS induction) that treats a sequence of pre-trained word embeddings as observations. Then, we propose our novel approach, again using Markov structure, that introduces latent word embedding variables and a neural projector. Lastly, we extend our approach to more general syntactic structures.
Answer: They first present a baseline model for Markov syntactic structure, and then introduce latent word embedding variables and a neural projector in their new approach, finally extend the approach to more general syntactic structures.
Question: Identify which instrument is string or percussion: Ferrinho, Bandurria
Answer: Bandurria is string, Ferrinho is percussion.
Question: How do they train models in this setup?
Evidence: The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).
The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).
Answer: The answers are shown as follows:
* Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).
Question: What is the role of whistleblowers in a democratic market economy?
Answer: Whistleblowers play an important role in a democratic market economy. With highly complex structured contracts, specifically in the financial sector, there’s asymmetric information due to limited access. Shareholders cannot adequately monitor the behavior of financial traders who can take risks, which when materialized shareholders will not be able to repay. There’s also a lack of efficient monitoring on part of the regulators/government agencies due to resource constraints or revolving doors among the staff working at these financial institutions and regulatory agencies. Thus, whistleblowers play a critical role in exposing potential fraud within their organizations.
Question: Identify which instrument is string or percussion: Rebana, Kudyapi
Answer: Kudyapi is string, Rebana is percussion.
Question: How are sentence splitting and tokenization performed?
Evidence: The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. The corpus comprises 8,275 sentences and 167,739 words in total. However, as mentioned above, only case presentation sections, headings and abstracts are annotated. The numbers of annotated entities are summarized in Table TABREF24.
Findings are the most frequently annotated type of entity. This makes sense given that findings paint a clinical picture of the patient's condition. The number of tokens per entity ranges from one token for all types to 5 tokens for cases (average length 3.1), nine tokens for conditions (average length 2.0), 16 tokens for factors (average length 2.5), 25 tokens for findings (average length 2.6) and 18 tokens for modifiers (average length 1.4) (cf. Table TABREF24). Examples of rather long entities are given in Table TABREF25.
Entities can appear in a discontinuous way. We model this as a relation between two spans which we call “discontinuous” (cf. Figure FIGREF26). Especially findings often appear as discontinuous entities, we found 543 discontinuous finding relations. The numbers for conditions and factors are lower with seven and two, respectively. Entities can also be nested within one another. This happens either when the span of one annotation is completely embedded in the span of another annotation (fully-nested; cf. Figure FIGREF12), or when there is a partial overlapping between the spans of two different entities (partially-nested; cf. Figure FIGREF12). There is a high number of inter-sentential relations in the corpus (cf. Table TABREF27). This can be explained by the fact that the case entity occurs early in each document; furthermore, it is related to finding and factor annotations that are distributed across different sentences.
The most frequently annotated relation in our corpus is the has-relation between a case entity and the findings related to that case. This correlates with the high number of finding entities. The relations contained in our corpus are summarized in Table TABREF27.
Sentence splitting and tokenization are performed using ScispaCy and its en_core_sci_md model.
Answer: Sentence splitting and tokenization are performed us_x001f_ing ScispaCy (https://allenai.github.io/scispacy/) and its en_core_sci_md model.
Question: Summarize best way to climb Mount Everest
Answer: To climb Mount Everest, prepare physically and mentally, gain high-altitude experience, climb during pre/post-monsoon seasons, choose a suitable route, and hire a reputable expedition company for support and logistics.
Question: Tell me whether these animals are herbivores or carnivores: deer, rabbits, cows, elephants, lions, wolves, polar bears, cheetahs
Answer: Deer are herbivores.
Rabbits are herbivores.
Cows are herbivores.
Elephants are herbivores.
Lions are carnivores.
Wolves are carnivores.
Polar bears are carnivores.
Cheetahs are carnivores.
Question: What is Kraft Dinner?
Answer: Kraft Dinner, also referred to as KD, is a macaroni-and-cheese product by Kraft. It is essentially the same boxed macaroni-and-cheese sold in the United States, but referred to by a different name.
Question: What data sets did the author use to train the machine translation system between the three languages?
Evidence: To train MT systems among the three languages, i.e., Japanese, Russian, and English, we used all the parallel data provided by Global Voices, more specifically those available at OPUS. Table TABREF9 summarizes the size of train/development/test splits used in our experiments. The number of parallel sentences for Ja INLINEFORM0 Ru is 12k, for Ja INLINEFORM1 En is 47k, and for Ru INLINEFORM2 En is 82k. Note that the three corpora are not mutually exclusive: 9k out of 12k sentences in the Ja INLINEFORM3 Ru corpus were also included in the other two parallel corpora, associated with identical English translations. This puts a limit on the positive impact that the helping corpora can have on the translation quality.
Even when one focuses on low-resource language pairs, we often have access to larger quantities of in-domain monolingual data of each language. Such monolingual data are useful to improve quality of MT, for example, as the source of pseudo-parallel data for augmenting training data for NMT BIBREF17 and as the training data for large and smoothed language models for PBSMT BIBREF4 . Table TABREF13 summarizes the statistics on our monolingual corpora for several domains including the news domain. Note that we removed from the Global Voices monolingual corpora those sentences that are already present in the parallel corpus.
https://dumps.wikimedia.org/backup-index.html (20180501) http://www.statmt.org/wmt18/translation-task.html https://www.yomiuri.co.jp/database/glossary/ http://www.cs.jhu.edu/~kevinduh/a/multitarget-tedtalks/ http://opus.nlpl.eu/Tatoeba-v2.php
We tokenized English and Russian sentences using tokenizer.perl of Moses BIBREF3 . To tokenize Japanese sentences, we used MeCab with the IPA dictionary. After tokenization, we eliminated duplicated sentence pairs and sentences with more than 100 tokens for all the languages.
To train MT systems among the three languages, i.e., Japanese, Russian, and English, we used all the parallel data provided by Global Voices, more specifically those available at OPUS.
Answer: They used all the parallel data provided by Global Voices, more specifically those available at OPUS.
Question: Can the model add new relations to the knowledge graph, or just new entities?
Evidence: In ConMask, we use a similar idea to select the most related words given some relationship and mask irrelevant words by assigning a relationship-dependent similarity score to words in the given entity description. We formally define relationship-dependent content masking as:
ConMask selects words that are related to the given relationship to mitigate the inclusion of irrelevant and noisy words. From the relevant text, ConMask then uses fully convolutional network (FCN) to extract word-based embeddings. Finally, it compares the extracted embeddings to existing entities in the KG to resolve a ranked list of target entities. The overall structure of ConMask is illustrated in Fig. 1 . Later subsections describe the model in detail.
In ConMask, we use a similar idea to select the most related words given some relationship and mask irrelevant words by assigning a relationship-dependent similarity score to words in the given entity description.
ConMask selects words that are related to the given relationship to mitigate the inclusion of irrelevant and noisy words.
Answer: The model does not add new relations to the knowledge graph.
Question: What is Flink?
Answer: Apache Flink is an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming data-flow engine written in Java and Scala. Flink executes arbitrary dataflow programs in a data-parallel and pipelined manner.
Question: What is the difference between worker bees and drone bees, and are there any other types of bees?
Answer: Workers are the smallest of the bee castes, but are by far the most numerous. All workers are female, and normally incapable of reproduction. They are unable to mate, but in a hopelessly queenless colony, workers may begin to lay unfertilized eggs, which develop into drones. Drones are the male honey bees. The only function of a drone is to fertilize a young queen bee. Queen bee function in the hive is one of production. Queen bee is normally the only reproductive female in the colony. Egg-laying begins in early spring, initiated when the first fresh pollen is brought home by the workers. Egg production will continue until fall, or as long as pollen is available. So there are three types of bees: workers, drones and a queen bee.