DataHammer/scimrc huggingface dataset:
Question: Given these paragraphs about Natural hydrogen, what are other forms of hydrogen obtained using different processes?
Evidence: Natural hydrogen (known as white hydrogen), is naturally occurring molecular hydrogen on or in Earth (as opposed to hydrogen produced in the laboratory or in industry). The name white hydrogen distinguishes it from green hydrogen, which is produced from renewable energy sources, and from grey, brown or black hydrogen, which is obtained from fossil sources or from the electrolysis of water. Natural hydrogen may be renewable, non-polluting and allows for lower cost operation compared to industrial hydrogen. Natural hydrogen has been identified in many source rocks in areas beyond the sedimentary basins where oil companies typically operate.

Origin of natural hydrogen
There are several sources of natural hydrogen:

- degassing of deep hydrogen from the Earth's crust and mantle;
- reaction of water with ultrabasic rocks (serpentinisation);
- contact of water with reducing agents in the Earth's mantle;
- interaction of water with freshly exposed rock surfaces (weathering);
- decomposition of hydroxyl ions in the structure of minerals;
- Natural radiolysis of water;
- decomposition of organic matter;
- biological activity
- Extraction
- Natural hydrogen is extracted from wells, mixed with other gases such as nitrogen or helium.

Several sources have been identified in France. Geologists Alain Prinzhofer and Eric Derville have demonstrated the existence of large reservoirs in a dozen countries, including Mali and the United States. However, their potential remains difficult to assess.

Numerous emanations on the ocean floor have been identified but are difficult to exploit. The discovery of a significant emergence in Russia in 2008 suggests the possibility of extracting native hydrogen in geological environments.

Geology
Natural hydrogen is generated continuously from a variety of natural sources. There are many known hydrogen emergences on mid-ocean ridges. Another of the known reactions, serpentinisation, occurs under the sea floor (in the oceanic crust).

Diagenetic origin (iron oxidation) in the sedimentary basins of cratons, notably in Russia. Other sources are being explored, such as mantle hydrogen, or hydrogen from radiolysis (natural electrolysis) or from bacterial activity. In France, the Alps and Pyrenees are suitable for exploitation. New Caledonia has hyperalkaline sources that show dihydrogen emissions. A large accumulation of natural hydrogen was discovered in Bourakebougou (Mali).

Characteristics
Dihydrogen is very soluble in fresh water, especially at depth (solubility increases with pressure).

https://en.wikipedia.org/wiki/Natural_hydrogen
Answer: Green hydrogen, which is produced from renewable energy sources, and from grey, brown or black hydrogen, which is obtained from fossil sources or from the electrolysis of water.
Question: Classify the cities based on whether they are located in Slovakia or Croatia.
Košice, Bratislava, Zagreb, Split, Petržalka, Rijeka
Answer: Slovakia - Bratislava, Košice, Petržalka
Croatia - Zagreb, Rijeka, Split
Question: Tell me which sport of ice hockey or baseball these positions belong: outfielder, pitcher, left winger, forward, right fielder, goalie, catcher, third baseman, left defenseman
Answer: Ice Hockey Positions: left winger, forward, goalie, left defenseman
Baseball Positions: outfielder, pitcher, right fielder, catcher, third baseman
Question: Does Rajasthan - the desert state of India, have winters?
Evidence: Summer
Due to the Desert Geography, frequently climb above 40 to 45 degrees Celsius in most places.

Due to its location, summers are the longest season in Rajasthan.

In this time tourist activities are very low.

Winter
The cold weather commences early in October and ends around the end of February and sometimes the temperatures reach nearly 2-degree celsius [*record in 1964 in JAIPUR the temperature reached 0-degree Celsius on 13 December.]
Answer: Summers are the longest season in Rajasthan where the temperature climbs upto 40-45 degree celsius in most places. However, Rajasthan does witness winters every year starting from the month of October until February. Sometimes the temperature can be as low as around 2 degrees as well.
Question: Which is better for you, red or green grapes?
Answer: In general, dark red and purple grapes are higher in antioxidants than green or white grapes. The antioxidants found in grapes can help protect your cells from free radicals, which are a natural byproduct of your body's biological processes
Question: From the passage provided, extract when the roaring lion photograph was shot
Evidence: The Roaring Lion is a black and white photographic portrait of a 67-year-old Winston Churchill as Prime Minister of the United Kingdom. The portrait was taken in 1941 by Armenian-Canadian photographer Yousuf Karsh in the Centre Block on Parliament Hill in Ottawa, Ontario, Canada.

Date and location of photography
The photograph was shot on December 30, 1941, in the Speaker's Chamber of the Canadian Speaker of the House of Commons at Parliament in Ottawa after Churchill delivered his "Some chicken, some neck" speech on World War II to Canadian members of parliament. The brief photo session was arranged by the Canadian prime minister, William Lyon Mackenzie King.
Answer: The photograph was shot on December 30, 1941
Question: What is New Zealand's most well-known wine that it produces?
Answer: New Zealand is known for its Sauvignon Blancs.
Question: Which two phases is the adversarial training framework included?
Evidence: Text generation can be formulated as follows: given the real data distribution INLINEFORM0 , the task is to train a generative model INLINEFORM1 where INLINEFORM2 can fit INLINEFORM3 well. In this formulation, INLINEFORM4 and INLINEFORM5 denotes a word in the vocabulary INLINEFORM6 .
Figure FIGREF3 shows the overview of our model ARAML. This adversarial training framework consists of two phases: 1) The discriminator is trained to assign higher rewards to real data than to generated data. 2) The generator is trained on the samples acquired from a stationary distribution with reward augmented MLE training objective. This training paradigm of the generator indeed constrains the search space with the MLE training objective, which alleviates the issue of unstable training.
This adversarial training framework consists of two phases: 1) The discriminator is trained to assign higher rewards to real data than to generated data. 2) The generator is trained on the samples acquired from a stationary distribu-tion with reward augmented MLE training objective. 
Answer: This adversarial training framework consists of two phases: 1) The discriminator is trained to assign higher rewards to real data than to generated data. 2) The generator is trained on the samples acquired from a stationary distribu-tion with reward augmented MLE training objective.
Question: Which other approaches do they compare with?
Evidence: Common methods that tackle the problem of class imbalance by modifying the data to create balanced datasets are undersampling and oversampling. Undersampling randomly removes instances from the majority class and is only suitable for problems with an abundance of data. Oversampling means creating more minority class instances to match the size of the majority class. Oversampling methods range from simple random oversampling, i.e. repeating the training procedure on instances from the minority class, chosen at random, to the more complex, which involves constructing synthetic minority-class samples. Random oversampling is similar to cost-sensitive learning as repeating the sample several times makes the cost of its mis-classification grow proportionally. Kolomiyets et al. kolomiyets2011model, Zhang et al. zhang2015character, and Wang and Yang wang2015s perform data augmentation using synonym replacement, i.e. replacing random words in sentences with their synonyms or nearest-neighbor embeddings, and show its effectiveness on multiple tasks and datasets. Wei et al. wei2019eda provide a great overview of `easy' data augmentation (EDA) techniques for NLP, including synonym replacement as described above, and random deletion, i.e. removing words in the sentence at random with pre-defined probability. They show the effectiveness of EDA across five text classification tasks. However, they mention that EDA may not lead to substantial improvements when using pre-trained models. In this work we test this claim by comparing performance gains of using cost-sensitive learning versus two data augmentation methods, synonym replacement and random deletion, with a pre-trained BERT model.
More complex augmentation methods include back-translation BIBREF11, translational data augmentation BIBREF12, and noising BIBREF13, but these are out of the scope of this study.
In this work we test this claim by comparing performance gains of using cost-sensitive learning versus two data augmentation methods, synonym replacement and random deletion, with a pre-trained BERT model.
Answer: Synonym replacement and random deletion.
Question: Should I ask my partner to marry me?
Answer: If you love your partner and can only ever see yourself being with them, then asking them to marry you might be a good idea. If you haven’t lived with them, it might be worth trying that first, and of course you should seek advice from your friends and family before taking such a big step. Good luck!
Question: How many people died as a result of the plague?
Answer: During the fourteenth century, the plague, also known as the "Black Death" caused more than 50 million deaths in Europe.
Question: Extract all the people and places from the following piece of texts: "The Hunger Games follows 16-year-old Katniss Everdeen, a girl from District 12 who volunteers for the 74th Hunger Games in place of her younger sister Primrose Everdeen. "
Answer: People: Katniss Everdeen, Primrose Everdeen
Place: District 12
Question: Given this text, tell me the inventor of the Rueben sandwich and where it was invented.
Evidence: One origin story holds that Reuben Kulakofsky (his first name sometimes spelled Reubin; his last name sometimes shortened to Kay), a Lithuanian-born Jewish grocer residing in Omaha, Nebraska, asked for a sandwich made of corned beef and sauerkraut at his weekly poker game held in the Blackstone Hotel from around 1920 through 1935. The participants, who nicknamed themselves "the committee", included the hotel's owner, Charles Schimmel. Schimmel's son, who worked in the kitchen, made the first Reuben for him, adding Swiss cheese and thousand island dressing to his order, putting the whole thing on rye bread. The sandwich first gained local fame when Schimmel put it on the Blackstone's lunch menu, and its fame spread when a former employee of the hotel won the national sandwich idea contest with the recipe. In Omaha, Nebraska, March 14 was proclaimed Reuben Sandwich Day.

Another account holds that the Reuben's creator was Arnold Reuben, the German-Jewish owner of Reuben's Delicatessen (1908–2001) in New York City. According to an interview with Craig Claiborne, Arnold Reuben created the "Reuben Special" around 1914. Bernard Sobel in his 1953 book, Broadway Heartbeat: Memoirs of a Press Agent states that the sandwich was an extemporaneous creation for Marjorie Rambeau, inaugurated when the Broadway actress visited the Reuben's Delicatessen one night when the cupboards were particularly bare.

Still other versions give credit to Alfred Scheuing, a chef at Reuben's Delicatessen, and say he created the sandwich for Reuben's son, Arnold Jr., in the 1930s.
Answer: There is no single agreed inventor of the Reuben sandwich. The text gives these possible answers:
*  Reuben Kulakofsky invented the sandwich in Omaha, Nebraska
* Arnold Reuben invented the sandwich at Reuben's Delicatessen in New York City
* Alfred Scheuing invented the sandwich at Reuben's Delicatessen in New York City
Question: Are these things heavy or light? Empty soup bowl, empty shipping container, couch, pillow, apple, pencil, mattress
Answer: Empty soup bowl: light, empty shipping container: heavy, couch: heavy, pillow: light, apple: light, pencil: light, mattress: heavy
Question: What is Impala?
Answer: Apache Impala is an open source massively parallel processing SQL query engine for data stored in a computer cluster running Apache Hadoop.
Question: Given this paragraph about magnesium, what is the atomic number of magnesium?
Evidence: Magnesium is a chemical element with the symbol Mg and atomic number 12. It is a shiny gray metal having a low density, low melting point and high chemical reactivity. Like the other alkaline earth metals (group 2 of the periodic table) it occurs naturally only in combination with other elements and it almost always has an oxidation state of +2. It reacts readily with air to form a thin passivation coating of magnesium oxide that inhibits further corrosion of the metal. The free metal burns with a brilliant-white light. The metal is obtained mainly by electrolysis of magnesium salts obtained from brine. It is less dense than aluminium and is used primarily as a component in strong and lightweight alloys that contain aluminium.
Answer: The atomic number of magnesium is 12.
Question: Extract and list the names of characters a player can control in The Last of Us mentioned in the paragraph. Also describe relationships between those characters.
Evidence: The Last of Us is an action-adventure game played from a third-person perspective. The player traverses post-apocalyptic environments such as towns, buildings, forests, and sewers to advance the story. The player can use firearms, improvised weapons, hand-to-hand combat, and stealth to defend against hostile humans and cannibalistic creatures infected by a mutated strain of the Cordyceps fungus. For most of the game, the player takes control of Joel, a man tasked with escorting a young girl, Ellie, across the United States. The player also controls Ellie throughout the game's winter segment and briefly controls Joel's daughter, Sarah, in the opening sequence.
Answer: In the Last of Us, a player can control Joel, Ellie (who is being escorted by Joen acress the United States), and Sarah (who is Joel's daughter).
Question: Who is the protagonist in Italo Calvino's Invisible Cities?
Evidence: "The book explores imagination and the imaginable through the descriptions of cities by an explorer, Marco Polo. The book is framed as a conversation between the elderly and busy emperor Kublai Khan, who constantly has merchants coming to describe the state of his expanding and vast empire, and Polo. The majority of the book consists of brief prose poems describing 55 fictitious cities that are narrated by Polo, many of which can be read as parables or meditations on culture, language, time, memory, death, or the general nature of human experience.

Short dialogues between Kublai and Polo are interspersed every five to ten cities discussing these topics. These interludes between the two characters are no less poetically constructed than the cities, and form a framing device that plays with the natural complexity of language and stories. In one key exchange in the middle of the book, Kublai prods Polo to tell him of the one city he has never mentioned directly—h"
Answer: The book explores imagination and the imaginable through the descriptions of cities by an explorer, Marco Polo.
Question: Write a horror movie synopsis of a couple who move into house and the movie must involve AI. Give it an M Night Shyamalam twist ending
Answer: Couple Kelsey (tech sales) and Mark (software, AI engineer) are struggling financially in Cali after get laid off and need to move into a much cheaper apartment. After a string of disappointing home viewing options, they get info that a really swanky home is available for very cheap. The home agent says they can have the home for rent for a strangely very cheap price, the catch is that it's a fully AI run smart home built by the landlord himself but it's got too many bugs and he's still trying to make it work. The couple moves in and the 'bugs' in the smart home system seem to have a mind of it's own + tries to hurt them. However, these 'tech issues' seem to get stranger when the couple starts seeing 'entities' and experiencing poltergeist type hauntings. They think it's the distortions from EMF waves emitted by the wrongly coded smart home system and gadgets. 
Twist comes towards the the end when the couple they realise the landlord was experimenting with AI and electromagnetic waves to conjure up portals to the ghost world in order to bring his dead wife back to life. 
The landlord has been trying to have Kelsey killed and 'program' her with the resurrected spirit of his dead wife.
Question: Where do the Kardashians live?
Answer: The Kardashians live in Hidden Hills, CA but own properties in Palm Springs, CA as well. They used to live in Calabasas, CA until they got too famous and moved one town over where they have more privacy and larger lots of land. Khloe Kardashian and Kris Jenner actually live right next to each other! They removed their property lines and have a backyard that spans both properties. Kim Kardashian lives right around the corner and Ye bought a house across the street from her when they divorced. Kourtney Kardashian lives a block away with her three children. Her husband, Travis Scott, lives 3 doors down and her children's father, Scott Disick, also lives in the same neighborhood.
Question: classify the following as living vs non-living: plants, pants, bees, beer, mug, goat, pen, night lamp, cow, pig, phone, dog, leaf, candle, mosquito
Answer: living: plants, bees, goat, cow, dog, mosquito, leaf
non-living: pants, beer, mug, pen, night lamp, phone, candle
Question: What are the different perspectives of multi-task learning?
Evidence: Different tasks may differ in characteristics of the word sequences INLINEFORM0 or the labels INLINEFORM1 . We compare lots of benchmark tasks for text classification and conclude three different perspectives of multi-task learning.
Multi-Cardinality Tasks are similar except for cardinality parameters, for example, movie review datasets with different average sequence lengths and class numbers.
Multi-Domain Tasks involve contents of different domains, for example, product review datasets on books, DVDs, electronics and kitchen appliances.
Multi-Objective Tasks are designed for different objectives, for example, sentiment analysis, topics classification and question type judgment.
The simplest multi-task learning scenario is that all tasks share the same cardinality, domain and objective, while come from different sources, so it is intuitive that they can obtain useful information from each other. However, in the most complex scenario, tasks may vary in cardinality, domain and even objective, where the interactions among different tasks can be quite complicated and implicit. We will evaluate our model on different scenarios in the Experiment section.
We compare lots of benchmark tasks for text classification and conclude three different perspectives of multi-task learning. • Multi-Cardinality Tasks are similar except for cardinality parameters, for example, movie review datasets with different average sequence lengths and class numbers. • Multi-Domain Tasks involve contents of different domains, for example, product review datasets on books, DVDs, electronics and kitchen appliances. • Multi-Objective Tasks are designed for different objectives, for example, sentiment analysis, topics classification and question type judgment.
Answer: • Multi-Cardinality Tasks are similar except for cardinality parameters. • Multi-Domain Tasks involve contents of different domains. • Multi-Objective Tasks are designed for different objectives.
Question: What is BERT?
Evidence: BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm. We extend its fine-tuning procedure to address one of its major limitations - applicability to inputs longer than a few hundred words, such as transcripts of human call conversations. Our method is conceptually simple. We segment the input into smaller chunks and feed each of them into the base model. Then, we propagate each output through a single recurrent layer, or another transformer, followed by a softmax activation. We obtain the final classification decision after the last segment has been consumed. We show that both BERT extensions are quick to fine-tune and converge after as little as 1 epoch of training on a small, domain-specific data set. We successfully apply them in three different tasks involving customer call satisfaction prediction and topic classification, and obtain a significant improvement over the baseline models in two of them.
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm.
Answer: BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm.
Question: Are external KBs capable to enhance external KBs?
Evidence: There are many possible ways to implement the idea of improving question answering with external KB. In this work, we use external KBs (such as NELL and ProBase) to enhance the representations of elements in the document KB. For instance, the argument “the sequence of amino acids” in Figure 1 from the document KB retrieves (“amino acids”, `is”, “protein”) from NELL. Enhanced with this additional clue, the original argument is a better match to the question.
Similar to BIBREF21 khot2017answering, we use ElasticSearch to retrieve facts from open KBs. We remove stop words from tokens of each argument and predicate in a document KB and regard the remained words as ElasticSearch queries. We set different search option for arguments and predicates, namely setting arguments as the dominant searchable fields for argument queries and setting predicates as the dominant searchable fields for predicate queries. We save the top 10 hints and selectively use them in the experiment.
We regard the retrieved facts from the external KB as neighbors to the arguments to be enhanced. Inspired by BIBREF22 scarselli2009graph, we update the vector of an element $v_e$ as follows, where $(e)$ and $(e)$ represent adjacent arguments from the facts retrieved by object and subject, respectively. In this work, $f(\cdot )$ is implemented by averaging the vectors of two arguments. 
$$v_e=v_e + \sum _{o^{\prime } \in (e)}^{}f(v_{p^{\prime }},v_{s^{\prime }})+\sum _{s^{\prime } \in (e)}^{}f(v_{p^{\prime }},v_{o^{\prime }})$$   (Eq. 26) 
There are many possible ways to implement the idea of improving question answering with external KB. In this work, we use external KBs (such as NELL and ProBase) to enhance the C
Answer: Yes.